From 4dc5fe888dc936caa93a355a6a79c8649eb5f5dd Mon Sep 17 00:00:00 2001 From: cyianor Date: Fri, 15 Nov 2024 10:44:13 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20scmethod?= =?UTF-8?q?s/scregclust@ed12406f5121c6716fcdb4ad21f73dcc0622205a=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/pbmc.html | 4 ++-- authors.html | 16 +++++++++------- index.html | 8 ++++---- pkgdown.yml | 2 +- reference/scregclust-package.html | 4 ++-- search.json | 2 +- 6 files changed, 19 insertions(+), 17 deletions(-) diff --git a/articles/pbmc.html b/articles/pbmc.html index 5e810d2..af51d11 100644 --- a/articles/pbmc.html +++ b/articles/pbmc.html @@ -135,7 +135,7 @@

Load the data in Seurat and prep #> Second step: Get residuals using fitted parameters for 19168 genes #> Computing corrected count matrix for 19168 genes #> Calculating gene attributes -#> Wall clock passed: Time difference of 15.23572 secs +#> Wall clock passed: Time difference of 15.3882 secs #> Determine variable features #> Centering data matrix #> Set default assay to SCT @@ -186,7 +186,7 @@

Use scregclust R2R^2 a gene has to achieve across modules. Otherwise the gene is marked as noise. The run can be reproduced with the command below. A pre-fitted -model can be downloaded from GitHub +model can be downloaded from GitHub for convenience.

 # set.seed(8374)
diff --git a/authors.html b/authors.html
index ff91acd..842b9cd 100644
--- a/authors.html
+++ b/authors.html
@@ -53,17 +53,19 @@ 
Authors

       
Citation

       
Source: inst/CITATION

 
-      
Larsson I, Held F, Popova G, Koc A, Jörnsten R, Nelander S (2023).
+      
Larsson I, Held F, Popova G, Koc A, Kundu S, Jörnsten R, Nelander S (2024).
 “Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.”
-bioRxiv.
-doi:10.1101/2023.03.10.532041.
+Nature Communications, 15(9699).
+doi:10.1038/s41467-024-53954-3.
 

       
@Article{,
   title = {Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers},
-  author = {Ida Larsson and Felix Held and Gergana Popova and Alper Koc and Rebecka Jörnsten and Sven Nelander},
-  journal = {bioRxiv},
-  year = {2023},
-  doi = {10.1101/2023.03.10.532041},
+  author = {Ida Larsson and Felix Held and Gergana Popova and Alper Koc and Soumi Kundu and Rebecka Jörnsten and Sven Nelander},
+  journal = {Nature Communications},
+  year = {2024},
+  volume = {15},
+  number = {9699},
+  doi = {10.1038/s41467-024-53954-3},
 }
diff --git a/index.html b/index.html index 5d7305f..dc3a0f3 100644 --- a/index.html +++ b/index.html @@ -5,14 +5,14 @@ -Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data • scregclust +Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data • scregclust - - - + + + Skip to contents diff --git a/pkgdown.yml b/pkgdown.yml index accffd4..6b471c5 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.1.1 pkgdown_sha: ~ articles: pbmc: pbmc.html -last_built: 2024-11-15T10:04Z +last_built: 2024-11-15T10:43Z urls: reference: https://scmethods.github.io/scregclust/reference article: https://scmethods.github.io/scregclust/articles diff --git a/reference/scregclust-package.html b/reference/scregclust-package.html index 86eba27..6b1280d 100644 --- a/reference/scregclust-package.html +++ b/reference/scregclust-package.html @@ -1,5 +1,5 @@ -scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data — scregclust-package • scregclustscregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data — scregclust-package • scregclust Skip to contents @@ -31,7 +31,7 @@
-

scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data

+

scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data

Source: R/scregclust-package.R
scregclust-package.Rd
diff --git a/search.json b/search.json index ed20816..4fba690 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://scmethods.github.io/scregclust/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://scmethods.github.io/scregclust/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"download-the-data","dir":"Articles","previous_headings":"","what":"Download the data","title":"Demonstration of workflow","text":"focusing filtered feature barcode matrix available HDF5 file website linked . data can downloaded manually using R. However obtain data, code assumes HDF5 file containing placed folder script name pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5.","code":"url <- paste0( \"https://cf.10xgenomics.com/samples/cell-arc/2.0.0/\", \"pbmc_granulocyte_sorted_3k/\", \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\" ) path <- \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\" download.file(url, path, cacheOK = FALSE, mode = \"wb\")"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"load-the-data-in-seurat-and-preprocess","dir":"Articles","previous_headings":"","what":"Load the data in Seurat and preprocess","title":"Demonstration of workflow","text":"perform preprocessing use Seurat load data. file ships two modalities, “Gene Expression” “Peaks”. use former. create Seurat object follow Seurat vignette subset cells features (genes). SCTransform used variance stabilization data Pearson residuals 6000 variable genes extracted matrix z.","code":"pbmc_data <- Read10X_h5( \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\", use.names = TRUE, unique.features = TRUE )[[\"Gene Expression\"]] #> Genome matrix has multiple modalities, returning a list of matrices for this genome pbmc <- CreateSeuratObject( counts = pbmc_data, min.cells = 3, min.features = 200 ) pbmc[[\"percent.mt\"]] <- PercentageFeatureSet(pbmc, pattern = \"^MT.\") pbmc <- subset(pbmc, subset = percent.mt < 30 & nFeature_RNA < 6000) pbmc <- SCTransform(pbmc, variable.features.n = 6000) #> Running SCTransform on assay: RNA #> Running SCTransform on layer: counts #> vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. #> Variance stabilizing transformation of count matrix of size 19168 by 2686 #> Model formula is y ~ log_umi #> Get Negative Binomial regression parameters per gene #> Using 2000 genes, 2686 cells #> Found 6 outliers - those will be ignored in fitting/regularization step #> Second step: Get residuals using fitted parameters for 19168 genes #> Computing corrected count matrix for 19168 genes #> Calculating gene attributes #> Wall clock passed: Time difference of 15.23572 secs #> Determine variable features #> Centering data matrix #> Set default assay to SCT z <- GetAssayData(pbmc, layer = \"scale.data\") dim(z) #> [1] 6000 2686"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"use-scregclust-for-clustering-target-genes-into-modules","dir":"Articles","previous_headings":"","what":"Use scregclust for clustering target genes into modules","title":"Demonstration of workflow","text":"use scregclust_format extracts gene symbols expression matrix determines genes considered regulators. default, transcription factors used regulators. Setting mode \"kinase\" uses kinases instead transcription factors. list regulators used internally returned get_regulator_list(). output scregclust_format list three elements. genesymbols contains rownames z sample_assignment initialized vector 1s length ncol(z) can filled known sample grouping. , use just keep uniform across cells. is_regulator indicator vector (elements 0 1) corresponding entries genesymbols 1 marking genesymbol selected regulator according model scregclust_format (\"TF\" \"kinase\") 0 otherwise. Run scregclust number initial modules set 10 test several penalties. penalties provided penalization used selection regulators associated module. increasing penalty implies selection fewer regulators. noise_threshold controls minimum R2R^2 gene achieve across modules. Otherwise gene marked noise. run can reproduced command . pre-fitted model can downloaded GitHub convenience.","code":"out <- scregclust_format(z, mode = \"TF\") genesymbols <- out$genesymbols sample_assignment <- out$sample_assignment is_regulator <- out$is_regulator # set.seed(8374) # fit <- scregclust( # z, genesymbols, is_regulator, penalization = seq(0.1, 0.5, 0.05), # n_modules = 10L, n_cycles = 50L, noise_threshold = 0.05 # ) # saveRDS(fit, file = \"pbmc_scregclust.rds\") url <- paste0( \"https://github.com/scmethods/scregclust/raw/main/datasets/\", \"pbmc_scregclust.rds\" ) path <- \"pbmc_scregclust.rds\" download.file(url, path) fit <- readRDS(\"pbmc_scregclust.rds\")"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"analysis-of-results","dir":"Articles","previous_headings":"","what":"Analysis of results","title":"Demonstration of workflow","text":"Results can visualized easily using built-functions. Metrics helping choosing optimal penalty can plotted calling plot object returned scregclust. results penalization parameter placed list, results, attached fit object. fit$results[[1]] contains results running scregclust penalization = 0.1. penalization parameter, algorithm might end finding multiple optimal configurations. configuration describes target genes module assignments regulators associated modules. results configuration contained list output. means fit$results[[1]]$output[[1]] contains results first final configuration. one may available. example, two final configurations found penalization parameters. plot regulator network first configuration penalization = 0.1 function plot_regulator_network can used.","code":"plot(fit) sapply(fit$results, function(r) length(r$output)) #> [1] 2 1 1 1 2 2 2 1 1 plot_regulator_network(fit$results[[1]]$output[[1]])"},{"path":"https://scmethods.github.io/scregclust/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Felix Held. Author, maintainer. Ida Larsson. Author. Sven Nelander. Author.","code":""},{"path":"https://scmethods.github.io/scregclust/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Larsson , Held F, Popova G, Koc , Jörnsten R, Nelander S (2023). “Reconstructing regulatory programs underlying phenotypic plasticity neural cancers.” bioRxiv. doi:10.1101/2023.03.10.532041.","code":"@Article{, title = {Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers}, author = {Ida Larsson and Felix Held and Gergana Popova and Alper Koc and Rebecka Jörnsten and Sven Nelander}, journal = {bioRxiv}, year = {2023}, doi = {10.1101/2023.03.10.532041}, }"},{"path":[]},{"path":"https://scmethods.github.io/scregclust/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data","text":"goal scregclust cluster genes regulatory programs. , genes