diff --git a/404.html b/404.html index edb3c744..ba52ef53 100644 --- a/404.html +++ b/404.html @@ -40,7 +40,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html index acf0eb75..5ce56300 100644 --- a/CODE_OF_CONDUCT.html +++ b/CODE_OF_CONDUCT.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html index 23153931..f0175073 100644 --- a/CONTRIBUTING.html +++ b/CONTRIBUTING.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/SUPPORT.html b/SUPPORT.html index 8e451870..7451dbb4 100644 --- a/SUPPORT.html +++ b/SUPPORT.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/articles/TenX_data_download.html b/articles/TenX_data_download.html index d429f474..689339e4 100644 --- a/articles/TenX_data_download.html +++ b/articles/TenX_data_download.html @@ -41,7 +41,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -119,7 +119,7 @@

Leonardo University
lcolladotor@gmail.com -

11 July 2024

+

12 July 2024

Source: vignettes/TenX_data_download.Rmd @@ -291,10 +291,7 @@

Download spaceranger output files "V1_Human_Lymph_Node_analysis.tar.gz" ) ) -lymph.data <- sapply(lymph.url, BiocFileCache::bfcrpath, x = bfc) -#> adding rname 'https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Human_Lymph_Node/V1_Human_Lymph_Node_filtered_feature_bc_matrix.tar.gz' -#> adding rname 'https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Human_Lymph_Node/V1_Human_Lymph_Node_spatial.tar.gz' -#> adding rname 'https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Human_Lymph_Node/V1_Human_Lymph_Node_analysis.tar.gz' +lymph.data <- sapply(lymph.url, BiocFileCache::bfcrpath, x = bfc)

10x Genomics provides the files in compressed tarballs (.tar.gz file extension). Which is why we’ll need to use utils::untar() to decompress the files. This will create @@ -537,14 +534,12 @@

From Gencode"release_32/gencode.v32.annotation.gtf.gz" ) ) -#> adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_32/gencode.v32.annotation.gtf.gz' -
-
+
 ## Show the GTF cache location
 gtf_cache
 #>                                                                                                          BFC23 
 #> "/Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/100014a479f08_gencode.v32.annotation.gtf.gz"
-
+
-
+
 
 ## Drop the few genes for which we don't have information
 spe <- spe[!is.na(match_genes), ]
@@ -626,7 +621,7 @@ 
Examples

 ## at the layer-level
 if (!exists("sce_layer")) sce_layer <- fetch_data("sce_layer")
 #> snapshotDate(): 2024-04-29
-#> 2024-07-11 14:08:44.042608 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1
+#> 2024-07-12 13:31:43.167783 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1
 
 ## Explore the data
 sce_layer
@@ -175,10 +175,12 @@ 
Examples

 #> dim: 22331 76 
 #> metadata(0):
 #> assays(2): counts logcounts
-#> rownames(22331): ENSG00000243485 ENSG00000238009 ... ENSG00000278384 ENSG00000271254
+#> rownames(22331): ENSG00000243485 ENSG00000238009 ... ENSG00000278384
+#>   ENSG00000271254
 #> rowData names(10): source type ... is_top_hvg is_top_hvg_sce_layer
 #> colnames(76): 151507_Layer1 151507_Layer2 ... 151676_Layer6 151676_WM
-#> colData names(13): sample_name layer_guess ... layer_guess_reordered_short spatialLIBD
+#> colData names(13): sample_name layer_guess ...
+#>   layer_guess_reordered_short spatialLIBD
 #> reducedDimNames(6): PCA TSNE_perplexity5 ... UMAP_neighbors15 PCAsub
 #> mainExpName: NULL
 #> altExpNames(0):
diff --git a/reference/frame_limits.html b/reference/frame_limits.html
index 63513285..b1d681ed 100644
--- a/reference/frame_limits.html
+++ b/reference/frame_limits.html
@@ -27,7 +27,7 @@
       
       
         spatialLIBD
-        1.17.5
+        1.17.6
       
     

 
@@ -155,7 +155,7 @@ 
Examples

     frame_limits(spe, sampleid = "151673")
 }
 #> snapshotDate(): 2024-04-29
-#> 2024-07-11 14:08:45.826378 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1
+#> 2024-07-12 13:31:44.633745 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1
 #> $y_min
 #> [1] 64
 #> 
diff --git a/reference/gene_set_enrichment.html b/reference/gene_set_enrichment.html
index b4532ea3..4cb2699c 100644
--- a/reference/gene_set_enrichment.html
+++ b/reference/gene_set_enrichment.html
@@ -22,7 +22,7 @@
       
       
         spatialLIBD
-        1.17.5
+        1.17.6
       
     

 
@@ -177,7 +177,7 @@ 
Examples

     modeling_results <- fetch_data(type = "modeling_results")
 }
 #> snapshotDate(): 2024-04-29
-#> 2024-07-11 14:08:56.604177 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
+#> 2024-07-12 13:31:55.530746 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
 
 ## Compute the gene set enrichment results
 asd_sfari_enrichment <- gene_set_enrichment(
@@ -188,28 +188,50 @@ 
Examples

 
 ## Explore the results
 asd_sfari_enrichment
-#>           OR         Pval   test NumSig SetSize                   ID model_type fdr_cut
-#> 1  1.2659915 1.761332e-03     WM    231     869       Gene_SFARI_all enrichment     0.1
-#> 2  1.1819109 9.895949e-02     WM     90     355      Gene_SFARI_high enrichment     0.1
-#> 3  1.2333378 1.853021e-01     WM     31     118 Gene_SFARI_syndromic enrichment     0.1
-#> 4  0.9702022 6.130806e-01 Layer1     71     869       Gene_SFARI_all enrichment     0.1
-#> 5  0.7192630 9.493328e-01 Layer1     22     355      Gene_SFARI_high enrichment     0.1
-#> 6  1.1216176 4.054532e-01 Layer1     11     118 Gene_SFARI_syndromic enrichment     0.1
-#> 7  2.7377140 5.096514e-21 Layer2    137     869       Gene_SFARI_all enrichment     0.1
-#> 8  2.7066379 8.845390e-10 Layer2     57     355      Gene_SFARI_high enrichment     0.1
-#> 9  2.6632367 3.564638e-04 Layer2     19     118 Gene_SFARI_syndromic enrichment     0.1
-#> 10 1.3579958 1.687561e-01 Layer3     14     869       Gene_SFARI_all enrichment     0.1
-#> 11 1.1738012 4.264658e-01 Layer3      5     355      Gene_SFARI_high enrichment     0.1
-#> 12 2.8947133 5.518757e-02 Layer3      4     118 Gene_SFARI_syndromic enrichment     0.1
-#> 13 1.2423009 1.544115e-01 Layer4     29     869       Gene_SFARI_all enrichment     0.1
-#> 14 1.1445522 3.748009e-01 Layer4     11     355      Gene_SFARI_high enrichment     0.1
-#> 15 2.6106289 1.575232e-02 Layer4      8     118 Gene_SFARI_syndromic enrichment     0.1
-#> 16 2.0969125 7.366596e-07 Layer5     60     869       Gene_SFARI_all enrichment     0.1
-#> 17 2.0956628 9.450654e-04 Layer5     25     355      Gene_SFARI_high enrichment     0.1
-#> 18 0.7064982 7.951889e-01 Layer5      3     118 Gene_SFARI_syndromic enrichment     0.1
-#> 19 2.6716353 1.472539e-07 Layer6     41     869       Gene_SFARI_all enrichment     0.1
-#> 20 2.6206690 5.845493e-04 Layer6     17     355      Gene_SFARI_high enrichment     0.1
-#> 21 2.2573853 7.927915e-02 Layer6      5     118 Gene_SFARI_syndromic enrichment     0.1
+#>           OR         Pval   test NumSig SetSize                   ID model_type
+#> 1  1.2659915 1.761332e-03     WM    231     869       Gene_SFARI_all enrichment
+#> 2  1.1819109 9.895949e-02     WM     90     355      Gene_SFARI_high enrichment
+#> 3  1.2333378 1.853021e-01     WM     31     118 Gene_SFARI_syndromic enrichment
+#> 4  0.9702022 6.130806e-01 Layer1     71     869       Gene_SFARI_all enrichment
+#> 5  0.7192630 9.493328e-01 Layer1     22     355      Gene_SFARI_high enrichment
+#> 6  1.1216176 4.054532e-01 Layer1     11     118 Gene_SFARI_syndromic enrichment
+#> 7  2.7377140 5.096514e-21 Layer2    137     869       Gene_SFARI_all enrichment
+#> 8  2.7066379 8.845390e-10 Layer2     57     355      Gene_SFARI_high enrichment
+#> 9  2.6632367 3.564638e-04 Layer2     19     118 Gene_SFARI_syndromic enrichment
+#> 10 1.3579958 1.687561e-01 Layer3     14     869       Gene_SFARI_all enrichment
+#> 11 1.1738012 4.264658e-01 Layer3      5     355      Gene_SFARI_high enrichment
+#> 12 2.8947133 5.518757e-02 Layer3      4     118 Gene_SFARI_syndromic enrichment
+#> 13 1.2423009 1.544115e-01 Layer4     29     869       Gene_SFARI_all enrichment
+#> 14 1.1445522 3.748009e-01 Layer4     11     355      Gene_SFARI_high enrichment
+#> 15 2.6106289 1.575232e-02 Layer4      8     118 Gene_SFARI_syndromic enrichment
+#> 16 2.0969125 7.366596e-07 Layer5     60     869       Gene_SFARI_all enrichment
+#> 17 2.0956628 9.450654e-04 Layer5     25     355      Gene_SFARI_high enrichment
+#> 18 0.7064982 7.951889e-01 Layer5      3     118 Gene_SFARI_syndromic enrichment
+#> 19 2.6716353 1.472539e-07 Layer6     41     869       Gene_SFARI_all enrichment
+#> 20 2.6206690 5.845493e-04 Layer6     17     355      Gene_SFARI_high enrichment
+#> 21 2.2573853 7.927915e-02 Layer6      5     118 Gene_SFARI_syndromic enrichment
+#>    fdr_cut
+#> 1      0.1
+#> 2      0.1
+#> 3      0.1
+#> 4      0.1
+#> 5      0.1
+#> 6      0.1
+#> 7      0.1
+#> 8      0.1
+#> 9      0.1
+#> 10     0.1
+#> 11     0.1
+#> 12     0.1
+#> 13     0.1
+#> 14     0.1
+#> 15     0.1
+#> 16     0.1
+#> 17     0.1
+#> 18     0.1
+#> 19     0.1
+#> 20     0.1
+#> 21     0.1
diff --git a/reference/gene_set_enrichment_plot.html b/reference/gene_set_enrichment_plot.html index aa83dd49..563642c8 100644 --- a/reference/gene_set_enrichment_plot.html +++ b/reference/gene_set_enrichment_plot.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -179,7 +179,7 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:08:57.530897 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:31:56.67117 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 ## Compute the gene set enrichment results asd_sfari_enrichment <- gene_set_enrichment( diff --git a/reference/geom_spatial.html b/reference/geom_spatial.html index 73069a9f..8d3d1a7e 100644 --- a/reference/geom_spatial.html +++ b/reference/geom_spatial.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -176,7 +176,7 @@

Examples

rm(spe_sub) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:08:58.897291 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:31:57.896908 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/get_colors.html b/reference/get_colors.html index e529b5bc..ad222ec5 100644 --- a/reference/get_colors.html +++ b/reference/get_colors.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -106,32 +106,43 @@

Examples

## Obtain the necessary data if (!exists("sce_layer")) sce_layer <- fetch_data("sce_layer") #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:17.128082 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:09.599532 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 ## Example layer colors with the corresponding names get_colors(libd_layer_colors, sce_layer$layer_guess) -#> Layer1 Layer2 Layer3 Layer4 Layer5 Layer6 WM -#> "#F0027F" "#377EB8" "#4DAF4A" "#984EA3" "#FFD700" "#FF7F00" "#1A1A1A" -#> NA WM2 -#> "transparent" "#666666" +#> Layer1 Layer2 Layer3 Layer4 Layer5 +#> "#F0027F" "#377EB8" "#4DAF4A" "#984EA3" "#FFD700" +#> Layer6 WM NA WM2 +#> "#FF7F00" "#1A1A1A" "transparent" "#666666" get_colors(libd_layer_colors, sce_layer$layer_guess_reordered_short) -#> L1 L2 L3 L4 L5 L6 WM -#> "#F0027F" "#377EB8" "#4DAF4A" "#984EA3" "#FFD700" "#FF7F00" "#1A1A1A" -#> NA WM2 -#> "transparent" "#666666" +#> L1 L2 L3 L4 L5 +#> "#F0027F" "#377EB8" "#4DAF4A" "#984EA3" "#FFD700" +#> L6 WM NA WM2 +#> "#FF7F00" "#1A1A1A" "transparent" "#666666" ## Example where colors are assigned automatically ## based on a pre-defined set of colors get_colors(clusters = sce_layer$kmeans_k7) -#> 1 2 3 4 5 6 7 8 9 -#> "#b2df8a" "#e41a1c" "#377eb8" "#4daf4a" "#ff7f00" "gold" "#a65628" "#999999" "black" -#> 10 11 12 -#> "grey" "white" "purple" +#> 1 2 3 4 5 6 7 +#> "#b2df8a" "#e41a1c" "#377eb8" "#4daf4a" "#ff7f00" "gold" "#a65628" ## Example where Polychrome::palette36.colors() gets used get_colors(clusters = letters[seq_len(13)]) #> <colors> #> #5A5156FF #E4E1E3FF #F6222EFF #FE00FAFF #16FF32FF #3283FEFF #FEAF16FF #B00068FF #1CFFCEFF #90AD1CFF #2ED9FFFF #DEA0FDFF #AA0DFEFF + +## What happens if you have a logical variable with NAs? +set.seed(20240712) +log_var <- sample(c(TRUE, FALSE, NA), + 1000, + replace = TRUE, + prob = c(0.3, 0.15, 0.55)) +log_var_sorted <- sort_clusters(log_var) +## A color does get assigned to 'NA', but will be overwritten by +## 'na_color' passed to `vis_clus_p()` and related functions. +get_colors(colors = NULL, clusters = log_var_sorted) +#> TRUE FALSE <NA> +#> "#b2df8a" "#e41a1c" "#377eb8" diff --git a/reference/img_edit.html b/reference/img_edit.html index a6585317..9986e490 100644 --- a/reference/img_edit.html +++ b/reference/img_edit.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -223,7 +223,7 @@

