diff --git a/404.html b/404.html index 8f72a1f..7308882 100644 --- a/404.html +++ b/404.html @@ -47,7 +47,14 @@ - + diff --git a/LICENSE.html b/LICENSE.html index 636ecce..e81ce3d 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -30,7 +30,13 @@ - + @@ -38,7 +44,7 @@

GNU General Public License

- + Source: LICENSE.md
diff --git a/articles/index.html b/articles/index.html index d3865a3..47faaae 100644 --- a/articles/index.html +++ b/articles/index.html @@ -30,7 +30,13 @@ -
    +
    diff --git a/articles/pbmc.html b/articles/pbmc.html index e1ae5b5..cdb367c 100644 --- a/articles/pbmc.html +++ b/articles/pbmc.html @@ -49,7 +49,14 @@ - + @@ -64,7 +71,7 @@

    Demonstration of workflow

    - + Source: vignettes/pbmc.Rmd
    pbmc.Rmd
    @@ -136,15 +143,178 @@

    Load the data in Seurat and prep #> Running SCTransform on assay: RNA #> Running SCTransform on layer: counts #> vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. +#> `vst.flavor` is set to 'v2' but could not find glmGamPoi installed. +#> Please install the glmGamPoi package for much faster estimation. +#> -------------------------------------------- +#> install.packages('BiocManager') +#> BiocManager::install('glmGamPoi') +#> -------------------------------------------- +#> Falling back to native (slower) implementation. #> Variance stabilizing transformation of count matrix of size 19168 by 2686 #> Model formula is y ~ log_umi #> Get Negative Binomial regression parameters per gene #> Using 2000 genes, 2686 cells -#> Found 8 outliers - those will be ignored in fitting/regularization step +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation +#> limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = +#> control$trace > : iteration limit reached +#> Warning in sqrt(1/i): NaNs produced +#> Found 14 outliers - those will be ignored in fitting/regularization step #> Second step: Get residuals using fitted parameters for 19168 genes #> Computing corrected count matrix for 19168 genes #> Calculating gene attributes -#> Wall clock passed: Time difference of 23.63094 secs +#> Wall clock passed: Time difference of 2.223406 mins #> Determine variable features #> Centering data matrix #> Set default assay to SCT diff --git a/articles/pbmc_files/figure-html/viz-metrics-1.png b/articles/pbmc_files/figure-html/viz-metrics-1.png index 121d9ea..001e3b7 100644 Binary files a/articles/pbmc_files/figure-html/viz-metrics-1.png and b/articles/pbmc_files/figure-html/viz-metrics-1.png differ diff --git a/articles/pbmc_files/figure-html/viz-reg-network-1.png b/articles/pbmc_files/figure-html/viz-reg-network-1.png index 0db6030..21ffafe 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    Authors

    Citation

    -

    +

    Source: inst/CITATION

    Larsson I, Held F, Popova G, Koc A, Jörnsten R, Nelander S (2023). “Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.” diff --git a/index.html b/index.html index fb989ac..549d49d 100644 --- a/index.html +++ b/index.html @@ -49,7 +49,14 @@ -

    +
    @@ -61,13 +68,23 @@ -
    -A diagram illustrating the scregclust algorithm.
    A diagram illustrating the scregclust algorithm.
    -
    -

    The goal of scregclust is to cluster genes by regulatory programs. To do so, clusters are associated with regulatory programs and target genes are allocated to clusters with best fitting regulatory programs.

    +

    +
    +

    Introduction +

    +

    The goal of scregclust is to cluster genes by regulatory programs. To do so, clusters are associated with regulators and target genes are allocated to clusters with best fitting regulatory programs.

    A detailed description of the algorithm and an in-depth evaluation of its properties can be found in our pre-print on bioRxiv: DOI 10.1101/2023.03.10.532041

    -