clustered modules turn associated regulators. algorithm alternates associating regulators modules reallocating target genes modules. detailed description algorithm -depth evaluation properties can found original research article Larsson, Held, et al. (2024) Reconstructing regulatory programs underlying phenotypic plasticity neural cancers. Nature Communications 15, 9699 DOI 10.1038/s41467-024-53954-3","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"Allocate 3d-array fill matrix along first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"","code":"alloc_array(input, n_cl)"},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"input matrix size n_obs x n_genes n_cl size three-dimensional array's first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"allocated filled array size n_cl x n_obs x n_genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract final configurations into a data frame — available_results","title":"Extract final configurations into a data frame — available_results","text":"Extract final configurations data frame","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract final configurations into a data frame — available_results","text":"","code":"available_results(obj)"},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract final configurations into a data frame — available_results","text":"obj object class scregclust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract final configurations into a data frame — available_results","text":"data.frame containing penalization parameters final configurations penalizations.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a table of module overlap for two clusterings — cluster_overlap","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"Compares two clusterings creates table overlap . Module labels match.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"","code":"cluster_overlap(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"k1 First clustering k2 Second clustering","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"matrix showing module overlap labels k1 columns labels k2 rows.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute NNLS coefficients — coef_nnls","title":"Compute NNLS coefficients — coef_nnls","text":"Computes non-negative least squares coefficients matrix right hand side.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute NNLS coefficients — coef_nnls","text":"","code":"coef_nnls(x, y, eps = 1e-12, max_iter = 1000L)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute NNLS coefficients — coef_nnls","text":"x Coefficient matrix (p x n matrix) y Right hand side (p x m matrix) eps Convergence tolerance max_iter Maximum number iterations","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute NNLS coefficients — coef_nnls","text":"list containing beta estimated coefficient matrix iterations vector containing number iterations needed -th column y -th entry.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute NNLS coefficients — coef_nnls","text":"Duy Khuong Nguyen Tu Bao Ho. Accelerated anti-lopsided algorithm nonnegative least squares. International Journal Data Science Analytics, 3(1):23–34, 2017. Adapted https://github.com/khuongnd/nnls_antilopsided","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute OLS coefficients — coef_ols","title":"Compute OLS coefficients — coef_ols","text":"design matrix full column-rank, use normal least squares estimate. Otherwise, use Moore-Penrose inverse compute least squares estimate.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute OLS coefficients — coef_ols","text":"","code":"coef_ols(y, x)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute OLS coefficients — coef_ols","text":"y Target vector (n x 1)/matrix (n x m) x Design matrix (n x p)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute OLS coefficients — coef_ols","text":"Vector OLS coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute ridge regression coefficients — coef_ridge","title":"Compute ridge regression coefficients — coef_ridge","text":"Compute ridge regression coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute ridge regression coefficients — coef_ridge","text":"","code":"coef_ridge(y, x, lambda)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute ridge regression coefficients — coef_ridge","text":"y Target vector (n x 1)/matrix (n x m) x Design matrix (n x p) lambda Positive parameter ridge penalty","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute ridge regression coefficients — coef_ridge","text":"Vector ridge regression coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Compute Hubert's Arabie's Adjusted Rand index","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"","code":"compute_adjusted_rand_index(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"k1 First clustering vector integers k2 Second clustering vector integers","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Adjusted Rand index numeric value","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Lawrence Hubert Phipps Arabie (1985). \"Comparing partitions\". Journal Classification. 2 (1): 193–218. DOI:10.1007/BF01908075","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the Rand index — compute_rand_index","title":"Compute the Rand index — compute_rand_index","text":"Compute Rand index","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the Rand index — compute_rand_index","text":"","code":"compute_rand_index(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the Rand index — compute_rand_index","text":"k1 First clustering vector integers k2 Second clustering vector integers","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the Rand index — compute_rand_index","text":"Rand index numeric value","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the Rand index — compute_rand_index","text":"W. M. Rand (1971). \"Objective criteria evaluation clustering methods\". Journal American Statistical Association 66 (336): 846–850. DOI:10.2307/2284239","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":null,"dir":"Reference","previous_headings":"","what":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"Implements estimation coop-lasso problem.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"","code":"coop_lasso( y, x, lambda, weights, beta_0 = NULL, rho_0 = 0.2, alpha_0 = 1.5, n_update = 2L, eps_corr = 0.2, max_iter = 1000L, eps_rel = 1e-08, eps_abs = 1e-12, verbose = FALSE )"},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"y Target (n x m) x Design matrix (n x p) lambda Penalization parameter weights specific weight group (typically sqrt(group size)). beta_0 Initial value coefficients, allowing warm start. Can set NULL, results initial beta zero matrix. rho_0 Initial ADMM step-size alpha_0 Initial ADMM relaxation parameter n_update Number steps -updates step-size/adaptation parameters eps_corr Lower bound correlation step-size update steps max_iter Maximum number iterations eps_rel Relative tolerance convergence check eps_abs Absolute tolerance convergence check verbose Whether information optimization process printed terminal","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"list containing beta coefficients convergence iterations Number iterations","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"Xu et al. (2017) Adaptive relaxed ADMM: Convergence theory practical implementation. DOI 10.1109/CVPR.2017.765","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count table nicely — count_table","title":"Format count table nicely — count_table","text":"Format count table nicely","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count table nicely — count_table","text":"","code":"count_table(counts, title, row_names, col_width = 5)"},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count table nicely — count_table","text":"counts list count vectors 1 + n_cl entries . NA values replaced - title title table row_names vector row names, one count vector col_width minimum width columns","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count table nicely — count_table","text":"string formatted table","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Fast computation of correlation — fast_cor","title":"Fast computation of correlation — fast_cor","text":"uses memory-intensive much faster algorithm built-cor function.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fast computation of correlation — fast_cor","text":"","code":"fast_cor(x, y)"},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fast computation of correlation — fast_cor","text":"x first input matrix y second input matrix","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fast computation of correlation — fast_cor","text":"Correlations matrix columns x y","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fast computation of correlation — fast_cor","text":"Computes correlation columns x y.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine module sizes — find_module_sizes","title":"Determine module sizes — find_module_sizes","text":"Determine module sizes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine module sizes — find_module_sizes","text":"","code":"find_module_sizes(module, n_modules)"},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine module sizes — find_module_sizes","text":"module Vector module indices n_modules Total number modules","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine module sizes — find_module_sizes","text":"named vector containining name module (index \"Noise\") number elements module","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the average number of active regulators per module — get_avg_num_regulators","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"Get average number active regulators per module","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"","code":"get_avg_num_regulators(fit)"},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"fit object class scRegClust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"data.frame containing average number active regulators per module penalization parameter.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of final configurations — get_num_final_configs","title":"Return the number of final configurations — get_num_final_configs","text":"Returns number final configurations per penalization parameter scRegClust object.