Examples

plot(x) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:19.080367 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:11.520313 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/img_update.html b/reference/img_update.html index befef34e..d139f77c 100644 --- a/reference/img_update.html +++ b/reference/img_update.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -140,7 +140,7 @@

Examples

imgData(img_update(spe, sampleid = "151507", brightness = 25)) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:30.05133 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:22.60946 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 #> DataFrame with 13 rows and 4 columns #> sample_id image_id data scaleFactor #> <character> <character> <list> <numeric> diff --git a/reference/img_update_all.html b/reference/img_update_all.html index a3221a9d..ca7eb5cf 100644 --- a/reference/img_update_all.html +++ b/reference/img_update_all.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -139,13 +139,7 @@

Examples

imgData(img_update_all(spe, brightness = 25)) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:40.197356 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 -#> adding rname 'https://spatial-dlpfc.s3.us-east-2.amazonaws.com/images/151670_tissue_lowres_image.png' -#> -#> adding rname 'https://spatial-dlpfc.s3.us-east-2.amazonaws.com/images/151671_tissue_lowres_image.png' -#> -#> adding rname 'https://spatial-dlpfc.s3.us-east-2.amazonaws.com/images/151672_tissue_lowres_image.png' -#> +#> 2024-07-12 13:32:32.973243 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 #> DataFrame with 24 rows and 4 columns #> sample_id image_id data scaleFactor #> <character> <character> <list> <numeric> diff --git a/reference/index.html b/reference/index.html index 59680bb9..a3fcd330 100644 --- a/reference/index.html +++ b/reference/index.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/layer_boxplot.html b/reference/layer_boxplot.html index bde7dd96..78862daf 100644 --- a/reference/layer_boxplot.html +++ b/reference/layer_boxplot.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -186,10 +186,10 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:52.469609 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:44.218557 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer") #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:53.117352 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:45.06449 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 ## Top 2 genes from the enrichment model sig_genes <- sig_genes_extract_all( diff --git a/reference/layer_matrix_plot.html b/reference/layer_matrix_plot.html index 2a72d558..68579ef7 100644 --- a/reference/layer_matrix_plot.html +++ b/reference/layer_matrix_plot.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/layer_stat_cor.html b/reference/layer_stat_cor.html index 3e09e8c4..a3c65a9b 100644 --- a/reference/layer_stat_cor.html +++ b/reference/layer_stat_cor.html @@ -17,7 +17,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -157,7 +157,7 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:55.633254 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:47.805356 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 ## Compute the correlations cor_stats_layer <- layer_stat_cor( @@ -176,20 +176,20 @@

Examples

#> 21 (3) 0.6281443 -0.050336358 -0.1988774 #> 7 (4) 0.1850724 -0.197283175 -0.2716890 summary(cor_stats_layer) -#> WM Layer6 Layer5 Layer4 Layer3 -#> Min. :-0.46352 Min. :-0.197283 Min. :-0.27169 Min. :-0.253477 Min. :-0.36105 -#> 1st Qu.:-0.26653 1st Qu.:-0.071919 1st Qu.:-0.14318 1st Qu.:-0.152714 1st Qu.:-0.10007 -#> Median :-0.19813 Median :-0.039558 Median : 0.03858 Median : 0.003875 Median : 0.05998 -#> Mean :-0.02982 Mean : 0.004476 Mean : 0.01355 Mean : 0.019215 Mean : 0.02022 -#> 3rd Qu.: 0.12482 3rd Qu.: 0.018964 3rd Qu.: 0.16692 3rd Qu.: 0.160288 3rd Qu.: 0.13638 -#> Max. : 0.71541 Max. : 0.457031 Max. : 0.30194 Max. : 0.425598 Max. : 0.56413 -#> Layer2 Layer1 -#> Min. :-0.31419 Min. :-0.29670 -#> 1st Qu.:-0.06673 1st Qu.:-0.12129 -#> Median : 0.01664 Median :-0.01590 -#> Mean : 0.01056 Mean :-0.01750 -#> 3rd Qu.: 0.11008 3rd Qu.: 0.03523 -#> Max. : 0.50734 Max. : 0.63940 +#> WM Layer6 Layer5 Layer4 +#> Min. :-0.46352 Min. :-0.197283 Min. :-0.27169 Min. :-0.253477 +#> 1st Qu.:-0.26653 1st Qu.:-0.071919 1st Qu.:-0.14318 1st Qu.:-0.152714 +#> Median :-0.19813 Median :-0.039558 Median : 0.03858 Median : 0.003875 +#> Mean :-0.02982 Mean : 0.004476 Mean : 0.01355 Mean : 0.019215 +#> 3rd Qu.: 0.12482 3rd Qu.: 0.018964 3rd Qu.: 0.16692 3rd Qu.: 0.160288 +#> Max. : 0.71541 Max. : 0.457031 Max. : 0.30194 Max. : 0.425598 +#> Layer3 Layer2 Layer1 +#> Min. :-0.36105 Min. :-0.31419 Min. :-0.29670 +#> 1st Qu.:-0.10007 1st Qu.:-0.06673 1st Qu.:-0.12129 +#> Median : 0.05998 Median : 0.01664 Median :-0.01590 +#> Mean : 0.02022 Mean : 0.01056 Mean :-0.01750 +#> 3rd Qu.: 0.13638 3rd Qu.: 0.11008 3rd Qu.: 0.03523 +#> Max. : 0.56413 Max. : 0.50734 Max. : 0.63940 ## Repeat with top_n set to 10 summary(layer_stat_cor( @@ -198,20 +198,27 @@

Examples

model_type = "enrichment", top_n = 10 )) -#> WM Layer6 Layer5 Layer4 -#> Min. :-0.419078 Min. :-0.245585 Min. :-0.309621 Min. :-0.333112 -#> 1st Qu.:-0.223879 1st Qu.:-0.148746 1st Qu.:-0.177987 1st Qu.:-0.119284 -#> Median :-0.104689 Median :-0.034822 Median : 0.049559 Median :-0.004004 -#> Mean :-0.003598 Mean :-0.008681 Mean :-0.004698 Mean : 0.007484 -#> 3rd Qu.: 0.040547 3rd Qu.: 0.036607 3rd Qu.: 0.145510 3rd Qu.: 0.116301 -#> Max. : 0.733922 Max. : 0.586829 Max. : 0.393224 Max. : 0.458987 -#> Layer3 Layer2 Layer1 -#> Min. :-0.3983700 Min. :-0.206511 Min. :-0.263452 -#> 1st Qu.:-0.1264219 1st Qu.:-0.101970 1st Qu.:-0.149696 -#> Median :-0.0000972 Median :-0.017683 Median :-0.064278 -#> Mean : 0.0055855 Mean : 0.009139 Mean :-0.001268 -#> 3rd Qu.: 0.1143531 3rd Qu.: 0.092592 3rd Qu.: 0.032686 -#> Max. : 0.6718714 Max. : 0.472751 Max. : 0.728782 +#> WM Layer6 Layer5 +#> Min. :-0.419078 Min. :-0.245585 Min. :-0.309621 +#> 1st Qu.:-0.223879 1st Qu.:-0.148746 1st Qu.:-0.177987 +#> Median :-0.104689 Median :-0.034822 Median : 0.049559 +#> Mean :-0.003598 Mean :-0.008681 Mean :-0.004698 +#> 3rd Qu.: 0.040547 3rd Qu.: 0.036607 3rd Qu.: 0.145510 +#> Max. : 0.733922 Max. : 0.586829 Max. : 0.393224 +#> Layer4 Layer3 Layer2 +#> Min. :-0.333112 Min. :-0.3983700 Min. :-0.206511 +#> 1st Qu.:-0.119284 1st Qu.:-0.1264219 1st Qu.:-0.101970 +#> Median :-0.004004 Median :-0.0000972 Median :-0.017683 +#> Mean : 0.007484 Mean : 0.0055855 Mean : 0.009139 +#> 3rd Qu.: 0.116301 3rd Qu.: 0.1143531 3rd Qu.: 0.092592 +#> Max. : 0.458987 Max. : 0.6718714 Max. : 0.472751 +#> Layer1 +#> Min. :-0.263452 +#> 1st Qu.:-0.149696 +#> Median :-0.064278 +#> Mean :-0.001268 +#> 3rd Qu.: 0.032686 +#> Max. : 0.728782 diff --git a/reference/layer_stat_cor_plot.html b/reference/layer_stat_cor_plot.html index a2cc9527..3d31c788 100644 --- a/reference/layer_stat_cor_plot.html +++ b/reference/layer_stat_cor_plot.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -146,7 +146,7 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:09:56.46779 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:32:48.779888 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 ## Compute the correlations cor_stats_layer <- layer_stat_cor( diff --git a/reference/libd_layer_colors.html b/reference/libd_layer_colors.html index 4ad45b7a..b09d4034 100644 --- a/reference/libd_layer_colors.html +++ b/reference/libd_layer_colors.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/locate_images.html b/reference/locate_images.html index cd77be24..c5961b06 100644 --- a/reference/locate_images.html +++ b/reference/locate_images.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/multi_gene_pca.html b/reference/multi_gene_pca.html index b83dd026..31b442de 100644 --- a/reference/multi_gene_pca.html +++ b/reference/multi_gene_pca.html @@ -22,7 +22,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/multi_gene_sparsity.html b/reference/multi_gene_sparsity.html index 942babfb..06be7c17 100644 --- a/reference/multi_gene_sparsity.html +++ b/reference/multi_gene_sparsity.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/multi_gene_z_score.html b/reference/multi_gene_z_score.html index 2ae809db..2c807fb2 100644 --- a/reference/multi_gene_z_score.html +++ b/reference/multi_gene_z_score.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/prep_stitched_data.html b/reference/prep_stitched_data.html index 4af67fca..894b0811 100644 --- a/reference/prep_stitched_data.html +++ b/reference/prep_stitched_data.html @@ -22,7 +22,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/read10xVisiumAnalysis.html b/reference/read10xVisiumAnalysis.html index b2d2eaca..d3bc2a74 100644 --- a/reference/read10xVisiumAnalysis.html +++ b/reference/read10xVisiumAnalysis.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/read10xVisiumWrapper.html b/reference/read10xVisiumWrapper.html index 600cb299..a90aa82b 100644 --- a/reference/read10xVisiumWrapper.html +++ b/reference/read10xVisiumWrapper.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/registration_block_cor.html b/reference/registration_block_cor.html index ccb3784e..24c313a1 100644 --- a/reference/registration_block_cor.html +++ b/reference/registration_block_cor.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -146,41 +146,41 @@

Examples

#> #> rgstr_devel > ## Pseudo-bulk #> rgstr_devel > sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:09:59.342752 make pseudobulk object -#> 2024-07-11 14:09:59.44328 drop lowly expressed genes -#> 2024-07-11 14:09:59.483301 normalize expression +#> 2024-07-12 13:32:51.615179 make pseudobulk object +#> 2024-07-12 13:32:51.712495 drop lowly expressed genes +#> 2024-07-12 13:32:51.75598 normalize expression #> #> rgstr_devel > colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 #> #> rgstr_devel > registration_mod <- registration_model(sce_pseudo, "age") -#> 2024-07-11 14:09:59.527977 create model matrix +#> 2024-07-12 13:32:51.802276 create model matrix #> #> rgstr_devel > head(registration_mod) #> registration_variableG0 registration_variableG1 registration_variableG2M @@ -198,8 +198,8 @@

Examples

#> E_G0 0 20.97006 #> A_G1 0 19.18719 block_cor <- registration_block_cor(sce_pseudo, registration_mod) -#> 2024-07-11 14:09:59.534617 run duplicateCorrelation() -#> 2024-07-11 14:10:00.089045 The estimated correlation is: -0.0187869166526901 +#> 2024-07-12 13:32:51.809926 run duplicateCorrelation() +#> 2024-07-12 13:32:52.481098 The estimated correlation is: -0.0187869166526901 diff --git a/reference/registration_model.html b/reference/registration_model.html index b917b005..14043780 100644 --- a/reference/registration_model.html +++ b/reference/registration_model.html @@ -21,7 +21,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -148,40 +148,40 @@

Examples

#> #> rgstr_devel > ## Pseudo-bulk #> rgstr_devel > sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:10:00.438993 make pseudobulk object -#> 2024-07-11 14:10:00.529084 drop lowly expressed genes -#> 2024-07-11 14:10:00.555515 normalize expression +#> 2024-07-12 13:32:52.810731 make pseudobulk object +#> 2024-07-12 13:32:52.907024 drop lowly expressed genes +#> 2024-07-12 13:32:52.934378 normalize expression #> #> rgstr_devel > colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 registration_mod <- registration_model(sce_pseudo, "age") -#> 2024-07-11 14:10:00.595729 create model matrix +#> 2024-07-12 13:32:52.978218 create model matrix head(registration_mod) #> registration_variableG0 registration_variableG1 registration_variableG2M #> A_G0 1 0 0 diff --git a/reference/registration_pseudobulk.html b/reference/registration_pseudobulk.html index 00e0926a..1646d28a 100644 --- a/reference/registration_pseudobulk.html +++ b/reference/registration_pseudobulk.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -159,37 +159,37 @@

Examples

## Pseudo-bulk sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:10:00.927949 make pseudobulk object -#> 2024-07-11 14:10:01.01792 drop lowly expressed genes -#> 2024-07-11 14:10:01.044167 normalize expression +#> 2024-07-12 13:32:53.39929 make pseudobulk object +#> 2024-07-12 13:32:53.479816 drop lowly expressed genes +#> 2024-07-12 13:32:53.508184 normalize expression colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 diff --git a/reference/registration_stats_anova.html b/reference/registration_stats_anova.html index 12f8840f..1bff0732 100644 --- a/reference/registration_stats_anova.html +++ b/reference/registration_stats_anova.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -180,41 +180,41 @@