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of final configurations — get_num_final_configs","text":"","code":"get_num_final_configs(fit)"},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of final configurations — get_num_final_configs","text":"fit object class scRegClust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of final configurations — get_num_final_configs","text":"integer vector containing number final configurations penalization parameter.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Rand indices — get_rand_indices","title":"Compute Rand indices — get_rand_indices","text":"Compute Rand indices fitted scregclust object","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Rand indices — get_rand_indices","text":"","code":"get_rand_indices(fit, groundtruth, adjusted = TRUE)"},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Rand indices — get_rand_indices","text":"fit object class scregclust groundtruth known clustering target genes (integer vector) adjusted TRUE, Adjusted Rand index computed. Otherwise ordinary Rand index computed.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Rand indices — get_rand_indices","text":"data.frame containing Rand indices. Since can one final configuration penalization parameters, Rand indices averaged fixed penalization parameter. Returned mean, standard deviation number final configurations averaged.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Rand indices — get_rand_indices","text":"W. M. Rand (1971). \"Objective criteria evaluation clustering methods\". Journal American Statistical Association 66 (336): 846–850. DOI:10.2307/2284239 Lawrence Hubert Phipps Arabie (1985). \"Comparing partitions\". Journal Classification. 2 (1): 193–218. DOI:10.1007/BF01908075","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Return list of regulator genes — get_regulator_list","title":"Return list of regulator genes — get_regulator_list","text":"Return list regulator genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return list of regulator genes — get_regulator_list","text":"","code":"get_regulator_list(mode = c(\"TF\", \"kinase\"))"},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return list of regulator genes — get_regulator_list","text":"mode Determines genes considered regulators. Currently supports TF=transcription factors kinases.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return list of regulator genes — get_regulator_list","text":"list gene symbols","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract target gene modules for given penalization parameters — get_target_gene_modules","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"Extract target gene modules given penalization parameters","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"","code":"get_target_gene_modules(fit, penalization = NULL)"},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"fit object class scregclust penalization numeric vector penalization parameters. penalization parameters specificed must used used fitting fit object.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"list lists final target modules. One list parameter penalization. lists contain moduleing target genes final configuration.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"Computes Jaccard distance rows matrix returns sparse symmetric indicator matrix containing entries distance less given upper bound. Note diagonal always 1.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"","code":"jaccard_indicator(x, upper_bnd = 0.8)"},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"x input matrix vectors compared rows. upper_bnd pairs Jaccard distance upper bound returned 1 others receive entry 0.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"list vectors describing sparse lower triangular pattern matrix Row indices j Column indices","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"Perform computations thresholded Jaccard distance","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"","code":"jaccard_indicator_comp(gs, eps)"},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"gs list integer vectors, one row, giving column indices non-zero elements row NULL whole row empty. eps upper bound Jaccard distance (1 - eps becomes lower bound Jaccard similarity)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"list row column indicies #row x #row indicator matrix specifying rows original matrix distance eps.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"function optimized sparse matrices computes pairwise Jaccard distances rows input matrix. Note actual distance saved. Instead, threshold (eps) supplied indicator matrix returned, one indicating distance smaller eps (equivalently, Jaccard similarity larger 1 - eps).","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform the k-means++ algorithm — kmeanspp","title":"Perform the k-means++ algorithm — kmeanspp","text":"Performs k-means++ algorithm cluster rows input matrix.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform the k-means++ algorithm — kmeanspp","text":"","code":"kmeanspp(x, n_cluster, n_init_clusterings = 10L, n_max_iter = 10L)"},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform the k-means++ algorithm — kmeanspp","text":"x Input matrix (n x p) n_cluster Number clusters n_init_clusterings Number repeated random initialisations perform n_max_iter Number maximum iterations perform k-means algorithm","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform the k-means++ algorithm — kmeanspp","text":"object class stats::kmeans.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform the k-means++ algorithm — kmeanspp","text":"Estimation repeated","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform the k-means++ algorithm — kmeanspp","text":"David Arthur Sergei Vassilvitskii. K-Means++: advantages careful seeding. Proceedings Eighteenth Annual ACM-SIAM Symposium Discrete Algorithms, SODA '07, pages 1027––1035. Society Industrial Applied Mathematics, 2007.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"Determine initial centers kmeans++ algorithm","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"","code":"kmeanspp_init(n_cluster, x = NULL, dm = NULL)"},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"x data matrix clustered dm distance matrix (rows x; class \"dist\")","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"Row indices initial cluster centres x","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"Plot average silhouette scores average predictive \\(R^2\\)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"","code":"plot_module_count_helper(list_of_fits, penalization)"},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"list_of_fits list scregclust objects fit dataset across variety module counts (varying n_modules running scregclust). penalization Either single numeric value requesting results penalty parameter across fits list_of_fits, one individual fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"ggplot2 plot showing average silhouette score average predictive \\(R^2\\)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"Plotting regulatory table scregclust directed graph","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"","code":"plot_regulator_network( output, arrow_size = 0.3, edge_scaling = 30, no_links = 6, col = c(\"gray80\", \"#FC7165\", \"#BD828C\", \"#9D8A9F\", \"#7D92B2\", \"#BDA88C\", \"#FCBD65\", \"#F2BB90\", \"#E7B9BA\", \"#BDB69C\", \"#92B27D\", \"#9B8BA5\", \"#9D7DB2\", \"#94A5BF\") )"},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"output Object type scregclust_output fit scregclust algorithm. arrow_size Size arrow head edge_scaling Scaling factor edge width no_links Threshold value (0-10) number edges show, higher value = stringent threshold = less edges col color","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"Graph gene modules regulators nodes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot individual silhouette scores — plot_silhouettes","title":"Plot individual silhouette scores — plot_silhouettes","text":"Plot individual silhouette scores","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot individual silhouette scores — plot_silhouettes","text":"","code":"plot_silhouettes(list_of_fits, penalization, final_config = 1L)"},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot individual silhouette scores — plot_silhouettes","text":"list_of_fits list scregclust objects fit dataset across variety module counts (varying n_modules running scregclust). penalization Either single numeric value requesting results penalty parameter across fits list_of_fits, one individual fit. final_config final configuration visualised. Either single number used fits list_of_fits, one individual fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot individual silhouette scores — plot_silhouettes","text":"ggplot2 plot showing silhouette scores supplied fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":null,"dir":"Reference","previous_headings":"","what":"Quick'n'dirty progress bar — progstr","title":"Quick'n'dirty progress bar — progstr","text":"Creates progress bar returns string.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quick'n'dirty progress bar — progstr","text":"","code":"progstr(step, n_steps, name, finished = FALSE, progress_length = 20L)"},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quick'n'dirty progress bar — progstr","text":"step current step worked n_steps total number steps name name process finished whether process finished progress_length length progress bar ascii signs","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quick'n'dirty progress bar — progstr","text":"string formatted progress bar","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove empty modules — remove_empty_modules","title":"Remove empty modules — remove_empty_modules","text":"Remove empty modules","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove empty modules — remove_empty_modules","text":"","code":"remove_empty_modules(module)"},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove empty modules — remove_empty_modules","text":"module Vector module indices","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove empty modules — remove_empty_modules","text":"updated vector module indices empty modules removed.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove empty modules — remove_empty_modules","text":"iterates modules positive index, leaving noise module untouched.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset input 3d-array by filling matrix along first dimension — reset_array","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"Reset input 3d-array filling matrix along first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"","code":"reset_array(arr, input)"},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"arr 3d-array dimension n_cl x n_obs x n_genes input matrix size n_obs x n_genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":null,"dir":"Reference","previous_headings":"","what":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data — scregclust-package","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data — scregclust-package","text":"Implementation scregclust algorithm (Larsson, Held, et al., 2024, doi:10.1038/s41467-024-53954-3 ) aims reconstruct regulatory programs target genes single cell data. Target genes clustered modules module associated linear model accounting regulatory program driving genes contains.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data — scregclust-package","text":"Computational methods scregclust algorithm","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq data — scregclust-package","text":"Ida Larsson, Felix Held, Sven Nelander","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":null,"dir":"Reference","previous_headings":"","what":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"Use scRegClust algorithm determine gene modules regulatory programs single-cell data.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"","code":"scregclust( expression, genesymbols, is_regulator, penalization, n_modules, initial_target_modules = NULL, sample_assignment = NULL, center = TRUE, split1_proportion = 0.5, total_proportion = 1, split_indices = NULL, prior_indicator = NULL, prior_genesymbols = NULL, prior_baseline = 1e-06, prior_weight = 0.5, min_module_size = 0L, allocate_per_obs = TRUE, noise_threshold = 0.025, n_cycles = 50L, use_kmeanspp_init = TRUE, n_initializations = 50L, max_optim_iter = 10000L, tol_coop_rel = 1e-08, tol_coop_abs = 1e-12, tol_nnls = 1e-04, compute_predictive_r2 = TRUE, compute_silhouette = FALSE, nowarnings = FALSE, verbose = TRUE )"},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"expression p x n matrix pre-processed single cell expression data p rows genes n columns cells. genesymbols vector gene names corresponding rows expression. length p. is_regulator indicator vector 1 indicates corresponding row expression candidate regulator. rows represent target genes. length p. penalization Sparsity penalty related amount regulators associated module. Either single positive number vector positive numbers. n_modules Requested number modules (integer). provided without specifying initial_target_modules, initial module allocation performed cross-correlation matrix targets genes first dataset data splitting. initial_target_modules initial assignment target genes modules length sum(is_regulator == 0L). specified, see n_modules regarding module initialization. provided, use_kmeanspp_init n_initializations ignored. sample_assignment vector sample assignment cell, can used perform data splitting stratification. length n. stratification NULL supplied. center Whether genes centered within subgroup defined sample_assignment. split1_proportion proportion use first dataset data splitting. proportion second dataset 1 - split1_proportion. stratification sample_assignment used, proportion strata controlled. total_proportion Can used use proportion supplied observations. proportion first dataset data splitting relation full dataset total_proportion * split1_proportion. split_indices Can used provide explicit data split. supplied split1_proportion, total_proportion ignored. Note sample_assigment provided center == TRUE, subgroup centering performed case random splitting. vector length n containing entries 1 cells first data split, 2 cells second data split NA cells excluded computations. prior_indicator indicator matrix (sparse dense) size q x q indicates whether known functional relationship two genes. Ideally, supplied sparse matrix (sparseMatrix Matrix package). , matrix converted one. prior_genesymbols vector gene names length q corresponding rows/columns prior_indicator. genesymbols, useful overlap. prior_baseline positive baseline network prior. larger parameter , less impact network prior . prior_weight number 0 1 indicating strength prior relation data. 0 ignores prior makes algorithm completely data-driven. 1 uses prior module allocation. min_module_size Minimum required size target genes module. Smaller modules emptied. allocate_per_obs Whether module allocation performed observation second data split separately. FALSE, target genes allocated modules aggregate sum squares across observations second data split. noise_threshold Threshold best \\(R^2\\) target gene gets identified noise. n_cycles Number maximum algorithmic cycles. use_kmeanspp_init Use kmeans++ module initialization initial_target_modules single integer; otherwise use kmeans random initial cluster centers n_initializations Number kmeans(++) initialization runs. max_optim_iter Maximum number iterations optimization coop-Lasso NNLS steps. tol_coop_rel Relative convergence tolerance optimization coop-Lasso step. tol_coop_abs Absolute convergence tolerance optimization coop-Lasso step. tol_nnls Convergence tolerance optimization NNLS step. compute_predictive_r2 Whether compute predictive \\(R^2\\) per module well regulator importance. compute_silhouette Whether compute silhouette scores target gene. nowarnings turned warning messages shown. verbose Whether print progress.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"list S3 class scregclust containing penalization supplied penalization parameters results list result lists (S3 class scregclust_result), one supplied penalization parameter. See . initial_target_modules Initial allocation target genes modules. split_indices either verbatim vector given input vector encoding splits NA = included, 1 = split 1 2 = split 2. Allows reproduciblity data splits. supplied penalization parameter, results contains list current penalization parameter, supplied genesymbols filtering (used fitting), supplied is_regulator vector filtering (used fitting), number fitted modules n_modules, whether current run converged single configuration (boolean), well output object containing numeric results final configuration. possible algorithm ends finite cycle configurations instead unique final configuration. Therefore, output list element list following contents: reg_table regulator table, matrix weights regulator module module vector length genesymbols containing module assignments genes regulators marked NA. Genes considered noise marked -1. module_all module, however, genes marked noise (-1 module) assigned module largest \\(R^2\\), even noise_threshold. r2 matrix predictive \\(R^2\\) value target gene module best_r2 vector best predictive \\(R^2\\) gene (regulators marked NA) best_r2_idx module index corresponding best predictive \\(R^2\\) gene (regulators marked NA) r2_module vector predictive \\(R^2\\) values module (included compute_predictive_r2 == TRUE) importance matrix importance values regulator (rows) module (columns) (included compute_predictive_r2 == TRUE) r2_cross_module_per_target matrix cross module \\(R^2\\) values target gene (rows) module (columns) (included compute_silhouette == TRUE) silhouette vector silhouette scores target gene (included compute_silhouette == TRUE) models regulator selection module matrix regulators rows modules columns signs regulator signs module matrix regulators rows modules columns weights average regulator coefficient module coeffs list regulator coefficient matrices module target genes re-estimated NNLS step sigmas matrix residual variances, one per target gene module; derived residuals NNLS step","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Package data before clustering — scregclust_format","title":"Package data before clustering — scregclust_format","text":"Package data clustering","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package data before clustering — scregclust_format","text":"","code":"scregclust_format(expression_matrix, mode = c(\"TF\", \"kinase\"))"},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Package data before clustering — scregclust_format","text":"expression_matrix p x n gene expression matrix gene symbols rownames. mode Determines genes considered regulators.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Package data before clustering — scregclust_format","text":"list genesymbols gene symbols extracted expression matrix sample_assignment vector filled 1's length columns gene expression matrix. is_regulator Whether gene considered regulator , determined dependent mode.","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Sample — split_sample","title":"Split Sample — split_sample","text":"Splits sample train test set","code":""},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Sample — split_sample","text":"","code":"split_sample( z, stratification, is_regulator, split_indices, split1_proportion, total_proportion, center )"},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Sample — split_sample","text":"z matrix single cell data rows genes columns cells. stratification vector sampling stratified length ncol(z) is_regulator indicator vector, telling rows z candidate regulators split_indices vector given split indices. can NULL split1_proportion proportion include first data split total_proportion proportion data include overall splitting center TRUE data row-centered. Set FALSE otherwise.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Sample — split_sample","text":"list containing z1_reg first data split, TF-part z2_reg second data split, TF-part z1_target first data split, non-TF part z2_target second data split, non-TF part split_indices either verbatim vector given input vector encoding splits NA = included, 1 = split 1 2 = split 2. Allows reproduciblity data splits.","code":""}] +[{"path":"https://scmethods.github.io/scregclust/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://scmethods.github.io/scregclust/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://scmethods.github.io/scregclust/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. 