Examples

#> #> rgstr_devel > ## Pseudo-bulk #> rgstr_devel > sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:10:01.525773 make pseudobulk object -#> 2024-07-11 14:10:01.600947 drop lowly expressed genes -#> 2024-07-11 14:10:01.627802 normalize expression +#> 2024-07-12 13:32:53.989431 make pseudobulk object +#> 2024-07-12 13:32:54.086941 drop lowly expressed genes +#> 2024-07-12 13:32:54.113607 normalize expression #> #> rgstr_devel > colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 #> #> rgstr_devel > registration_mod <- registration_model(sce_pseudo, "age") -#> 2024-07-11 14:10:01.66828 create model matrix +#> 2024-07-12 13:32:54.152957 create model matrix #> #> rgstr_devel > head(registration_mod) #> registration_variableG0 registration_variableG1 registration_variableG2M @@ -233,13 +233,13 @@

Examples

#> A_G1 0 19.18719 #> #> rgst__devel > block_cor <- registration_block_cor(sce_pseudo, registration_mod) -#> 2024-07-11 14:10:01.673711 run duplicateCorrelation() -#> 2024-07-11 14:10:02.234059 The estimated correlation is: -0.0187869166526901 +#> 2024-07-12 13:32:54.158299 run duplicateCorrelation() +#> 2024-07-12 13:32:54.691751 The estimated correlation is: -0.0187869166526901 results_anova <- registration_stats_anova(sce_pseudo, block_cor, "age", gene_ensembl = "ensembl", gene_name = "gene_name", suffix = "example" ) -#> 2024-07-11 14:10:02.240467 computing F-statistics +#> 2024-07-12 13:32:54.698011 computing F-statistics head(results_anova) #> f_stat_example p_value_example fdr_example AveExpr_example ensembl gene #> 1 0.1328075 0.93918553 0.9951876 5.499488 ENSG1 gene1 @@ -254,7 +254,7 @@

Examples

block_cor = NaN, "age", gene_ensembl = "ensembl", gene_name = "gene_name", suffix = "example" ) -#> 2024-07-11 14:10:02.270357 computing F-statistics +#> 2024-07-12 13:32:54.723868 computing F-statistics head(results_anova_nan) #> f_stat_example p_value_example fdr_example AveExpr_example ensembl gene #> 1 0.1357180 0.93736053 0.9943765 5.499488 ENSG1 gene1 @@ -276,7 +276,7 @@

Examples

covars = NULL, gene_ensembl = "ensembl", gene_name = "gene_name", suffix = "nocovar" ) -#> 2024-07-11 14:10:02.289762 computing F-statistics +#> 2024-07-12 13:32:54.741926 computing F-statistics head(results_anova_nocovar) #> f_stat_nocovar p_value_nocovar fdr_nocovar AveExpr_nocovar ensembl gene #> 1 0.1393641 0.9351345 0.9962097 5.499488 ENSG1 gene1 @@ -290,20 +290,20 @@

Examples

## 'suffix' values. results_anova_merged <- merge(results_anova, results_anova_nocovar) head(results_anova_merged) -#> ensembl gene f_stat_example p_value_example fdr_example AveExpr_example f_stat_nocovar -#> 1 ENSG1 gene1 0.1328075 0.93918553 0.9951876 5.499488 0.1393641 -#> 2 ENSG10 gene10 1.4570886 0.26147730 0.9945781 2.627385 1.4062441 -#> 3 ENSG100 gene100 3.2217706 0.04889378 0.9945781 2.232185 3.3026378 -#> 4 ENSG1000 gene1000 0.6758112 0.57868578 0.9945781 10.308443 0.5350167 -#> 5 ENSG1001 gene1001 0.7945746 0.51364699 0.9945781 6.472580 0.8090300 -#> 6 ENSG1002 gene1002 0.3648583 0.77922852 0.9945781 10.207492 0.3768459 -#> p_value_nocovar fdr_nocovar AveExpr_nocovar -#> 1 0.93513454 0.9962097 5.499488 -#> 2 0.27343407 0.9940431 2.627385 -#> 3 0.04397329 0.9940431 2.232185 -#> 4 0.66417556 0.9940431 10.308443 -#> 5 0.50527512 0.9940431 6.472580 -#> 6 0.77078373 0.9940431 10.207492 +#> ensembl gene f_stat_example p_value_example fdr_example AveExpr_example +#> 1 ENSG1 gene1 0.1328075 0.93918553 0.9951876 5.499488 +#> 2 ENSG10 gene10 1.4570886 0.26147730 0.9945781 2.627385 +#> 3 ENSG100 gene100 3.2217706 0.04889378 0.9945781 2.232185 +#> 4 ENSG1000 gene1000 0.6758112 0.57868578 0.9945781 10.308443 +#> 5 ENSG1001 gene1001 0.7945746 0.51364699 0.9945781 6.472580 +#> 6 ENSG1002 gene1002 0.3648583 0.77922852 0.9945781 10.207492 +#> f_stat_nocovar p_value_nocovar fdr_nocovar AveExpr_nocovar +#> 1 0.1393641 0.93513454 0.9962097 5.499488 +#> 2 1.4062441 0.27343407 0.9940431 2.627385 +#> 3 3.3026378 0.04397329 0.9940431 2.232185 +#> 4 0.5350167 0.66417556 0.9940431 10.308443 +#> 5 0.8090300 0.50527512 0.9940431 6.472580 +#> 6 0.3768459 0.77078373 0.9940431 10.207492 diff --git a/reference/registration_stats_enrichment.html b/reference/registration_stats_enrichment.html index 1d3eb6b0..efb72e9d 100644 --- a/reference/registration_stats_enrichment.html +++ b/reference/registration_stats_enrichment.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -171,41 +171,41 @@

Examples

#> #> rgstr_devel > ## Pseudo-bulk #> rgstr_devel > sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:10:02.664154 make pseudobulk object -#> 2024-07-11 14:10:02.746224 drop lowly expressed genes -#> 2024-07-11 14:10:02.789414 normalize expression +#> 2024-07-12 13:32:55.209308 make pseudobulk object +#> 2024-07-12 13:32:55.309618 drop lowly expressed genes +#> 2024-07-12 13:32:55.336193 normalize expression #> #> rgstr_devel > colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 #> #> rgstr_devel > registration_mod <- registration_model(sce_pseudo, "age") -#> 2024-07-11 14:10:02.828205 create model matrix +#> 2024-07-12 13:32:55.376402 create model matrix #> #> rgstr_devel > head(registration_mod) #> registration_variableG0 registration_variableG1 registration_variableG2M @@ -224,66 +224,66 @@

Examples

#> A_G1 0 19.18719 #> #> rgst__devel > block_cor <- registration_block_cor(sce_pseudo, registration_mod) -#> 2024-07-11 14:10:02.833594 run duplicateCorrelation() -#> 2024-07-11 14:10:03.367088 The estimated correlation is: -0.0187869166526901 +#> 2024-07-12 13:32:55.381853 run duplicateCorrelation() +#> 2024-07-12 13:32:55.923901 The estimated correlation is: -0.0187869166526901 results_enrichment <- registration_stats_enrichment(sce_pseudo, block_cor, "age", gene_ensembl = "ensembl", gene_name = "gene_name" ) -#> 2024-07-11 14:10:03.371203 computing enrichment statistics -#> 2024-07-11 14:10:03.417578 extract and reformat enrichment results +#> 2024-07-12 13:32:55.926072 computing enrichment statistics +#> 2024-07-12 13:32:55.972651 extract and reformat enrichment results head(results_enrichment) -#> t_stat_G0 t_stat_G1 t_stat_G2M t_stat_S p_value_G0 p_value_G1 p_value_G2M p_value_S -#> Gene_0001 0.1482017 0.5610669 -0.3612235 -0.3458508 0.88374480 0.58130631 0.7219173 0.7332538 -#> Gene_0002 1.1913621 -0.4218015 0.1861521 -0.9362781 0.24817889 0.67790173 0.8542983 0.3608759 -#> Gene_0003 0.3911563 -0.1708744 -1.1308523 0.8936891 0.70003266 0.86612822 0.2721883 0.3826660 -#> Gene_0004 -0.2261922 0.7745193 -0.3413959 -0.1966017 0.82346696 0.44815103 0.7365517 0.8462264 -#> Gene_0005 -2.8506769 0.0763176 1.3501762 1.0489289 0.01022576 0.93996395 0.1928228 0.3073674 -#> Gene_0006 0.6567980 -2.0933725 1.9447842 -0.5422089 0.51918624 0.04995892 0.0667520 0.5939759 -#> fdr_G0 fdr_G1 fdr_G2M fdr_S logFC_G0 logFC_G1 logFC_G2M logFC_S -#> Gene_0001 0.9877448 0.9964804 0.9852245 0.9874654 0.0714226 0.26841193 -0.1735825 -0.1662520 -#> Gene_0002 0.9610612 0.9964804 0.9852245 0.9419020 1.1391547 -0.41614757 0.1843559 -0.9073630 -#> Gene_0003 0.9875616 0.9964804 0.9380488 0.9419020 0.0746434 -0.03279529 -0.2096408 0.1677927 -#> Gene_0004 0.9875616 0.9964804 0.9852245 0.9874654 -0.1509837 0.50974778 -0.2274905 -0.1312736 -#> Gene_0005 0.8705766 0.9964804 0.9327794 0.9354977 -2.2969018 0.07345877 1.2416624 0.9817806 -#> Gene_0006 0.9715681 0.9964804 0.9059695 0.9745520 0.3707513 -1.07744072 1.0138450 -0.3071555 -#> ensembl gene -#> Gene_0001 ENSG1 gene1 -#> Gene_0002 ENSG2 gene2 -#> Gene_0003 ENSG3 gene3 -#> Gene_0004 ENSG4 gene4 -#> Gene_0005 ENSG5 gene5 -#> Gene_0006 ENSG6 gene6 +#> t_stat_G0 t_stat_G1 t_stat_G2M t_stat_S p_value_G0 p_value_G1 +#> Gene_0001 0.1482017 0.5610669 -0.3612235 -0.3458508 0.88374480 0.58130631 +#> Gene_0002 1.1913621 -0.4218015 0.1861521 -0.9362781 0.24817889 0.67790173 +#> Gene_0003 0.3911563 -0.1708744 -1.1308523 0.8936891 0.70003266 0.86612822 +#> Gene_0004 -0.2261922 0.7745193 -0.3413959 -0.1966017 0.82346696 0.44815103 +#> Gene_0005 -2.8506769 0.0763176 1.3501762 1.0489289 0.01022576 0.93996395 +#> Gene_0006 0.6567980 -2.0933725 1.9447842 -0.5422089 0.51918624 0.04995892 +#> p_value_G2M p_value_S fdr_G0 fdr_G1 fdr_G2M fdr_S +#> Gene_0001 0.7219173 0.7332538 0.9877448 0.9964804 0.9852245 0.9874654 +#> Gene_0002 0.8542983 0.3608759 0.9610612 0.9964804 0.9852245 0.9419020 +#> Gene_0003 0.2721883 0.3826660 0.9875616 0.9964804 0.9380488 0.9419020 +#> Gene_0004 0.7365517 0.8462264 0.9875616 0.9964804 0.9852245 0.9874654 +#> Gene_0005 0.1928228 0.3073674 0.8705766 0.9964804 0.9327794 0.9354977 +#> Gene_0006 0.0667520 0.5939759 0.9715681 0.9964804 0.9059695 0.9745520 +#> logFC_G0 logFC_G1 logFC_G2M logFC_S ensembl gene +#> Gene_0001 0.0714226 0.26841193 -0.1735825 -0.1662520 ENSG1 gene1 +#> Gene_0002 1.1391547 -0.41614757 0.1843559 -0.9073630 ENSG2 gene2 +#> Gene_0003 0.0746434 -0.03279529 -0.2096408 0.1677927 ENSG3 gene3 +#> Gene_0004 -0.1509837 0.50974778 -0.2274905 -0.1312736 ENSG4 gene4 +#> Gene_0005 -2.2969018 0.07345877 1.2416624 0.9817806 ENSG5 gene5 +#> Gene_0006 0.3707513 -1.07744072 1.0138450 -0.3071555 ENSG6 gene6 ## Specifying `block_cor = NaN` then ignores the correlation structure results_enrichment_nan <- registration_stats_enrichment(sce_pseudo, block_cor = NaN, "age", gene_ensembl = "ensembl", gene_name = "gene_name" ) -#> 2024-07-11 14:10:03.430109 computing enrichment statistics -#> 2024-07-11 14:10:03.47337 extract and reformat enrichment results +#> 2024-07-12 13:32:55.98448 computing enrichment statistics +#> 2024-07-12 13:32:56.029077 extract and reformat enrichment results head(results_enrichment_nan) -#> t_stat_G0 t_stat_G1 t_stat_G2M t_stat_S p_value_G0 p_value_G1 p_value_G2M p_value_S -#> Gene_0001 0.1497747 0.56711100 -0.3650801 -0.3495404 0.88252049 0.57727506 0.71908334 0.7305269 -#> Gene_0002 1.2045227 -0.42617266 0.1880666 -0.9463404 0.24317510 0.67476888 0.85281819 0.3558526 -#> Gene_0003 0.3947677 -0.17243577 -1.1419417 0.9022243 0.69740991 0.86491760 0.26766387 0.3782304 -#> Gene_0004 -0.2301639 0.78851532 -0.3474114 -0.2000511 0.82042384 0.44011889 0.73210050 0.8435656 -#> Gene_0005 -2.8587641 0.07646923 1.3531129 1.0511322 0.01004623 0.93984490 0.19189934 0.3063803 -#> Gene_0006 0.6606524 -2.10817213 1.9581833 -0.5453678 0.51676445 0.04851534 0.06505398 0.5918443 -#> fdr_G0 fdr_G1 fdr_G2M fdr_S logFC_G0 logFC_G1 logFC_G2M logFC_S -#> Gene_0001 0.9869999 0.9961051 0.9838800 0.9869894 0.0714226 0.26841193 -0.1735825 -0.1662520 -#> Gene_0002 0.9512827 0.9961051 0.9838800 0.9361872 1.1391547 -0.41614757 0.1843559 -0.9073630 -#> Gene_0003 0.9863868 0.9961051 0.9269203 0.9361872 0.0746434 -0.03279529 -0.2096408 0.1677927 -#> Gene_0004 0.9863868 0.9961051 0.9838800 0.9869894 -0.1509837 0.50974778 -0.2274905 -0.1312736 -#> Gene_0005 0.8538025 0.9961051 0.9167840 0.9269610 -2.2969018 0.07345877 1.2416624 0.9817806 -#> Gene_0006 0.9659963 0.9961051 0.8763496 0.9719602 0.3707513 -1.07744072 1.0138450 -0.3071555 -#> ensembl gene -#> Gene_0001 ENSG1 gene1 -#> Gene_0002 ENSG2 gene2 -#> Gene_0003 ENSG3 gene3 -#> Gene_0004 ENSG4 gene4 -#> Gene_0005 ENSG5 gene5 -#> Gene_0006 ENSG6 gene6 +#> t_stat_G0 t_stat_G1 t_stat_G2M t_stat_S p_value_G0 p_value_G1 +#> Gene_0001 0.1497747 0.56711100 -0.3650801 -0.3495404 0.88252049 0.57727506 +#> Gene_0002 1.2045227 -0.42617266 0.1880666 -0.9463404 0.24317510 0.67476888 +#> Gene_0003 0.3947677 -0.17243577 -1.1419417 0.9022243 0.69740991 0.86491760 +#> Gene_0004 -0.2301639 0.78851532 -0.3474114 -0.2000511 0.82042384 0.44011889 +#> Gene_0005 -2.8587641 0.07646923 1.3531129 1.0511322 0.01004623 0.93984490 +#> Gene_0006 0.6606524 -2.10817213 1.9581833 -0.5453678 0.51676445 0.04851534 +#> p_value_G2M p_value_S fdr_G0 fdr_G1 fdr_G2M fdr_S +#> Gene_0001 0.71908334 0.7305269 0.9869999 0.9961051 0.9838800 0.9869894 +#> Gene_0002 0.85281819 0.3558526 0.9512827 0.9961051 0.9838800 0.9361872 +#> Gene_0003 0.26766387 0.3782304 0.9863868 0.9961051 0.9269203 0.9361872 +#> Gene_0004 0.73210050 0.8435656 0.9863868 0.9961051 0.9838800 0.9869894 +#> Gene_0005 0.19189934 0.3063803 0.8538025 0.9961051 0.9167840 0.9269610 +#> Gene_0006 0.06505398 0.5918443 0.9659963 0.9961051 0.8763496 0.9719602 +#> logFC_G0 logFC_G1 logFC_G2M logFC_S ensembl gene +#> Gene_0001 0.0714226 0.26841193 -0.1735825 -0.1662520 ENSG1 gene1 +#> Gene_0002 1.1391547 -0.41614757 0.1843559 -0.9073630 ENSG2 gene2 +#> Gene_0003 0.0746434 -0.03279529 -0.2096408 0.1677927 ENSG3 gene3 +#> Gene_0004 -0.1509837 0.50974778 -0.2274905 -0.1312736 ENSG4 gene4 +#> Gene_0005 -2.2969018 0.07345877 1.2416624 0.9817806 ENSG5 gene5 +#> Gene_0006 0.3707513 -1.07744072 1.0138450 -0.3071555 ENSG6 gene6 diff --git a/reference/registration_stats_pairwise.html b/reference/registration_stats_pairwise.html index 7ffa165f..aa3ed52d 100644 --- a/reference/registration_stats_pairwise.html +++ b/reference/registration_stats_pairwise.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -173,41 +173,41 @@