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This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"download-the-data","dir":"Articles","previous_headings":"","what":"Download the data","title":"Demonstration of workflow","text":"focusing filtered feature barcode matrix available HDF5 file website linked . data can downloaded manually using R. However obtain data, code assumes HDF5 file containing placed folder script name pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5.","code":"url <- paste0( \"https://cf.10xgenomics.com/samples/cell-arc/2.0.0/\", \"pbmc_granulocyte_sorted_3k/\", \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\" ) path <- \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\" download.file(url, path, cacheOK = FALSE, mode = \"wb\")"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"load-the-data-in-seurat-and-preprocess","dir":"Articles","previous_headings":"","what":"Load the data in Seurat and preprocess","title":"Demonstration of workflow","text":"perform preprocessing use Seurat load data. file ships two modalities, “Gene Expression” “Peaks”. use former. create Seurat object follow Seurat vignette subset cells features (genes). SCTransform used variance stabilization data Pearson residuals 6000 variable genes extracted matrix z.","code":"pbmc_data <- Read10X_h5( \"pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix.h5\", use.names = TRUE, unique.features = TRUE )[[\"Gene Expression\"]] #> Genome matrix has multiple modalities, returning a list of matrices for this genome pbmc <- CreateSeuratObject( counts = pbmc_data, min.cells = 3, min.features = 200 ) pbmc[[\"percent.mt\"]] <- PercentageFeatureSet(pbmc, pattern = \"^MT.\") pbmc <- subset(pbmc, subset = percent.mt < 30 & nFeature_RNA < 6000) pbmc <- SCTransform(pbmc, variable.features.n = 6000) #> Running SCTransform on assay: RNA #> Running SCTransform on layer: counts #> vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. #> Variance stabilizing transformation of count matrix of size 19168 by 2686 #> Model formula is y ~ log_umi #> Get Negative Binomial regression parameters per gene #> Using 2000 genes, 2686 cells #> Found 6 outliers - those will be ignored in fitting/regularization step #> Second step: Get residuals using fitted parameters for 19168 genes #> Computing corrected count matrix for 19168 genes #> Calculating gene attributes #> Wall clock passed: Time difference of 15.3882 secs #> Determine variable features #> Centering data matrix #> Set default assay to SCT z <- GetAssayData(pbmc, layer = \"scale.data\") dim(z) #> [1] 6000 2686"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"use-scregclust-for-clustering-target-genes-into-modules","dir":"Articles","previous_headings":"","what":"Use scregclust for clustering target genes into modules","title":"Demonstration of workflow","text":"use scregclust_format extracts gene symbols expression matrix determines genes considered regulators. default, transcription factors used regulators. Setting mode \"kinase\" uses kinases instead transcription factors. list regulators used internally returned get_regulator_list(). output scregclust_format list three elements. genesymbols contains rownames z sample_assignment initialized vector 1s length ncol(z) can filled known sample grouping. , use just keep uniform across cells. is_regulator indicator vector (elements 0 1) corresponding entries genesymbols 1 marking genesymbol selected regulator according model scregclust_format (\"TF\" \"kinase\") 0 otherwise. Run scregclust number initial modules set 10 test several penalties. penalties provided penalization used selection regulators associated module. increasing penalty implies selection fewer regulators. noise_threshold controls minimum R2R^2 gene achieve across modules. Otherwise gene marked noise. run can reproduced command . pre-fitted model can downloaded GitHub convenience.","code":"out <- scregclust_format(z, mode = \"TF\") genesymbols <- out$genesymbols sample_assignment <- out$sample_assignment is_regulator <- out$is_regulator # set.seed(8374) # fit <- scregclust( # z, genesymbols, is_regulator, penalization = seq(0.1, 0.5, 0.05), # n_modules = 10L, n_cycles = 50L, noise_threshold = 0.05 # ) # saveRDS(fit, file = \"pbmc_scregclust.rds\") url <- paste0( \"https://github.com/scmethods/scregclust/raw/main/datasets/\", \"pbmc_scregclust.rds\" ) path <- \"pbmc_scregclust.rds\" download.file(url, path) fit <- readRDS(\"pbmc_scregclust.rds\")"},{"path":"https://scmethods.github.io/scregclust/articles/pbmc.html","id":"analysis-of-results","dir":"Articles","previous_headings":"","what":"Analysis of results","title":"Demonstration of workflow","text":"Results can visualized easily using built-functions. Metrics helping choosing optimal penalty can plotted calling plot object returned scregclust. results penalization parameter placed list, results, attached fit object. fit$results[[1]] contains results running scregclust penalization = 0.1. penalization parameter, algorithm might end finding multiple optimal configurations. configuration describes target genes module assignments regulators associated modules. results configuration contained list output. means fit$results[[1]]$output[[1]] contains results first final configuration. one may available. example, two final configurations found penalization parameters. plot regulator network first configuration penalization = 0.1 function plot_regulator_network can used.","code":"plot(fit) sapply(fit$results, function(r) length(r$output)) #> [1] 2 1 1 1 2 2 2 1 1 plot_regulator_network(fit$results[[1]]$output[[1]])"},{"path":"https://scmethods.github.io/scregclust/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Felix Held. Author, maintainer. Ida Larsson. Author. Sven Nelander. Author.","code":""},{"path":"https://scmethods.github.io/scregclust/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Larsson , Held F, Popova G, Koc , Kundu S, Jörnsten R, Nelander S (2024). “Reconstructing regulatory programs underlying phenotypic plasticity neural cancers.” Nature Communications, 15(9699). doi:10.1038/s41467-024-53954-3.","code":"@Article{, title = {Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers}, author = {Ida Larsson and Felix Held and Gergana Popova and Alper Koc and Soumi Kundu and Rebecka Jörnsten and Sven Nelander}, journal = {Nature Communications}, year = {2024}, volume = {15}, number = {9699}, doi = {10.1038/s41467-024-53954-3}, }"},{"path":[]},{"path":"https://scmethods.github.io/scregclust/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data","text":"goal scregclust cluster genes regulatory programs. , genes clustered modules turn associated regulators. algorithm alternates associating regulators modules reallocating target genes modules. detailed description algorithm -depth evaluation properties can found original research article Larsson, Held, et al. (2024) Reconstructing regulatory programs underlying phenotypic plasticity neural cancers. Nature Communications 15, 9699 DOI 10.1038/s41467-024-53954-3","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"Allocate 3d-array fill matrix along first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"","code":"alloc_array(input, n_cl)"},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"input matrix size n_obs x n_genes n_cl size three-dimensional array's first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/alloc_array.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Allocate 3d-array and fill with matrix along first dimension — alloc_array","text":"allocated filled array size n_cl x n_obs x n_genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract final configurations into a data frame — available_results","title":"Extract final configurations into a data frame — available_results","text":"Extract final configurations data frame","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract final configurations into a data frame — available_results","text":"","code":"available_results(obj)"},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract final configurations into a data frame — available_results","text":"obj object class scregclust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/available_results.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract final configurations into a data frame — available_results","text":"data.frame containing penalization parameters final configurations penalizations.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a table of module overlap for two clusterings — cluster_overlap","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"Compares two clusterings creates table overlap . Module labels match.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"","code":"cluster_overlap(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"k1 First clustering k2 Second clustering","code":""},{"path":"https://scmethods.github.io/scregclust/reference/cluster_overlap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a table of module overlap for two clusterings — cluster_overlap","text":"matrix showing module overlap labels k1 columns labels k2 rows.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute NNLS coefficients — coef_nnls","title":"Compute NNLS coefficients — coef_nnls","text":"Computes non-negative least squares coefficients matrix right hand side.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute NNLS coefficients — coef_nnls","text":"","code":"coef_nnls(x, y, eps = 1e-12, max_iter = 1000L)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute NNLS coefficients — coef_nnls","text":"x Coefficient matrix (p x n matrix) y Right hand side (p x m matrix) eps Convergence tolerance max_iter Maximum number iterations","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute NNLS coefficients — coef_nnls","text":"list containing beta estimated coefficient matrix iterations vector containing number iterations needed -th column y -th entry.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_nnls.