Examples

#> #> rgstr_devel > ## Pseudo-bulk #> rgstr_devel > sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL) -#> 2024-07-11 14:10:03.843229 make pseudobulk object -#> 2024-07-11 14:10:03.919931 drop lowly expressed genes -#> 2024-07-11 14:10:03.947153 normalize expression +#> 2024-07-12 13:32:56.403609 make pseudobulk object +#> 2024-07-12 13:32:56.499578 drop lowly expressed genes +#> 2024-07-12 13:32:56.526557 normalize expression #> #> rgstr_devel > colData(sce_pseudo) #> DataFrame with 20 rows and 8 columns -#> Mutation_Status Cell_Cycle Treatment sample_id age registration_variable -#> <character> <character> <character> <character> <numeric> <character> -#> A_G0 NA G0 NA A 19.1872 G0 -#> B_G0 NA G0 NA B 25.3496 G0 -#> C_G0 NA G0 NA C 24.1802 G0 -#> D_G0 NA G0 NA D 15.5211 G0 -#> E_G0 NA G0 NA E 20.9701 G0 -#> ... ... ... ... ... ... ... -#> A_S NA S NA A 19.1872 S -#> B_S NA S NA B 25.3496 S -#> C_S NA S NA C 24.1802 S -#> D_S NA S NA D 15.5211 S -#> E_S NA S NA E 20.9701 S -#> registration_sample_id ncells -#> <character> <integer> -#> A_G0 A 8 -#> B_G0 B 13 -#> C_G0 C 9 -#> D_G0 D 7 -#> E_G0 E 10 -#> ... ... ... -#> A_S A 12 -#> B_S B 8 -#> C_S C 7 -#> D_S D 14 -#> E_S E 11 +#> Mutation_Status Cell_Cycle Treatment sample_id age +#> <character> <character> <character> <character> <numeric> +#> A_G0 NA G0 NA A 19.1872 +#> B_G0 NA G0 NA B 25.3496 +#> C_G0 NA G0 NA C 24.1802 +#> D_G0 NA G0 NA D 15.5211 +#> E_G0 NA G0 NA E 20.9701 +#> ... ... ... ... ... ... +#> A_S NA S NA A 19.1872 +#> B_S NA S NA B 25.3496 +#> C_S NA S NA C 24.1802 +#> D_S NA S NA D 15.5211 +#> E_S NA S NA E 20.9701 +#> registration_variable registration_sample_id ncells +#> <character> <character> <integer> +#> A_G0 G0 A 8 +#> B_G0 G0 B 13 +#> C_G0 G0 C 9 +#> D_G0 G0 D 7 +#> E_G0 G0 E 10 +#> ... ... ... ... +#> A_S S A 12 +#> B_S S B 8 +#> C_S S C 7 +#> D_S S D 14 +#> E_S S E 11 #> #> rgstr_devel > registration_mod <- registration_model(sce_pseudo, "age") -#> 2024-07-11 14:10:03.986249 create model matrix +#> 2024-07-12 13:32:56.567119 create model matrix #> #> rgstr_devel > head(registration_mod) #> registration_variableG0 registration_variableG1 registration_variableG2M @@ -226,43 +226,50 @@

Examples

#> A_G1 0 19.18719 #> #> rgst__devel > block_cor <- registration_block_cor(sce_pseudo, registration_mod) -#> 2024-07-11 14:10:03.991736 run duplicateCorrelation() -#> 2024-07-11 14:10:04.534352 The estimated correlation is: -0.0187869166526901 +#> 2024-07-12 13:32:56.572642 run duplicateCorrelation() +#> 2024-07-12 13:32:57.125927 The estimated correlation is: -0.0187869166526901 results_pairwise <- registration_stats_pairwise(sce_pseudo, registration_mod, block_cor, gene_ensembl = "ensembl", gene_name = "gene_name" ) -#> 2024-07-11 14:10:04.535834 running the baseline pairwise model -#> 2024-07-11 14:10:04.543764 computing pairwise statistics +#> 2024-07-12 13:32:57.127193 running the baseline pairwise model +#> 2024-07-12 13:32:57.135122 computing pairwise statistics head(results_pairwise) -#> t_stat_G0-G1 t_stat_G0-G2M t_stat_G0-S t_stat_G1-G2M t_stat_G1-S t_stat_G2M-S -#> Gene_0001 -0.2393683 0.29771391 0.28880637 0.5370822 0.5281747 -0.008907535 -#> Gene_0002 0.9547055 0.58609293 1.25623276 -0.3686126 0.3015273 0.670139826 -#> Gene_0003 0.3389469 0.89685781 -0.29386667 0.5579109 -0.6328135 -1.190724479 -#> Gene_0004 -0.5817335 0.06735952 -0.01735353 0.6490930 0.5643800 -0.084713052 -#> Gene_0005 -1.7445717 -2.60436287 -2.41309134 -0.8597912 -0.6685196 0.191271534 -#> Gene_0006 1.7476962 -0.77609353 0.81810637 -2.5237898 -0.9295898 1.594199902 -#> p_value_G0-G1 p_value_G0-G2M p_value_G0-S p_value_G1-G2M p_value_G1-S p_value_G2M-S -#> Gene_0001 0.81368047 0.76952707 0.77621965 0.59816691 0.6042013 0.9929965 -#> Gene_0002 0.35309875 0.56550988 0.22601193 0.71696428 0.7666676 0.5117648 -#> Gene_0003 0.73879812 0.38231306 0.77241539 0.58417423 0.5352709 0.2501141 -#> Gene_0004 0.56837654 0.94708094 0.98635651 0.52494641 0.5798624 0.9334786 -#> Gene_0005 0.09909959 0.01850622 0.02738372 0.40185996 0.5127728 0.8505774 -#> Gene_0006 0.09854299 0.44835125 0.42460931 0.02184936 0.3655890 0.1293024 -#> fdr_G0-G1 fdr_G0-G2M fdr_G0-S fdr_G1-G2M fdr_G1-S fdr_G2M-S logFC_G0-G1 logFC_G0-G2M -#> Gene_0001 0.9971936 0.9833020 0.9800365 0.9949522 0.995513 0.9998989 -0.14774200 0.18375383 -#> Gene_0002 0.9971936 0.9612205 0.9224977 0.9949522 0.995513 0.9727830 1.16647669 0.71609911 -#> Gene_0003 0.9971936 0.9493926 0.9783602 0.9949522 0.995513 0.9392457 0.08057902 0.21321313 -#> Gene_0004 0.9971936 0.9958273 0.9956820 0.9949522 0.995513 0.9987686 -0.49554861 0.05738008 -#> Gene_0005 0.9971936 0.7217980 0.8946073 0.9949522 0.995513 0.9882374 -1.77777042 -2.65392316 -#> Gene_0006 0.9971936 0.9561207 0.9698559 0.9949522 0.995513 0.9374452 1.08614399 -0.48232028 -#> logFC_G0-S logFC_G1-G2M logFC_G1-S logFC_G2M-S ensembl gene -#> Gene_0001 0.17825596 0.3314958 0.3259980 -0.005497874 ENSG1 gene1 -#> Gene_0002 1.53488825 -0.4503776 0.3684116 0.818789145 ENSG2 gene2 -#> Gene_0003 -0.06986195 0.1326341 -0.1504410 -0.283075078 ENSG3 gene3 -#> Gene_0004 -0.01478258 0.5529287 0.4807660 -0.072162656 ENSG4 gene4 -#> Gene_0005 -2.45901178 -0.8761527 -0.6812414 0.194911377 ENSG5 gene5 -#> Gene_0006 0.50843007 -1.5684643 -0.5777139 0.990750347 ENSG6 gene6 +#> t_stat_G0-G1 t_stat_G0-G2M t_stat_G0-S t_stat_G1-G2M t_stat_G1-S +#> Gene_0001 -0.2393683 0.29771391 0.28880637 0.5370822 0.5281747 +#> Gene_0002 0.9547055 0.58609293 1.25623276 -0.3686126 0.3015273 +#> Gene_0003 0.3389469 0.89685781 -0.29386667 0.5579109 -0.6328135 +#> Gene_0004 -0.5817335 0.06735952 -0.01735353 0.6490930 0.5643800 +#> Gene_0005 -1.7445717 -2.60436287 -2.41309134 -0.8597912 -0.6685196 +#> Gene_0006 1.7476962 -0.77609353 0.81810637 -2.5237898 -0.9295898 +#> t_stat_G2M-S p_value_G0-G1 p_value_G0-G2M p_value_G0-S p_value_G1-G2M +#> Gene_0001 -0.008907535 0.81368047 0.76952707 0.77621965 0.59816691 +#> Gene_0002 0.670139826 0.35309875 0.56550988 0.22601193 0.71696428 +#> Gene_0003 -1.190724479 0.73879812 0.38231306 0.77241539 0.58417423 +#> Gene_0004 -0.084713052 0.56837654 0.94708094 0.98635651 0.52494641 +#> Gene_0005 0.191271534 0.09909959 0.01850622 0.02738372 0.40185996 +#> Gene_0006 1.594199902 0.09854299 0.44835125 0.42460931 0.02184936 +#> p_value_G1-S p_value_G2M-S fdr_G0-G1 fdr_G0-G2M fdr_G0-S fdr_G1-G2M +#> Gene_0001 0.6042013 0.9929965 0.9971936 0.9833020 0.9800365 0.9949522 +#> Gene_0002 0.7666676 0.5117648 0.9971936 0.9612205 0.9224977 0.9949522 +#> Gene_0003 0.5352709 0.2501141 0.9971936 0.9493926 0.9783602 0.9949522 +#> Gene_0004 0.5798624 0.9334786 0.9971936 0.9958273 0.9956820 0.9949522 +#> Gene_0005 0.5127728 0.8505774 0.9971936 0.7217980 0.8946073 0.9949522 +#> Gene_0006 0.3655890 0.1293024 0.9971936 0.9561207 0.9698559 0.9949522 +#> fdr_G1-S fdr_G2M-S logFC_G0-G1 logFC_G0-G2M logFC_G0-S logFC_G1-G2M +#> Gene_0001 0.995513 0.9998989 -0.14774200 0.18375383 0.17825596 0.3314958 +#> Gene_0002 0.995513 0.9727830 1.16647669 0.71609911 1.53488825 -0.4503776 +#> Gene_0003 0.995513 0.9392457 0.08057902 0.21321313 -0.06986195 0.1326341 +#> Gene_0004 0.995513 0.9987686 -0.49554861 0.05738008 -0.01478258 0.5529287 +#> Gene_0005 0.995513 0.9882374 -1.77777042 -2.65392316 -2.45901178 -0.8761527 +#> Gene_0006 0.995513 0.9374452 1.08614399 -0.48232028 0.50843007 -1.5684643 +#> logFC_G1-S logFC_G2M-S ensembl gene +#> Gene_0001 0.3259980 -0.005497874 ENSG1 gene1 +#> Gene_0002 0.3684116 0.818789145 ENSG2 gene2 +#> Gene_0003 -0.1504410 -0.283075078 ENSG3 gene3 +#> Gene_0004 0.4807660 -0.072162656 ENSG4 gene4 +#> Gene_0005 -0.6812414 0.194911377 ENSG5 gene5 +#> Gene_0006 -0.5777139 0.990750347 ENSG6 gene6 ## Specifying `block_cor = NaN` then ignores the correlation structure results_pairwise_nan <- registration_stats_pairwise(sce_pseudo, @@ -270,37 +277,44 @@

Examples

block_cor = NaN, gene_ensembl = "ensembl", gene_name = "gene_name" ) -#> 2024-07-11 14:10:04.567189 running the baseline pairwise model -#> 2024-07-11 14:10:04.57407 computing pairwise statistics +#> 2024-07-12 13:32:57.157114 running the baseline pairwise model +#> 2024-07-12 13:32:57.164119 computing pairwise statistics head(results_pairwise_nan) -#> t_stat_G0-G1 t_stat_G0-G2M t_stat_G0-S t_stat_G1-G2M t_stat_G1-S t_stat_G2M-S -#> Gene_0001 -0.2419770 0.30095840 0.29195379 0.5429354 0.5339308 -0.009004609 -#> Gene_0002 0.9655460 0.59274792 1.27049707 -0.3727981 0.3049511 0.677749150 -#> Gene_0003 0.3424456 0.90611560 -0.29690010 0.5636700 -0.6393457 -1.203015701 -#> Gene_0004 -0.5922676 0.06857927 -0.01766777 0.6608469 0.5745998 -0.086247043 -#> Gene_0005 -1.7497882 -2.61215029 -2.42030683 -0.8623621 -0.6705186 0.191843463 -#> Gene_0006 1.7620062 -0.78244810 0.82480493 -2.5444543 -0.9372012 1.607253036 -#> p_value_G0-G1 p_value_G0-G2M p_value_G0-S p_value_G1-G2M p_value_G1-S p_value_G2M-S -#> Gene_0001 0.81169124 0.76709393 0.77385274 0.59421797 0.6002984 0.9929202 -#> Gene_0002 0.34779939 0.56114829 0.22101069 0.71390337 0.7641032 0.5070458 -#> Gene_0003 0.73621058 0.37753160 0.77013771 0.58033474 0.5311147 0.2454479 -#> Gene_0004 0.56146250 0.94612425 0.98610948 0.51756162 0.5730837 0.9322771 -#> Gene_0005 0.09817164 0.01821015 0.02698596 0.40048330 0.5115293 0.8501363 -#> Gene_0006 0.09602824 0.44470810 0.42089899 0.02094098 0.3617722 0.1264013 -#> fdr_G0-G1 fdr_G0-G2M fdr_G0-S fdr_G1-G2M fdr_G1-S fdr_G2M-S logFC_G0-G1 logFC_G0-G2M -#> Gene_0001 0.9971067 0.9806277 0.9782050 0.9932537 0.9942711 0.9998953 -0.14774200 0.18375383 -#> Gene_0002 0.9971067 0.9557062 0.9058626 0.9932537 0.9942711 0.9658549 1.16647669 0.71609911 -#> Gene_0003 0.9971067 0.9380457 0.9754753 0.9932537 0.9942711 0.9234394 0.08057902 0.21321313 -#> Gene_0004 0.9971067 0.9957322 0.9955379 0.9932537 0.9942711 0.9978377 -0.49554861 0.05738008 -#> Gene_0005 0.9971067 0.6854563 0.8734187 0.9932537 0.9942711 0.9864989 -1.77777042 -2.65392316 -#> Gene_0006 0.9971067 0.9508593 0.9618345 0.9932537 0.9942711 0.9218958 1.08614399 -0.48232028 -#> logFC_G0-S logFC_G1-G2M logFC_G1-S logFC_G2M-S ensembl gene -#> Gene_0001 0.17825596 0.3314958 0.3259980 -0.005497874 ENSG1 gene1 -#> Gene_0002 1.53488825 -0.4503776 0.3684116 0.818789145 ENSG2 gene2 -#> Gene_0003 -0.06986195 0.1326341 -0.1504410 -0.283075078 ENSG3 gene3 -#> Gene_0004 -0.01478258 0.5529287 0.4807660 -0.072162656 ENSG4 gene4 -#> Gene_0005 -2.45901178 -0.8761527 -0.6812414 0.194911377 ENSG5 gene5 -#> Gene_0006 0.50843007 -1.5684643 -0.5777139 0.990750347 ENSG6 gene6 +#> t_stat_G0-G1 t_stat_G0-G2M t_stat_G0-S t_stat_G1-G2M t_stat_G1-S +#> Gene_0001 -0.2419770 0.30095840 0.29195379 0.5429354 0.5339308 +#> Gene_0002 0.9655460 0.59274792 1.27049707 -0.3727981 0.3049511 +#> Gene_0003 0.3424456 0.90611560 -0.29690010 0.5636700 -0.6393457 +#> Gene_0004 -0.5922676 0.06857927 -0.01766777 0.6608469 0.5745998 +#> Gene_0005 -1.7497882 -2.61215029 -2.42030683 -0.8623621 -0.6705186 +#> Gene_0006 1.7620062 -0.78244810 0.82480493 -2.5444543 -0.9372012 +#> t_stat_G2M-S p_value_G0-G1 p_value_G0-G2M p_value_G0-S p_value_G1-G2M +#> Gene_0001 -0.009004609 0.81169124 0.76709393 0.77385274 0.59421797 +#> Gene_0002 0.677749150 0.34779939 0.56114829 0.22101069 0.71390337 +#> Gene_0003 -1.203015701 0.73621058 0.37753160 0.77013771 0.58033474 +#> Gene_0004 -0.086247043 0.56146250 0.94612425 0.98610948 0.51756162 +#> Gene_0005 0.191843463 0.09817164 0.01821015 0.02698596 0.40048330 +#> Gene_0006 1.607253036 0.09602824 0.44470810 0.42089899 0.02094098 +#> p_value_G1-S p_value_G2M-S fdr_G0-G1 fdr_G0-G2M fdr_G0-S fdr_G1-G2M +#> Gene_0001 0.6002984 0.9929202 0.9971067 0.9806277 0.9782050 0.9932537 +#> Gene_0002 0.7641032 0.5070458 0.9971067 0.9557062 0.9058626 0.9932537 +#> Gene_0003 0.5311147 0.2454479 0.9971067 0.9380457 0.9754753 0.9932537 +#> Gene_0004 0.5730837 0.9322771 0.9971067 0.9957322 0.9955379 0.9932537 +#> Gene_0005 0.5115293 0.8501363 0.9971067 0.6854563 0.8734187 0.9932537 +#> Gene_0006 0.3617722 0.1264013 0.9971067 0.9508593 0.9618345 0.9932537 +#> fdr_G1-S fdr_G2M-S logFC_G0-G1 logFC_G0-G2M logFC_G0-S logFC_G1-G2M +#> Gene_0001 0.9942711 0.9998953 -0.14774200 0.18375383 0.17825596 0.3314958 +#> Gene_0002 0.9942711 0.9658549 1.16647669 0.71609911 1.53488825 -0.4503776 +#> Gene_0003 0.9942711 0.9234394 0.08057902 0.21321313 -0.06986195 0.1326341 +#> Gene_0004 0.9942711 0.9978377 -0.49554861 0.05738008 -0.01478258 0.5529287 +#> Gene_0005 0.9942711 0.9864989 -1.77777042 -2.65392316 -2.45901178 -0.8761527 +#> Gene_0006 0.9942711 0.9218958 1.08614399 -0.48232028 0.50843007 -1.5684643 +#> logFC_G1-S logFC_G2M-S ensembl gene +#> Gene_0001 0.3259980 -0.005497874 ENSG1 gene1 +#> Gene_0002 0.3684116 0.818789145 ENSG2 gene2 +#> Gene_0003 -0.1504410 -0.283075078 ENSG3 gene3 +#> Gene_0004 0.4807660 -0.072162656 ENSG4 gene4 +#> Gene_0005 -0.6812414 0.194911377 ENSG5 gene5 +#> Gene_0006 -0.5777139 0.990750347 ENSG6 gene6 diff --git a/reference/registration_wrapper.html b/reference/registration_wrapper.html index ef6e0aa1..5604a818 100644 --- a/reference/registration_wrapper.html +++ b/reference/registration_wrapper.html @@ -22,7 +22,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -200,18 +200,18 @@

Examples

sce, "Cell_Cycle", "sample_id", c("age"), "ensembl", "gene_name", "wrapper" ) -#> 2024-07-11 14:10:04.965708 make pseudobulk object -#> 2024-07-11 14:10:05.038867 dropping 9 pseudo-bulked samples that are below 'min_ncells'. -#> 2024-07-11 14:10:05.049523 drop lowly expressed genes -#> 2024-07-11 14:10:05.075818 normalize expression -#> 2024-07-11 14:10:05.105944 create model matrix -#> 2024-07-11 14:10:05.110871 run duplicateCorrelation() -#> 2024-07-11 14:10:06.1212 The estimated correlation is: -0.0783081238514527 -#> 2024-07-11 14:10:06.122383 computing enrichment statistics -#> 2024-07-11 14:10:06.165556 extract and reformat enrichment results -#> 2024-07-11 14:10:06.174678 running the baseline pairwise model -#> 2024-07-11 14:10:06.181645 computing pairwise statistics -#> 2024-07-11 14:10:06.205885 computing F-statistics +#> 2024-07-12 13:32:57.528348 make pseudobulk object +#> 2024-07-12 13:32:57.653101 dropping 9 pseudo-bulked samples that are below 'min_ncells'. +#> 2024-07-12 13:32:57.664283 drop lowly expressed genes +#> 2024-07-12 13:32:57.691477 normalize expression +#> 2024-07-12 13:32:57.72175 create model matrix +#> 2024-07-12 13:32:57.726874 run duplicateCorrelation() +#> 2024-07-12 13:32:58.746871 The estimated correlation is: -0.0783081238514527 +#> 2024-07-12 13:32:58.748092 computing enrichment statistics +#> 2024-07-12 13:32:58.791619 extract and reformat enrichment results +#> 2024-07-12 13:32:58.800281 running the baseline pairwise model +#> 2024-07-12 13:32:58.807097 computing pairwise statistics +#> 2024-07-12 13:32:58.83143 computing F-statistics diff --git a/reference/run_app.html b/reference/run_app.html index 1eedb133..3e9e2520 100644 --- a/reference/run_app.html +++ b/reference/run_app.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/sce_to_spe.html b/reference/sce_to_spe.html index b2c6eb92..ddbd2407 100644 --- a/reference/sce_to_spe.html +++ b/reference/sce_to_spe.html @@ -21,7 +21,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -128,7 +128,7 @@