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute NNLS coefficients — coef_nnls","text":"Duy Khuong Nguyen Tu Bao Ho. Accelerated anti-lopsided algorithm nonnegative least squares. International Journal Data Science Analytics, 3(1):23–34, 2017. Adapted https://github.com/khuongnd/nnls_antilopsided","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute OLS coefficients — coef_ols","title":"Compute OLS coefficients — coef_ols","text":"design matrix full column-rank, use normal least squares estimate. Otherwise, use Moore-Penrose inverse compute least squares estimate.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute OLS coefficients — coef_ols","text":"","code":"coef_ols(y, x)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute OLS coefficients — coef_ols","text":"y Target vector (n x 1)/matrix (n x m) x Design matrix (n x p)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute OLS coefficients — coef_ols","text":"Vector OLS coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute ridge regression coefficients — coef_ridge","title":"Compute ridge regression coefficients — coef_ridge","text":"Compute ridge regression coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute ridge regression coefficients — coef_ridge","text":"","code":"coef_ridge(y, x, lambda)"},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute ridge regression coefficients — coef_ridge","text":"y Target vector (n x 1)/matrix (n x m) x Design matrix (n x p) lambda Positive parameter ridge penalty","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coef_ridge.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute ridge regression coefficients — coef_ridge","text":"Vector ridge regression coefficients","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Compute Hubert's Arabie's Adjusted Rand index","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"","code":"compute_adjusted_rand_index(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"k1 First clustering vector integers k2 Second clustering vector integers","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Adjusted Rand index numeric value","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_adjusted_rand_index.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Hubert's and Arabie's Adjusted Rand index — compute_adjusted_rand_index","text":"Lawrence Hubert Phipps Arabie (1985). \"Comparing partitions\". Journal Classification. 2 (1): 193–218. DOI:10.1007/BF01908075","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the Rand index — compute_rand_index","title":"Compute the Rand index — compute_rand_index","text":"Compute Rand index","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the Rand index — compute_rand_index","text":"","code":"compute_rand_index(k1, k2)"},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the Rand index — compute_rand_index","text":"k1 First clustering vector integers k2 Second clustering vector integers","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the Rand index — compute_rand_index","text":"Rand index numeric value","code":""},{"path":"https://scmethods.github.io/scregclust/reference/compute_rand_index.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute the Rand index — compute_rand_index","text":"W. M. Rand (1971). \"Objective criteria evaluation clustering methods\". Journal American Statistical Association 66 (336): 846–850. DOI:10.2307/2284239","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":null,"dir":"Reference","previous_headings":"","what":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"Implements estimation coop-lasso problem.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"","code":"coop_lasso( y, x, lambda, weights, beta_0 = NULL, rho_0 = 0.2, alpha_0 = 1.5, n_update = 2L, eps_corr = 0.2, max_iter = 1000L, eps_rel = 1e-08, eps_abs = 1e-12, verbose = FALSE )"},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"y Target (n x m) x Design matrix (n x p) lambda Penalization parameter weights specific weight group (typically sqrt(group size)). beta_0 Initial value coefficients, allowing warm start. Can set NULL, results initial beta zero matrix. rho_0 Initial ADMM step-size alpha_0 Initial ADMM relaxation parameter n_update Number steps -updates step-size/adaptation parameters eps_corr Lower bound correlation step-size update steps max_iter Maximum number iterations eps_rel Relative tolerance convergence check eps_abs Absolute tolerance convergence check verbose Whether information optimization process printed terminal","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"list containing beta coefficients convergence iterations Number iterations","code":""},{"path":"https://scmethods.github.io/scregclust/reference/coop_lasso.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"ADMM algorithm for solving the group-penalized least squares problem — coop_lasso","text":"Xu et al. (2017) Adaptive relaxed ADMM: Convergence theory practical implementation. DOI 10.1109/CVPR.2017.765","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Format count table nicely — count_table","title":"Format count table nicely — count_table","text":"Format count table nicely","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format count table nicely — count_table","text":"","code":"count_table(counts, title, row_names, col_width = 5)"},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format count table nicely — count_table","text":"counts list count vectors 1 + n_cl entries . NA values replaced - title title table row_names vector row names, one count vector col_width minimum width columns","code":""},{"path":"https://scmethods.github.io/scregclust/reference/count_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format count table nicely — count_table","text":"string formatted table","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":null,"dir":"Reference","previous_headings":"","what":"Fast computation of correlation — fast_cor","title":"Fast computation of correlation — fast_cor","text":"uses memory-intensive much faster algorithm built-cor function.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fast computation of correlation — fast_cor","text":"","code":"fast_cor(x, y)"},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fast computation of correlation — fast_cor","text":"x first input matrix y second input matrix","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fast computation of correlation — fast_cor","text":"Correlations matrix columns x y","code":""},{"path":"https://scmethods.github.io/scregclust/reference/fast_cor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fast computation of correlation — fast_cor","text":"Computes correlation columns x y.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine module sizes — find_module_sizes","title":"Determine module sizes — find_module_sizes","text":"Determine module sizes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine module sizes — find_module_sizes","text":"","code":"find_module_sizes(module, n_modules)"},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine module sizes — find_module_sizes","text":"module Vector module indices n_modules Total number modules","code":""},{"path":"https://scmethods.github.io/scregclust/reference/find_module_sizes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine module sizes — find_module_sizes","text":"named vector containining name module (index \"Noise\") number elements module","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the average number of active regulators per module — get_avg_num_regulators","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"Get average number active regulators per module","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"","code":"get_avg_num_regulators(fit)"},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"fit object class scRegClust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_avg_num_regulators.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the average number of active regulators per module — get_avg_num_regulators","text":"data.frame containing average number active regulators per module penalization parameter.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":null,"dir":"Reference","previous_headings":"","what":"Return the number of final configurations — get_num_final_configs","title":"Return the number of final configurations — get_num_final_configs","text":"Returns number final configurations per penalization parameter scRegClust object.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return the number of final configurations — get_num_final_configs","text":"","code":"get_num_final_configs(fit)"},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return the number of final configurations — get_num_final_configs","text":"fit object class scRegClust","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_num_final_configs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return the number of final configurations — get_num_final_configs","text":"integer vector containing number final configurations penalization parameter.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Rand indices — get_rand_indices","title":"Compute Rand indices — get_rand_indices","text":"Compute Rand indices fitted scregclust object","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Rand indices — get_rand_indices","text":"","code":"get_rand_indices(fit, groundtruth, adjusted = TRUE)"},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Rand indices — get_rand_indices","text":"fit object class scregclust groundtruth known clustering target genes (integer vector) adjusted TRUE, Adjusted Rand index computed. Otherwise ordinary Rand index computed.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Rand indices — get_rand_indices","text":"data.frame containing Rand indices. Since can one final configuration penalization parameters, Rand indices averaged fixed penalization parameter. Returned mean, standard deviation number final configurations averaged.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_rand_indices.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute Rand indices — get_rand_indices","text":"W. M. Rand (1971). \"Objective criteria evaluation clustering methods\". Journal American Statistical Association 66 (336): 846–850. DOI:10.2307/2284239 Lawrence Hubert Phipps Arabie (1985). \"Comparing partitions\". Journal Classification. 