Examples

spe <- sce_to_spe(sce) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:08.081284 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:00.728184 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/sig_genes_extract.html b/reference/sig_genes_extract.html index 87aee165..d3808a18 100644 --- a/reference/sig_genes_extract.html +++ b/reference/sig_genes_extract.html @@ -21,7 +21,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -157,37 +157,58 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:17.848291 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:10.249093 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer") #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:18.468371 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:10.92515 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 ## anova top 10 genes sig_genes_extract( modeling_results = modeling_results, sce_layer = sce_layer ) -#> top model_type test gene stat pval fdr gene_index ensembl -#> 1 1 anova full KRT17 142.6223 1.047345e-37 2.338827e-33 18321 ENSG00000128422 -#> 2 2 anova full SERPINE2 130.0159 2.154786e-36 2.405926e-32 3621 ENSG00000135919 -#> 3 3 anova full GFAP 127.3117 4.267922e-36 3.176899e-32 18436 ENSG00000131095 -#> 4 4 anova full CBLN4 118.0481 4.923904e-35 2.748892e-31 19844 ENSG00000054803 -#> 5 5 anova full VSTM2A 111.0341 3.530274e-34 1.576691e-30 8315 ENSG00000170419 -#> 6 6 anova full CAMK2N1 108.9155 6.542909e-34 2.435162e-30 308 ENSG00000162545 -#> 7 7 anova full HS3ST4 104.9437 2.142606e-33 6.049917e-30 16991 ENSG00000182601 -#> 8 8 anova full MYRF 104.9059 2.167361e-33 6.049917e-30 11871 ENSG00000124920 -#> 9 9 anova full BCAS1 103.3046 3.537055e-33 8.776220e-30 19839 ENSG00000064787 -#> 10 10 anova full MOBP 100.9366 7.389973e-33 1.650255e-29 3978 ENSG00000168314 -#> 11 1 anova noWM SERPINE2 209.6240 9.525498e-37 2.127139e-32 3621 ENSG00000135919 -#> 12 2 anova noWM B3GALT2 181.2393 5.843659e-35 4.749316e-31 1809 ENSG00000162630 -#> 13 3 anova noWM KRT17 180.6747 6.380345e-35 4.749316e-31 18321 ENSG00000128422 -#> 14 4 anova noWM EFHD2 163.2460 1.099248e-33 5.247556e-30 232 ENSG00000142634 -#> 15 5 anova noWM CAMK2N1 162.8576 1.174949e-33 5.247556e-30 308 ENSG00000162545 -#> 16 6 anova noWM HOPX 157.1804 3.164231e-33 1.177674e-29 5291 ENSG00000171476 -#> 17 7 anova noWM CLSTN2 148.4278 1.558137e-32 4.970680e-29 4638 ENSG00000158258 -#> 18 8 anova noWM TUBA1B 135.6197 1.890890e-31 4.918869e-28 13893 ENSG00000123416 -#> 19 9 anova noWM HS3ST2 135.3872 1.982438e-31 4.918869e-28 16964 ENSG00000122254 -#> 20 10 anova noWM ETV1 130.0174 6.033934e-31 1.347438e-27 8095 ENSG00000006468 +#> top model_type test gene stat pval fdr gene_index +#> 1 1 anova full KRT17 142.6223 1.047345e-37 2.338827e-33 18321 +#> 2 2 anova full SERPINE2 130.0159 2.154786e-36 2.405926e-32 3621 +#> 3 3 anova full GFAP 127.3117 4.267922e-36 3.176899e-32 18436 +#> 4 4 anova full CBLN4 118.0481 4.923904e-35 2.748892e-31 19844 +#> 5 5 anova full VSTM2A 111.0341 3.530274e-34 1.576691e-30 8315 +#> 6 6 anova full CAMK2N1 108.9155 6.542909e-34 2.435162e-30 308 +#> 7 7 anova full HS3ST4 104.9437 2.142606e-33 6.049917e-30 16991 +#> 8 8 anova full MYRF 104.9059 2.167361e-33 6.049917e-30 11871 +#> 9 9 anova full BCAS1 103.3046 3.537055e-33 8.776220e-30 19839 +#> 10 10 anova full MOBP 100.9366 7.389973e-33 1.650255e-29 3978 +#> 11 1 anova noWM SERPINE2 209.6240 9.525498e-37 2.127139e-32 3621 +#> 12 2 anova noWM B3GALT2 181.2393 5.843659e-35 4.749316e-31 1809 +#> 13 3 anova noWM KRT17 180.6747 6.380345e-35 4.749316e-31 18321 +#> 14 4 anova noWM EFHD2 163.2460 1.099248e-33 5.247556e-30 232 +#> 15 5 anova noWM CAMK2N1 162.8576 1.174949e-33 5.247556e-30 308 +#> 16 6 anova noWM HOPX 157.1804 3.164231e-33 1.177674e-29 5291 +#> 17 7 anova noWM CLSTN2 148.4278 1.558137e-32 4.970680e-29 4638 +#> 18 8 anova noWM TUBA1B 135.6197 1.890890e-31 4.918869e-28 13893 +#> 19 9 anova noWM HS3ST2 135.3872 1.982438e-31 4.918869e-28 16964 +#> 20 10 anova noWM ETV1 130.0174 6.033934e-31 1.347438e-27 8095 +#> ensembl +#> 1 ENSG00000128422 +#> 2 ENSG00000135919 +#> 3 ENSG00000131095 +#> 4 ENSG00000054803 +#> 5 ENSG00000170419 +#> 6 ENSG00000162545 +#> 7 ENSG00000182601 +#> 8 ENSG00000124920 +#> 9 ENSG00000064787 +#> 10 ENSG00000168314 +#> 11 ENSG00000135919 +#> 12 ENSG00000162630 +#> 13 ENSG00000128422 +#> 14 ENSG00000142634 +#> 15 ENSG00000162545 +#> 16 ENSG00000171476 +#> 17 ENSG00000158258 +#> 18 ENSG00000123416 +#> 19 ENSG00000122254 +#> 20 ENSG00000006468 ## Extract all genes sig_genes_extract( @@ -195,118 +216,230 @@