2 (1): 193–218. DOI:10.1007/BF01908075","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Return list of regulator genes — get_regulator_list","title":"Return list of regulator genes — get_regulator_list","text":"Return list regulator genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return list of regulator genes — get_regulator_list","text":"","code":"get_regulator_list(mode = c(\"TF\", \"kinase\"))"},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return list of regulator genes — get_regulator_list","text":"mode Determines genes considered regulators. Currently supports TF=transcription factors kinases.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_regulator_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return list of regulator genes — get_regulator_list","text":"list gene symbols","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract target gene modules for given penalization parameters — get_target_gene_modules","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"Extract target gene modules given penalization parameters","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"","code":"get_target_gene_modules(fit, penalization = NULL)"},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"fit object class scregclust penalization numeric vector penalization parameters. penalization parameters specificed must used used fitting fit object.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/get_target_gene_modules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract target gene modules for given penalization parameters — get_target_gene_modules","text":"list lists final target modules. One list parameter penalization. lists contain moduleing target genes final configuration.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"Computes Jaccard distance rows matrix returns sparse symmetric indicator matrix containing entries distance less given upper bound. Note diagonal always 1.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"","code":"jaccard_indicator(x, upper_bnd = 0.8)"},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"x input matrix vectors compared rows. upper_bnd pairs Jaccard distance upper bound returned 1 others receive entry 0.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute indicator matrix of pairwise distances smaller than threshold — jaccard_indicator","text":"list vectors describing sparse lower triangular pattern matrix Row indices j Column indices","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"Perform computations thresholded Jaccard distance","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"","code":"jaccard_indicator_comp(gs, eps)"},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"gs list integer vectors, one row, giving column indices non-zero elements row NULL whole row empty. eps upper bound Jaccard distance (1 - eps becomes lower bound Jaccard similarity)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"list row column indicies #row x #row indicator matrix specifying rows original matrix distance eps.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/jaccard_indicator_comp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform the computations for thresholded Jaccard distance — jaccard_indicator_comp","text":"function optimized sparse matrices computes pairwise Jaccard distances rows input matrix. Note actual distance saved. Instead, threshold (eps) supplied indicator matrix returned, one indicating distance smaller eps (equivalently, Jaccard similarity larger 1 - eps).","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform the k-means++ algorithm — kmeanspp","title":"Perform the k-means++ algorithm — kmeanspp","text":"Performs k-means++ algorithm cluster rows input matrix.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform the k-means++ algorithm — kmeanspp","text":"","code":"kmeanspp(x, n_cluster, n_init_clusterings = 10L, n_max_iter = 10L)"},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform the k-means++ algorithm — kmeanspp","text":"x Input matrix (n x p) n_cluster Number clusters n_init_clusterings Number repeated random initialisations perform n_max_iter Number maximum iterations perform k-means algorithm","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform the k-means++ algorithm — kmeanspp","text":"object class stats::kmeans.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform the k-means++ algorithm — kmeanspp","text":"Estimation repeated","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Perform the k-means++ algorithm — kmeanspp","text":"David Arthur Sergei Vassilvitskii. K-Means++: advantages careful seeding. Proceedings Eighteenth Annual ACM-SIAM Symposium Discrete Algorithms, SODA '07, pages 1027––1035. Society Industrial Applied Mathematics, 2007.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"Determine initial centers kmeans++ algorithm","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"","code":"kmeanspp_init(n_cluster, x = NULL, dm = NULL)"},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"x data matrix clustered dm distance matrix (rows x; class \"dist\")","code":""},{"path":"https://scmethods.github.io/scregclust/reference/kmeanspp_init.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine initial centers for the kmeans++ algorithm — kmeanspp_init","text":"Row indices initial cluster centres x","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"Plot average silhouette scores average predictive \\(R^2\\)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"","code":"plot_module_count_helper(list_of_fits, penalization)"},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"list_of_fits list scregclust objects fit dataset across variety module counts (varying n_modules running scregclust). penalization Either single numeric value requesting results penalty parameter across fits list_of_fits, one individual fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_module_count_helper.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot average silhouette scores and average predictive \\(R^2\\) — plot_module_count_helper","text":"ggplot2 plot showing average silhouette score average predictive \\(R^2\\)","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"Plotting regulatory table scregclust directed graph","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"","code":"plot_regulator_network( output, arrow_size = 0.3, edge_scaling = 30, no_links = 6, col = c(\"gray80\", \"#FC7165\", \"#BD828C\", \"#9D8A9F\", \"#7D92B2\", \"#BDA88C\", \"#FCBD65\", \"#F2BB90\", \"#E7B9BA\", \"#BDB69C\", \"#92B27D\", \"#9B8BA5\", \"#9D7DB2\", \"#94A5BF\") )"},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"output Object type scregclust_output fit scregclust algorithm. arrow_size Size arrow head edge_scaling Scaling factor edge width no_links Threshold value (0-10) number edges show, higher value = stringent threshold = less edges col color","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_regulator_network.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting the regulatory table from scregclust as a directed graph — plot_regulator_network","text":"Graph gene modules regulators nodes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot individual silhouette scores — plot_silhouettes","title":"Plot individual silhouette scores — plot_silhouettes","text":"Plot individual silhouette scores","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot individual silhouette scores — plot_silhouettes","text":"","code":"plot_silhouettes(list_of_fits, penalization, final_config = 1L)"},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot individual silhouette scores — plot_silhouettes","text":"list_of_fits list scregclust objects fit dataset across variety module counts (varying n_modules running scregclust). penalization Either single numeric value requesting results penalty parameter across fits list_of_fits, one individual fit. final_config final configuration visualised. Either single number used fits list_of_fits, one individual fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/plot_silhouettes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot individual silhouette scores — plot_silhouettes","text":"ggplot2 plot showing silhouette scores supplied fit.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":null,"dir":"Reference","previous_headings":"","what":"Quick'n'dirty progress bar — progstr","title":"Quick'n'dirty progress bar — progstr","text":"Creates progress bar returns string.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quick'n'dirty progress bar — progstr","text":"","code":"progstr(step, n_steps, name, finished = FALSE, progress_length = 20L)"},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Quick'n'dirty progress bar — progstr","text":"step current step worked n_steps total number steps name name process finished whether process finished progress_length length progress bar ascii signs","code":""},{"path":"https://scmethods.github.io/scregclust/reference/progstr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Quick'n'dirty progress bar — progstr","text":"string formatted progress bar","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove empty modules — remove_empty_modules","title":"Remove empty modules — remove_empty_modules","text":"Remove empty modules","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove empty modules — remove_empty_modules","text":"","code":"remove_empty_modules(module)"},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove empty modules — remove_empty_modules","text":"module Vector module indices","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove empty modules — remove_empty_modules","text":"updated vector module indices empty modules removed.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/remove_empty_modules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove empty modules — remove_empty_modules","text":"iterates modules positive index, leaving noise module untouched.