Examples

sce_layer = sce_layer, n = nrow(sce_layer) ) -#> top model_type test gene stat pval fdr gene_index ensembl -#> 1 1 anova full KRT17 142.62226 1.047345e-37 2.338827e-33 18321 ENSG00000128422 -#> 2 2 anova full SERPINE2 130.01590 2.154786e-36 2.405926e-32 3621 ENSG00000135919 -#> 3 3 anova full GFAP 127.31169 4.267922e-36 3.176899e-32 18436 ENSG00000131095 -#> 4 4 anova full CBLN4 118.04809 4.923904e-35 2.748892e-31 19844 ENSG00000054803 -#> 5 5 anova full VSTM2A 111.03407 3.530274e-34 1.576691e-30 8315 ENSG00000170419 -#> 6 6 anova full CAMK2N1 108.91547 6.542909e-34 2.435162e-30 308 ENSG00000162545 -#> 7 7 anova full HS3ST4 104.94373 2.142606e-33 6.049917e-30 16991 ENSG00000182601 -#> 8 8 anova full MYRF 104.90591 2.167361e-33 6.049917e-30 11871 ENSG00000124920 -#> 9 9 anova full BCAS1 103.30459 3.537055e-33 8.776220e-30 19839 ENSG00000064787 -#> 10 10 anova full MOBP 100.93656 7.389973e-33 1.650255e-29 3978 ENSG00000168314 -#> 11 11 anova full EFHD2 95.11926 4.830189e-32 9.805724e-29 232 ENSG00000142634 -#> 12 12 anova full MBP 93.31013 8.841183e-32 1.645271e-28 19352 ENSG00000197971 -#> 13 13 anova full PLP1 90.81919 2.067407e-31 3.551327e-28 9433 ENSG00000123560 -#> 14 14 anova full TLE4 89.57378 3.185778e-31 5.081544e-28 10842 ENSG00000106829 -#> 15 15 anova full SCN1B 89.29950 3.506551e-31 5.220320e-28 20704 ENSG00000105711 -#> 16 16 anova full HS3ST2 88.80268 4.174799e-31 5.826714e-28 16964 ENSG00000122254 -#> 17 17 anova full HPCAL1 87.92038 5.702797e-31 7.491127e-28 2313 ENSG00000115756 -#> 18 18 anova full FRMPD2 87.32346 7.053483e-31 8.394075e-28 12882 ENSG00000170324 -#> 19 19 anova full HOPX 87.28856 7.141974e-31 8.394075e-28 5291 ENSG00000171476 -#> 20 20 anova full CTGF 86.88731 8.245258e-31 9.206242e-28 7750 ENSG00000118523 -#> 21 21 anova full CARTPT 86.45410 9.634771e-31 1.024543e-27 6121 ENSG00000164326 -#> 22 22 anova full MAG 85.73052 1.251635e-30 1.242947e-27 20716 ENSG00000105695 -#> 23 23 anova full MT1G 85.49982 1.361082e-30 1.242947e-27 17246 ENSG00000125144 -#> 24 24 anova full LAMP5 85.49858 1.361696e-30 1.242947e-27 19480 ENSG00000125869 -#> 25 25 anova full PARM1 85.43907 1.391504e-30 1.242947e-27 5344 ENSG00000169116 -#> 26 26 anova full CNR1 82.78401 3.706832e-30 3.183741e-27 7564 ENSG00000118432 -#> 27 27 anova full SNCG 81.49962 6.013272e-30 4.973422e-27 13092 ENSG00000173267 -#> 28 28 anova full CLDND1 79.53535 1.276556e-29 1.018099e-26 4350 ENSG00000080822 -#> 29 29 anova full OPALIN 79.24022 1.431399e-29 1.102226e-26 13171 ENSG00000197430 -#> 30 30 anova full NECAB2 78.87941 1.647264e-29 1.226169e-26 17538 ENSG00000103154 -#> 31 31 anova full RGS12 78.67761 1.782330e-29 1.283910e-26 5059 ENSG00000159788 -#> 32 32 anova full RORB 77.71670 2.600133e-29 1.814486e-26 10825 ENSG00000198963 -#> 33 33 anova full CRYM 75.38463 6.612801e-29 4.474863e-26 16941 ENSG00000103316 -#> 34 34 anova full TF 74.48048 9.559810e-29 6.278827e-26 4598 ENSG00000091513 -#> 35 35 anova full SEPT4 74.28070 1.037625e-28 6.620342e-26 18627 ENSG00000108387 -#> 36 36 anova full MAL 73.88124 1.223066e-28 7.392804e-26 2851 ENSG00000172005 -#> 37 37 anova full TUBA1B 73.87759 1.224906e-28 7.392804e-26 13893 ENSG00000123416 -#> 38 38 anova full LPAR1 73.75946 1.286128e-28 7.558034e-26 11021 ENSG00000198121 -#> 39 39 anova full ERMN 73.66065 1.339754e-28 7.671295e-26 3181 ENSG00000136541 -#> 40 40 anova full EVI2A 72.84319 1.881777e-28 1.025818e-25 18141 ENSG00000126860 -#> 41 41 anova full B3GALT2 72.84110 1.883415e-28 1.025818e-25 1809 ENSG00000162630 -#> 42 42 anova full ERBB3 72.33845 2.324733e-28 1.236038e-25 14064 ENSG00000065361 -#> 43 43 anova full PVALB 72.26690 2.395696e-28 1.244146e-25 21800 ENSG00000100362 -#> 44 44 anova full SOWAHA 70.87215 4.327060e-28 2.196081e-25 6395 ENSG00000198944 -#> 45 45 anova full PPP1R14A 70.24044 5.674096e-28 2.765105e-25 20817 ENSG00000167641 -#> 46 46 anova full OLFM1 70.23154 5.695886e-28 2.765105e-25 11296 ENSG00000130558 -#> 47 47 anova full CLSTN2 70.07161 6.102471e-28 2.899453e-25 4638 ENSG00000158258 -#> 48 48 anova full SHB 69.98933 6.323153e-28 2.941715e-25 10755 ENSG00000107338 -#> 49 49 anova full SMYD2 69.81673 6.813144e-28 3.104986e-25 1994 ENSG00000143499 -#> 50 50 anova full CLDN11 69.69652 7.177327e-28 3.205538e-25 4790 ENSG00000013297 -#> 51 51 anova full CNDP1 69.23235 8.782105e-28 3.845356e-25 19335 ENSG00000150656 -#> 52 52 anova full SNCB 68.96081 9.887700e-28 4.246197e-25 6771 ENSG00000074317 -#> 53 53 anova full NAP1L5 68.85738 1.034557e-27 4.358997e-25 5434 ENSG00000177432 -#> 54 54 anova full TPPP3 68.53788 1.190263e-27 4.922180e-25 17341 ENSG00000159713 -#> 55 55 anova full PDZD2 68.33889 1.299236e-27 5.275134e-25 5932 ENSG00000133401 -#> 56 56 anova full MOG 68.06957 1.463276e-27 5.835073e-25 7082 ENSG00000204655 -#> 57 57 anova full S1PR5 67.98191 1.521144e-27 5.959415e-25 20298 ENSG00000180739 -#> 58 58 anova full CNP 67.60795 1.795712e-27 6.913801e-25 18334 ENSG00000173786 -#> 59 59 anova full STMN1 67.55910 1.835160e-27 6.945925e-25 390 ENSG00000117632 -#> 60 60 anova full PCDH17 67.41889 1.953426e-27 7.270326e-25 14929 ENSG00000118946 -#> 61 61 anova full JAM3 67.15544 2.197292e-27 8.043888e-25 12600 ENSG00000166086 -#> 62 62 anova full SPP1 67.11037 2.242055e-27 8.075377e-25 5424 ENSG00000118785 -#> 63 63 anova full NSG2 66.84392 2.526499e-27 8.836315e-25 6749 ENSG00000170091 -#> 64 64 anova full RASSF2 66.83867 2.532462e-27 8.836315e-25 19455 ENSG00000101265 -#> 65 65 anova full AQP1 66.58571 2.837625e-27 9.748771e-25 8201 ENSG00000240583 -#> 66 66 anova full KLK6 66.37700 3.117757e-27 1.054888e-24 21228 ENSG00000167755 -#> 67 67 anova full PCDH8 66.28215 3.254313e-27 1.084658e-24 14927 ENSG00000136099 -#> 68 68 anova full CD9 66.16132 3.437214e-27 1.128771e-24 13536 ENSG00000010278 -#> 69 69 anova full HSPA2 66.01644 3.670548e-27 1.187928e-24 15448 ENSG00000126803 -#> 70 70 anova full LAMP2 65.92895 3.819274e-27 1.208047e-24 9511 ENSG00000005893 -#> 71 71 anova full ADAMTS4 65.91652 3.840907e-27 1.208047e-24 1600 ENSG00000158859 -#> 72 72 anova full NKX6-2 65.69436 4.249409e-27 1.317966e-24 13454 ENSG00000148826 -#> 73 73 anova full RXFP1 65.65748 4.321441e-27 1.321946e-24 5706 ENSG00000171509 -#> 74 74 anova full NDRG1 65.45209 4.746138e-27 1.432243e-24 10404 ENSG00000104419 -#> 75 75 anova full CUX2 65.04134 5.728994e-27 1.692984e-24 14444 ENSG00000111249 -#> 76 76 anova full HS6ST3 65.02892 5.761802e-27 1.692984e-24 14990 ENSG00000185352 -#> 77 77 anova full CRYAB 64.96607 5.930678e-27 1.719974e-24 12390 ENSG00000109846 -#> 78 78 anova full EPHA4 64.72078 6.640039e-27 1.901009e-24 3606 ENSG00000116106 -#> 79 79 anova full CAMK2D 64.59040 7.052032e-27 1.993404e-24 5548 ENSG00000145349 -#> 80 80 anova full MT2A 64.50088 7.350042e-27 2.051672e-24 17241 ENSG00000125148 -#> 81 81 anova full SATB1 64.19407 8.473373e-27 2.336036e-24 3886 ENSG00000182568 -#> 82 82 anova full CALB2 63.95806 9.456666e-27 2.551130e-24 17428 ENSG00000172137 -#> 83 83 anova full NEFL 63.95231 9.482055e-27 2.551130e-24 9854 ENSG00000277586 -#> 84 84 anova full TMEM144 63.91723 9.638339e-27 2.562306e-24 5705 ENSG00000164124 -#> 85 85 anova full SELENOP 63.78713 1.024149e-26 2.669139e-24 5993 ENSG00000250722 -#> 86 86 anova full SNRPN 63.77925 1.027925e-26 2.669139e-24 15855 ENSG00000128739 -#> 87 87 anova full RNASE1 63.49234 1.175599e-26 3.017506e-24 15115 ENSG00000129538 -#> 88 88 anova full CLIC4 63.10849 1.407984e-26 3.572920e-24 372 ENSG00000169504 -#> 89 89 anova full NFIA 63.03233 1.459447e-26 3.661900e-24 845 ENSG00000162599 -#> 90 90 anova full NSG1 62.72375 1.688565e-26 4.189705e-24 5069 ENSG00000168824 -#> 91 91 anova full CRLF1 62.52176 1.858283e-26 4.516390e-24 20551 ENSG00000006016 -#> 92 92 anova full FABP7 62.51905 1.860677e-26 4.516390e-24 7713 ENSG00000164434 -#> 93 93 anova full RCN1 62.03260 2.345867e-26 5.632854e-24 11682 ENSG00000049449 -#> 94 94 anova full VAMP1 61.84644 2.564412e-26 6.092115e-24 13544 ENSG00000139190 -#> 95 95 anova full HHIP 61.80629 2.614236e-26 6.121400e-24 5646 ENSG00000164161 -#> 96 96 anova full CA4 61.79251 2.631563e-26 6.121400e-24 18652 ENSG00000167434 -#> 97 97 anova full NR4A2 61.51882 3.001264e-26 6.909405e-24 3178 ENSG00000153234 -#> 98 98 anova full CARNS1 61.43900 3.118843e-26 7.106825e-24 12099 ENSG00000172508 -#> 99 99 anova full SCD 61.37812 3.211695e-26 7.244481e-24 13221 ENSG00000099194 -#> 100 100 anova full PMP22 60.92389 4.000660e-26 8.933874e-24 17940 ENSG00000109099 -#> 101 101 anova full NEUROD6 60.22879 5.613816e-26 1.241209e-23 8204 ENSG00000164600 -#> 102 102 anova full TMEM125 60.20007 5.693345e-26 1.246452e-23 660 ENSG00000179178 -#> 103 103 anova full ASS1 59.82893 6.831717e-26 1.481156e-23 11234 ENSG00000130707 -#> 104 104 anova full FAM84A 59.68038 7.350694e-26 1.578349e-23 2335 ENSG00000162981 -#> 105 105 anova full ELOVL1 59.52045 7.954890e-26 1.691816e-23 666 ENSG00000066322 -#> 106 106 anova full RETREG1 59.44490 8.257848e-26 1.739679e-23 5910 ENSG00000154153 -#> 107 107 anova full MEF2C 59.36216 8.603284e-26 1.795513e-23 6227 ENSG00000081189 -#> 108 108 anova full CERS2 59.28061 8.958301e-26 1.852295e-23 1338 ENSG00000143418 -#> 109 109 anova full PTP4A2 59.08478 9.873643e-26 2.022829e-23 487 ENSG00000184007 -#> 110 110 anova full NEFM 58.86242 1.103046e-25 2.239283e-23 9853 ENSG00000104722 -#> 111 111 anova full GJC2 58.49740 1.324042e-25 2.663710e-23 2083 ENSG00000198835 +#> top model_type test gene stat pval fdr gene_index +#> 1 1 anova full KRT17 142.62226 1.047345e-37 2.338827e-33 18321 +#> 2 2 anova full SERPINE2 130.01590 2.154786e-36 2.405926e-32 3621 +#> 3 3 anova full GFAP 127.31169 4.267922e-36 3.176899e-32 18436 +#> 4 4 anova full CBLN4 118.04809 4.923904e-35 2.748892e-31 19844 +#> 5 5 anova full VSTM2A 111.03407 3.530274e-34 1.576691e-30 8315 +#> 6 6 anova full CAMK2N1 108.91547 6.542909e-34 2.435162e-30 308 +#> 7 7 anova full HS3ST4 104.94373 2.142606e-33 6.049917e-30 16991 +#> 8 8 anova full MYRF 104.90591 2.167361e-33 6.049917e-30 11871 +#> 9 9 anova full BCAS1 103.30459 3.537055e-33 8.776220e-30 19839 +#> 10 10 anova full MOBP 100.93656 7.389973e-33 1.650255e-29 3978 +#> 11 11 anova full EFHD2 95.11926 4.830189e-32 9.805724e-29 232 +#> 12 12 anova full MBP 93.31013 8.841183e-32 1.645271e-28 19352 +#> 13 13 anova full PLP1 90.81919 2.067407e-31 3.551327e-28 9433 +#> 14 14 anova full TLE4 89.57378 3.185778e-31 5.081544e-28 10842 +#> 15 15 anova full SCN1B 89.29950 3.506551e-31 5.220320e-28 20704 +#> 16 16 anova full HS3ST2 88.80268 4.174799e-31 5.826714e-28 16964 +#> 17 17 anova full HPCAL1 87.92038 5.702797e-31 7.491127e-28 2313 +#> 18 18 anova full FRMPD2 87.32346 7.053483e-31 8.394075e-28 12882 +#> 19 19 anova full HOPX 87.28856 7.141974e-31 8.394075e-28 5291 +#> 20 20 anova full CTGF 86.88731 8.245258e-31 9.206242e-28 7750 +#> 21 21 anova full CARTPT 86.45410 9.634771e-31 1.024543e-27 6121 +#> 22 22 anova full MAG 85.73052 1.251635e-30 1.242947e-27 20716 +#> 23 23 anova full MT1G 85.49982 1.361082e-30 1.242947e-27 17246 +#> 24 24 anova full LAMP5 85.49858 1.361696e-30 1.242947e-27 19480 +#> 25 25 anova full PARM1 85.43907 1.391504e-30 1.242947e-27 5344 +#> 26 26 anova full CNR1 82.78401 3.706832e-30 3.183741e-27 7564 +#> 27 27 anova full SNCG 81.49962 6.013272e-30 4.973422e-27 13092 +#> 28 28 anova full CLDND1 79.53535 1.276556e-29 1.018099e-26 4350 +#> 29 29 anova full OPALIN 79.24022 1.431399e-29 1.102226e-26 13171 +#> 30 30 anova full NECAB2 78.87941 1.647264e-29 1.226169e-26 17538 +#> 31 31 anova full RGS12 78.67761 1.782330e-29 1.283910e-26 5059 +#> 32 32 anova full RORB 77.71670 2.600133e-29 1.814486e-26 10825 +#> 33 33 anova full CRYM 75.38463 6.612801e-29 4.474863e-26 16941 +#> 34 34 anova full TF 74.48048 9.559810e-29 6.278827e-26 4598 +#> 35 35 anova full SEPT4 74.28070 1.037625e-28 6.620342e-26 18627 +#> 36 36 anova full MAL 73.88124 1.223066e-28 7.392804e-26 2851 +#> 37 37 anova full TUBA1B 73.87759 1.224906e-28 7.392804e-26 13893 +#> 38 38 anova full LPAR1 73.75946 1.286128e-28 7.558034e-26 11021 +#> 39 39 anova full ERMN 73.66065 1.339754e-28 7.671295e-26 3181 +#> 40 40 anova full EVI2A 72.84319 1.881777e-28 1.025818e-25 18141 +#> 41 41 anova full B3GALT2 72.84110 1.883415e-28 1.025818e-25 1809 +#> 42 42 anova full ERBB3 72.33845 2.324733e-28 1.236038e-25 14064 +#> 43 43 anova full PVALB 72.26690 2.395696e-28 1.244146e-25 21800 +#> 44 44 anova full SOWAHA 70.87215 4.327060e-28 2.196081e-25 6395 +#> 45 45 anova full PPP1R14A 70.24044 5.674096e-28 2.765105e-25 20817 +#> 46 46 anova full OLFM1 70.23154 5.695886e-28 2.765105e-25 11296 +#> 47 47 anova full CLSTN2 70.07161 6.102471e-28 2.899453e-25 4638 +#> 48 48 anova full SHB 69.98933 6.323153e-28 2.941715e-25 10755 +#> 49 49 anova full SMYD2 69.81673 6.813144e-28 3.104986e-25 1994 +#> 50 50 anova full CLDN11 69.69652 7.177327e-28 3.205538e-25 4790 +#> 51 51 anova full CNDP1 69.23235 8.782105e-28 3.845356e-25 19335 +#> 52 52 anova full SNCB 68.96081 9.887700e-28 4.246197e-25 6771 +#> 53 53 anova full NAP1L5 68.85738 1.034557e-27 4.358997e-25 5434 +#> 54 54 anova full TPPP3 68.53788 1.190263e-27 4.922180e-25 17341 +#> 55 55 anova full PDZD2 68.33889 1.299236e-27 5.275134e-25 5932 +#> 56 56 anova full MOG 68.06957 1.463276e-27 5.835073e-25 7082 +#> 57 57 anova full S1PR5 67.98191 1.521144e-27 5.959415e-25 20298 +#> 58 58 anova full CNP 67.60795 1.795712e-27 6.913801e-25 18334 +#> 59 59 anova full STMN1 67.55910 1.835160e-27 6.945925e-25 390 +#> 60 60 anova full PCDH17 67.41889 1.953426e-27 7.270326e-25 14929 +#> 61 61 anova full JAM3 67.15544 2.197292e-27 8.043888e-25 12600 +#> 62 62 anova full SPP1 67.11037 2.242055e-27 8.075377e-25 5424 +#> 63 63 anova full NSG2 66.84392 2.526499e-27 8.836315e-25 6749 +#> 64 64 anova full RASSF2 66.83867 2.532462e-27 8.836315e-25 19455 +#> 65 65 anova full AQP1 66.58571 2.837625e-27 9.748771e-25 8201 +#> 66 66 anova full KLK6 66.37700 3.117757e-27 1.054888e-24 21228 +#> 67 67 anova full PCDH8 66.28215 3.254313e-27 1.084658e-24 14927 +#> 68 68 anova full CD9 66.16132 3.437214e-27 1.128771e-24 13536 +#> 69 69 anova full HSPA2 66.01644 3.670548e-27 1.187928e-24 15448 +#> 70 70 anova full LAMP2 65.92895 3.819274e-27 1.208047e-24 9511 +#> 71 71 anova full ADAMTS4 65.91652 3.840907e-27 1.208047e-24 1600 +#> 72 72 anova full NKX6-2 65.69436 4.249409e-27 1.317966e-24 13454 +#> 73 73 anova full RXFP1 65.65748 4.321441e-27 1.321946e-24 5706 +#> 74 74 anova full NDRG1 65.45209 4.746138e-27 1.432243e-24 10404 +#> 75 75 anova full CUX2 65.04134 5.728994e-27 1.692984e-24 14444 +#> 76 76 anova full HS6ST3 65.02892 5.761802e-27 1.692984e-24 14990 +#> 77 77 anova full CRYAB 64.96607 5.930678e-27 1.719974e-24 12390 +#> 78 78 anova full EPHA4 64.72078 6.640039e-27 1.901009e-24 3606 +#> 79 79 anova full CAMK2D 64.59040 7.052032e-27 1.993404e-24 5548 +#> 80 80 anova full MT2A 64.50088 7.350042e-27 2.051672e-24 17241 +#> 81 81 anova full SATB1 64.19407 8.473373e-27 2.336036e-24 3886 +#> 82 82 anova full CALB2 63.95806 9.456666e-27 2.551130e-24 17428 +#> 83 83 anova full NEFL 63.95231 9.482055e-27 2.551130e-24 9854 +#> 84 84 anova full TMEM144 63.91723 9.638339e-27 2.562306e-24 5705 +#> 85 85 anova full SELENOP 63.78713 1.024149e-26 2.669139e-24 5993 +#> 86 86 anova full SNRPN 63.77925 1.027925e-26 2.669139e-24 15855 +#> 87 87 anova full RNASE1 63.49234 1.175599e-26 3.017506e-24 15115 +#> 88 88 anova full CLIC4 63.10849 1.407984e-26 3.572920e-24 372 +#> 89 89 anova full NFIA 63.03233 1.459447e-26 3.661900e-24 845 +#> 90 90 anova full NSG1 62.72375 1.688565e-26 4.189705e-24 5069 +#> 91 91 anova full CRLF1 62.52176 1.858283e-26 4.516390e-24 20551 +#> 92 92 anova full FABP7 62.51905 1.860677e-26 4.516390e-24 7713 +#> 93 93 anova full RCN1 62.03260 2.345867e-26 5.632854e-24 11682 +#> 94 94 anova full VAMP1 61.84644 2.564412e-26 6.092115e-24 13544 +#> 95 95 anova full HHIP 61.80629 2.614236e-26 6.121400e-24 5646 +#> 96 96 anova full CA4 61.79251 2.631563e-26 6.121400e-24 18652 +#> 97 97 anova full NR4A2 61.51882 3.001264e-26 6.909405e-24 3178 +#> 98 98 anova full CARNS1 61.43900 3.118843e-26 7.106825e-24 12099 +#> 99 99 anova full SCD 61.37812 3.211695e-26 7.244481e-24 13221 +#> 100 100 anova full PMP22 60.92389 4.000660e-26 8.933874e-24 17940 +#> 101 101 anova full NEUROD6 60.22879 5.613816e-26 1.241209e-23 8204 +#> 102 102 anova full TMEM125 60.20007 5.693345e-26 1.246452e-23 660 +#> 103 103 anova full ASS1 59.82893 6.831717e-26 1.481156e-23 11234 +#> 104 104 anova full FAM84A 59.68038 7.350694e-26 1.578349e-23 2335 +#> 105 105 anova full ELOVL1 59.52045 7.954890e-26 1.691816e-23 666 +#> 106 106 anova full RETREG1 59.44490 8.257848e-26 1.739679e-23 5910 +#> 107 107 anova full MEF2C 59.36216 8.603284e-26 1.795513e-23 6227 +#> 108 108 anova full CERS2 59.28061 8.958301e-26 1.852295e-23 1338 +#> 109 109 anova full PTP4A2 59.08478 9.873643e-26 2.022829e-23 487 +#> 110 110 anova full NEFM 58.86242 1.103046e-25 2.239283e-23 9853 +#> 111 111 anova full GJC2 58.49740 1.324042e-25 2.663710e-23 2083 +#> ensembl +#> 1 ENSG00000128422 +#> 2 ENSG00000135919 +#> 3 ENSG00000131095 +#> 4 ENSG00000054803 +#> 5 ENSG00000170419 +#> 6 ENSG00000162545 +#> 7 ENSG00000182601 +#> 8 ENSG00000124920 +#> 9 ENSG00000064787 +#> 10 ENSG00000168314 +#> 11 ENSG00000142634 +#> 12 ENSG00000197971 +#> 13 ENSG00000123560 +#> 14 ENSG00000106829 +#> 15 ENSG00000105711 +#> 16 ENSG00000122254 +#> 17 ENSG00000115756 +#> 18 ENSG00000170324 +#> 19 ENSG00000171476 +#> 20 ENSG00000118523 +#> 21 ENSG00000164326 +#> 22 ENSG00000105695 +#> 23 ENSG00000125144 +#> 24 ENSG00000125869 +#> 25 ENSG00000169116 +#> 26 ENSG00000118432 +#> 27 ENSG00000173267 +#> 28 ENSG00000080822 +#> 29 ENSG00000197430 +#> 30 ENSG00000103154 +#> 31 ENSG00000159788 +#> 32 ENSG00000198963 +#> 33 ENSG00000103316 +#> 34 ENSG00000091513 +#> 35 ENSG00000108387 +#> 36 ENSG00000172005 +#> 37 ENSG00000123416 +#> 38 ENSG00000198121 +#> 39 ENSG00000136541 +#> 40 ENSG00000126860 +#> 41 ENSG00000162630 +#> 42 ENSG00000065361 +#> 43 ENSG00000100362 +#> 44 ENSG00000198944 +#> 45 ENSG00000167641 +#> 46 ENSG00000130558 +#> 47 ENSG00000158258 +#> 48 ENSG00000107338 +#> 49 ENSG00000143499 +#> 50 ENSG00000013297 +#> 51 ENSG00000150656 +#> 52 ENSG00000074317 +#> 53 ENSG00000177432 +#> 54 ENSG00000159713 +#> 55 ENSG00000133401 +#> 56 ENSG00000204655 +#> 57 ENSG00000180739 +#> 58 ENSG00000173786 +#> 59 ENSG00000117632 +#> 60 ENSG00000118946 +#> 61 ENSG00000166086 +#> 62 ENSG00000118785 +#> 63 ENSG00000170091 +#> 64 ENSG00000101265 +#> 65 ENSG00000240583 +#> 66 ENSG00000167755 +#> 67 ENSG00000136099 +#> 68 ENSG00000010278 +#> 69 ENSG00000126803 +#> 70 ENSG00000005893 +#> 71 ENSG00000158859 +#> 72 ENSG00000148826 +#> 73 ENSG00000171509 +#> 74 ENSG00000104419 +#> 75 ENSG00000111249 +#> 76 ENSG00000185352 +#> 77 ENSG00000109846 +#> 78 ENSG00000116106 +#> 79 ENSG00000145349 +#> 80 ENSG00000125148 +#> 81 ENSG00000182568 +#> 82 ENSG00000172137 +#> 83 ENSG00000277586 +#> 84 ENSG00000164124 +#> 85 ENSG00000250722 +#> 86 ENSG00000128739 +#> 87 ENSG00000129538 +#> 88 ENSG00000169504 +#> 89 ENSG00000162599 +#> 90 ENSG00000168824 +#> 91 ENSG00000006016 +#> 92 ENSG00000164434 +#> 93 ENSG00000049449 +#> 94 ENSG00000139190 +#> 95 ENSG00000164161 +#> 96 ENSG00000167434 +#> 97 ENSG00000153234 +#> 98 ENSG00000172508 +#> 99 ENSG00000099194 +#> 100 ENSG00000109099 +#> 101 ENSG00000164600 +#> 102 ENSG00000179178 +#> 103 ENSG00000130707 +#> 104 ENSG00000162981 +#> 105 ENSG00000066322 +#> 106 ENSG00000154153 +#> 107 ENSG00000081189 +#> 108 ENSG00000143418 +#> 109 ENSG00000184007 +#> 110 ENSG00000104722 +#> 111 ENSG00000198835 #> [ reached 'max' / getOption("max.print") -- omitted 44551 rows ] diff --git a/reference/sig_genes_extract_all.html b/reference/sig_genes_extract_all.html index e1181eb2..cfa0f8da 100644 --- a/reference/sig_genes_extract_all.html +++ b/reference/sig_genes_extract_all.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -129,10 +129,10 @@