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset input 3d-array by filling matrix along first dimension — reset_array","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"Reset input 3d-array filling matrix along first dimension","code":""},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"","code":"reset_array(arr, input)"},{"path":"https://scmethods.github.io/scregclust/reference/reset_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reset input 3d-array by filling matrix along first dimension — reset_array","text":"arr 3d-array dimension n_cl x n_obs x n_genes input matrix size n_obs x n_genes","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":null,"dir":"Reference","previous_headings":"","what":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data — scregclust-package","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data — scregclust-package","text":"Implementation scregclust algorithm (Larsson, Held, et al., 2024, doi:10.1038/s41467-024-53954-3 ) aims reconstruct regulatory programs target genes single cell data. Target genes clustered modules module associated linear model accounting regulatory program driving genes contains.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data — scregclust-package","text":"Computational methods scregclust algorithm","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/scregclust-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"scregclust: Reconstructing the Regulatory Programs of Target Genes in scRNA-seq Data — scregclust-package","text":"Ida Larsson, Felix Held, Sven Nelander","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":null,"dir":"Reference","previous_headings":"","what":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"Use scRegClust algorithm determine gene modules regulatory programs single-cell data.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"","code":"scregclust( expression, genesymbols, is_regulator, penalization, n_modules, initial_target_modules = NULL, sample_assignment = NULL, center = TRUE, split1_proportion = 0.5, total_proportion = 1, split_indices = NULL, prior_indicator = NULL, prior_genesymbols = NULL, prior_baseline = 1e-06, prior_weight = 0.5, min_module_size = 0L, allocate_per_obs = TRUE, noise_threshold = 0.025, n_cycles = 50L, use_kmeanspp_init = TRUE, n_initializations = 50L, max_optim_iter = 10000L, tol_coop_rel = 1e-08, tol_coop_abs = 1e-12, tol_nnls = 1e-04, compute_predictive_r2 = TRUE, compute_silhouette = FALSE, nowarnings = FALSE, verbose = TRUE )"},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"expression p x n matrix pre-processed single cell expression data p rows genes n columns cells. genesymbols vector gene names corresponding rows expression. length p. is_regulator indicator vector 1 indicates corresponding row expression candidate regulator. rows represent target genes. length p. penalization Sparsity penalty related amount regulators associated module. Either single positive number vector positive numbers. n_modules Requested number modules (integer). provided without specifying initial_target_modules, initial module allocation performed cross-correlation matrix targets genes first dataset data splitting. initial_target_modules initial assignment target genes modules length sum(is_regulator == 0L). specified, see n_modules regarding module initialization. provided, use_kmeanspp_init n_initializations ignored. sample_assignment vector sample assignment cell, can used perform data splitting stratification. length n. stratification NULL supplied. center Whether genes centered within subgroup defined sample_assignment. split1_proportion proportion use first dataset data splitting. proportion second dataset 1 - split1_proportion. stratification sample_assignment used, proportion strata controlled. total_proportion Can used use proportion supplied observations. proportion first dataset data splitting relation full dataset total_proportion * split1_proportion. split_indices Can used provide explicit data split. supplied split1_proportion, total_proportion ignored. Note sample_assigment provided center == TRUE, subgroup centering performed case random splitting. vector length n containing entries 1 cells first data split, 2 cells second data split NA cells excluded computations. prior_indicator indicator matrix (sparse dense) size q x q indicates whether known functional relationship two genes. Ideally, supplied sparse matrix (sparseMatrix Matrix package). , matrix converted one. prior_genesymbols vector gene names length q corresponding rows/columns prior_indicator. genesymbols, useful overlap. prior_baseline positive baseline network prior. larger parameter , less impact network prior . prior_weight number 0 1 indicating strength prior relation data. 0 ignores prior makes algorithm completely data-driven. 1 uses prior module allocation. min_module_size Minimum required size target genes module. Smaller modules emptied. allocate_per_obs Whether module allocation performed observation second data split separately. FALSE, target genes allocated modules aggregate sum squares across observations second data split. noise_threshold Threshold best \\(R^2\\) target gene gets identified noise. n_cycles Number maximum algorithmic cycles. use_kmeanspp_init Use kmeans++ module initialization initial_target_modules single integer; otherwise use kmeans random initial cluster centers n_initializations Number kmeans(++) initialization runs. max_optim_iter Maximum number iterations optimization coop-Lasso NNLS steps. tol_coop_rel Relative convergence tolerance optimization coop-Lasso step. tol_coop_abs Absolute convergence tolerance optimization coop-Lasso step. tol_nnls Convergence tolerance optimization NNLS step. compute_predictive_r2 Whether compute predictive \\(R^2\\) per module well regulator importance. compute_silhouette Whether compute silhouette scores target gene. nowarnings turned warning messages shown. verbose Whether print progress.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Uncover gene modules and their regulatory programs from single-cell data — scregclust","text":"list S3 class scregclust containing penalization supplied penalization parameters results list result lists (S3 class scregclust_result), one supplied penalization parameter. See . initial_target_modules Initial allocation target genes modules. split_indices either verbatim vector given input vector encoding splits NA = included, 1 = split 1 2 = split 2. Allows reproduciblity data splits. supplied penalization parameter, results contains list current penalization parameter, supplied genesymbols filtering (used fitting), supplied is_regulator vector filtering (used fitting), number fitted modules n_modules, whether current run converged single configuration (boolean), well output object containing numeric results final configuration. possible algorithm ends finite cycle configurations instead unique final configuration. Therefore, output list element list following contents: reg_table regulator table, matrix weights regulator module module vector length genesymbols containing module assignments genes regulators marked NA. Genes considered noise marked -1. module_all module, however, genes marked noise (-1 module) assigned module largest \\(R^2\\), even noise_threshold. r2 matrix predictive \\(R^2\\) value target gene module best_r2 vector best predictive \\(R^2\\) gene (regulators marked NA) best_r2_idx module index corresponding best predictive \\(R^2\\) gene (regulators marked NA) r2_module vector predictive \\(R^2\\) values module (included compute_predictive_r2 == TRUE) importance matrix importance values regulator (rows) module (columns) (included compute_predictive_r2 == TRUE) r2_cross_module_per_target matrix cross module \\(R^2\\) values target gene (rows) module (columns) (included compute_silhouette == TRUE) silhouette vector silhouette scores target gene (included compute_silhouette == TRUE) models regulator selection module matrix regulators rows modules columns signs regulator signs module matrix regulators rows modules columns weights average regulator coefficient module coeffs list regulator coefficient matrices module target genes re-estimated NNLS step sigmas matrix residual variances, one per target gene module; derived residuals NNLS step","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":null,"dir":"Reference","previous_headings":"","what":"Package data before clustering — scregclust_format","title":"Package data before clustering — scregclust_format","text":"Package data clustering","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Package data before clustering — scregclust_format","text":"","code":"scregclust_format(expression_matrix, mode = c(\"TF\", \"kinase\"))"},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Package data before clustering — scregclust_format","text":"expression_matrix p x n gene expression matrix gene symbols rownames. mode Determines genes considered regulators.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/scregclust_format.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Package data before clustering — scregclust_format","text":"list genesymbols gene symbols extracted expression matrix sample_assignment vector filled 1's length columns gene expression matrix. is_regulator Whether gene considered regulator , determined dependent mode.","code":""},{"path":[]},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Sample — split_sample","title":"Split Sample — split_sample","text":"Splits sample train test set","code":""},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Sample — split_sample","text":"","code":"split_sample( z, stratification, is_regulator, split_indices, split1_proportion, total_proportion, center )"},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Sample — split_sample","text":"z matrix single cell data rows genes columns cells. stratification vector sampling stratified length ncol(z) is_regulator indicator vector, telling rows z candidate regulators split_indices vector given split indices. can NULL split1_proportion proportion include first data split total_proportion proportion data include overall splitting center TRUE data row-centered. Set FALSE otherwise.","code":""},{"path":"https://scmethods.github.io/scregclust/reference/split_sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Sample — split_sample","text":"list containing z1_reg first data split, TF-part z2_reg second data split, TF-part z1_target first data split, non-TF part z2_target second data split, non-TF part split_indices either verbatim vector given input vector encoding splits NA = included, 1 = split 1 2 = split 2. Allows reproduciblity data splits.","code":""}]