Examples

modeling_results <- fetch_data(type = "modeling_results") } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:19.335113 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:11.905811 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc154f26894b_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1 if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer") #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:20.030926 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:12.752845 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/fc1571d8e1fb_Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata%3Fdl%3D1 ## top 10 genes for all models sig_genes_extract_all( @@ -140,32 +140,32 @@

Examples

sce_layer = sce_layer ) #> DataFrame with 510 rows and 12 columns -#> top model_type test gene stat pval fdr gene_index -#> <integer> <character> <character> <character> <numeric> <numeric> <numeric> <integer> -#> 1 1 enrichment WM NDRG1 16.3053 1.25896e-26 2.51372e-22 10404 -#> 2 2 enrichment WM PTP4A2 16.1469 2.25133e-26 2.51372e-22 487 -#> 3 3 enrichment WM AQP1 15.9927 3.97849e-26 2.96145e-22 8201 -#> 4 4 enrichment WM PAQR6 15.1971 7.86258e-25 4.38948e-21 1501 -#> 5 5 enrichment WM ANP32B 14.9798 1.80183e-24 8.04735e-21 10962 -#> ... ... ... ... ... ... ... ... ... -#> 506 6 anova noWM HOPX 157.180 3.16423e-33 1.17767e-29 5291 -#> 507 7 anova noWM CLSTN2 148.428 1.55814e-32 4.97068e-29 4638 -#> 508 8 anova noWM TUBA1B 135.620 1.89089e-31 4.91887e-28 13893 -#> 509 9 anova noWM HS3ST2 135.387 1.98244e-31 4.91887e-28 16964 -#> 510 10 anova noWM ETV1 130.017 6.03393e-31 1.34744e-27 8095 -#> ensembl in_rows in_rows_top20 -#> <character> <IntegerList> <IntegerList> -#> 1 ENSG00000104419 1,110,113,... 1,110,113,... -#> 2 ENSG00000184007 2,126 2,126 -#> 3 ENSG00000240583 3,104,112,... 3,104,112,... -#> 4 ENSG00000160781 4,130 4,130 -#> 5 ENSG00000136938 5,123 5,123 -#> ... ... ... ... -#> 506 ENSG00000171476 234,248,256,... -#> 507 ENSG00000158258 390,415,424,... -#> 508 ENSG00000123416 360,362,373,... -#> 509 ENSG00000122254 375,386,413,... -#> 510 ENSG00000006468 446,464,510 +#> top model_type test gene stat pval +#> <integer> <character> <character> <character> <numeric> <numeric> +#> 1 1 enrichment WM NDRG1 16.3053 1.25896e-26 +#> 2 2 enrichment WM PTP4A2 16.1469 2.25133e-26 +#> 3 3 enrichment WM AQP1 15.9927 3.97849e-26 +#> 4 4 enrichment WM PAQR6 15.1971 7.86258e-25 +#> 5 5 enrichment WM ANP32B 14.9798 1.80183e-24 +#> ... ... ... ... ... ... ... +#> 506 6 anova noWM HOPX 157.180 3.16423e-33 +#> 507 7 anova noWM CLSTN2 148.428 1.55814e-32 +#> 508 8 anova noWM TUBA1B 135.620 1.89089e-31 +#> 509 9 anova noWM HS3ST2 135.387 1.98244e-31 +#> 510 10 anova noWM ETV1 130.017 6.03393e-31 +#> fdr gene_index ensembl in_rows in_rows_top20 +#> <numeric> <integer> <character> <IntegerList> <IntegerList> +#> 1 2.51372e-22 10404 ENSG00000104419 1,110,113,... 1,110,113,... +#> 2 2.51372e-22 487 ENSG00000184007 2,126 2,126 +#> 3 2.96145e-22 8201 ENSG00000240583 3,104,112,... 3,104,112,... +#> 4 4.38948e-21 1501 ENSG00000160781 4,130 4,130 +#> 5 8.04735e-21 10962 ENSG00000136938 5,123 5,123 +#> ... ... ... ... ... ... +#> 506 1.17767e-29 5291 ENSG00000171476 234,248,256,... +#> 507 4.97068e-29 4638 ENSG00000158258 390,415,424,... +#> 508 4.91887e-28 13893 ENSG00000123416 360,362,373,... +#> 509 4.91887e-28 16964 ENSG00000122254 375,386,413,... +#> 510 1.34744e-27 8095 ENSG00000006468 446,464,510 #> results #> <CharacterList> #> 1 WM_top1,WM-Layer4_top10,WM-Layer5_top3,... diff --git a/reference/sort_clusters.html b/reference/sort_clusters.html index 037bbec4..748b62c2 100644 --- a/reference/sort_clusters.html +++ b/reference/sort_clusters.html @@ -1,6 +1,7 @@ -Sort clusters by frequency — sort_clusters • spatialLIBDSort clusters by frequency — sort_clusters • spatialLIBD @@ -18,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -69,8 +70,9 @@

Sort clusters by frequency

-

This function takes a vector with cluster labels and sorts it by frequency -such that the most frequent cluster is the first one and so on.

+

This function takes a vector with cluster labels, recasts it as a factor(), +and sorts the factor() levels by frequency such that the most frequent +cluster is the first level and so on.

@@ -92,9 +94,8 @@

Arguments

Value

-

A factor of length equal to clusters where the levels are the new -ordered clusters and the names of the factor are the original values from -clusters.

+

A factor() version of clusters where the levels are ordered by +frequency.

@@ -107,11 +108,61 @@

Examples

class(clus) #> [1] "character" -## Sort them and obtain a factor +## We see that we have 10 elements in this vector, which is +## an unnamed character vector +clus +#> [1] "d" "d" "d" "d" "c" "c" "c" "b" "b" "a" + +## letter 'd' is the most frequent +table(clus) +#> clus +#> a b c d +#> 1 2 3 4 + +## Sort them and obtain a factor. Notice that it's a named +## factor, and the names correspond to the original values +## in the character vector. sort_clusters(clus) -#> d d d d c c c b b a -#> 1 1 1 1 2 2 2 3 3 4 -#> Levels: 1 2 3 4 +#> [1] d d d d c c c b b a +#> Levels: d c b a + +## Since 'd' was the most frequent, it gets assigned to the first level +## in the factor variable. +table(sort_clusters(clus)) +#> +#> d c b a +#> 4 3 2 1 + +## If we skip the first 3 values of clus (which are all 'd'), we can +## change the most frequent cluster. And thus the ordering of the +## factor levels. +sort_clusters(clus, map_subset = seq_len(length(clus)) > 3) +#> [1] d d d d c c c b b a +#> Levels: c b a d + +## Let's try with a factor variable +clus_factor <- factor(clus) +## sort_clusters() returns an identical result in this case +stopifnot(identical(sort_clusters(clus), sort_clusters(clus_factor))) + +## What happens if you have a logical variable with NAs? +set.seed(20240712) +log_var <- sample(c(TRUE, FALSE, NA), + 1000, + replace = TRUE, + prob = c(0.3, 0.15, 0.55)) +## Here, the NAs are the most frequent group. +table(log_var, useNA = "ifany") +#> log_var +#> FALSE TRUE <NA> +#> 135 304 561 + +## The NAs are not used for sorting. Since we have more 'TRUE' than 'FALSE' +## then, 'TRUE' becomes the first level. +table(sort_clusters(log_var), useNA = "ifany") +#> +#> TRUE FALSE <NA> +#> 304 135 561
diff --git a/reference/spatialLIBD-package.html b/reference/spatialLIBD-package.html index 13766015..3e732ad9 100644 --- a/reference/spatialLIBD-package.html +++ b/reference/spatialLIBD-package.html @@ -18,7 +18,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.html b/reference/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.html index 7669f3ea..869b4c0d 100644 --- a/reference/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.html +++ b/reference/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 diff --git a/reference/vis_clus.html b/reference/vis_clus.html index 2c7c71c2..01566a90 100644 --- a/reference/vis_clus.html +++ b/reference/vis_clus.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -243,7 +243,7 @@

Examples

print(p4) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:22.198229 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:15.004503 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/vis_clus_p.html b/reference/vis_clus_p.html index b186040a..67f40ed4 100644 --- a/reference/vis_clus_p.html +++ b/reference/vis_clus_p.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -203,7 +203,7 @@

Examples

rm(spe_sub) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:36.562334 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:29.511104 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/vis_gene.html b/reference/vis_gene.html index e4f4dbfc..f89e22aa 100644 --- a/reference/vis_gene.html +++ b/reference/vis_gene.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -342,7 +342,7 @@

Examples

print(p8) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:10:47.346846 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:40.464118 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/vis_gene_p.html b/reference/vis_gene_p.html index 7379d594..bc2637ce 100644 --- a/reference/vis_gene_p.html +++ b/reference/vis_gene_p.html @@ -21,7 +21,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -220,7 +220,7 @@

Examples

rm(spe_sub) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:11:06.190703 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:33:59.946661 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/vis_grid_clus.html b/reference/vis_grid_clus.html index 6df31a1d..2f7d641c 100644 --- a/reference/vis_grid_clus.html +++ b/reference/vis_grid_clus.html @@ -19,7 +19,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -238,7 +238,7 @@

Examples

cowplot::plot_grid(plotlist = p_list, ncol = 2) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:11:16.883657 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:34:10.866459 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 diff --git a/reference/vis_grid_gene.html b/reference/vis_grid_gene.html index a2e976b1..525f4f5b 100644 --- a/reference/vis_grid_gene.html +++ b/reference/vis_grid_gene.html @@ -20,7 +20,7 @@ spatialLIBD - 1.17.5 + 1.17.6 @@ -257,7 +257,7 @@

Examples

cowplot::plot_grid(plotlist = p_list, ncol = 2) } #> snapshotDate(): 2024-04-29 -#> 2024-07-11 14:11:28.811041 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1 +#> 2024-07-12 13:34:22.971932 loading file /Users/leocollado/Library/Caches/org.R-project.R/R/BiocFileCache/6f56656adcb_Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata%3Fdl%3D1