| Next changeset 1:fb2f1b8b0013 (2022-12-04) |
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Commit message:
"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit ddcf915dd9b690d7f3876e08b939adde36cbb8dd" |
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added:
scran-normalize.R scran_normalize.xml test-data/counts.tab test-data/logcounts.tab test-data/logcounts_hclust.tsv test-data/logcounts_igraph.tsv |
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| diff -r 000000000000 -r 252eded61848 scran-normalize.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scran-normalize.R Thu Sep 26 10:50:55 2019 -0400 |
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| @@ -0,0 +1,91 @@ +# load packages that are provided in the conda env +options( show.error.messages=F, + error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) +loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") +warnings() + +library(optparse) +library(scran) + +#Arguments +option_list = list( + make_option( + c("-d", "--data"), + default = NA, + type = 'character', + help = "Input file that contains count values to transform" + ), + make_option( + c("-s", "--sep"), + default = '\t', + type = 'character', + help = "File separator [default : '%default' ]" + ), + make_option( + "--cluster", + default=FALSE, + action="store_true", + type = 'logical', + help = "Whether to calculate the size factor per cluster or on all cell" + ), + make_option( + c("-m", "--method"), + default = 'hclust', + type = 'character', + help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]" + ), + make_option( + "--size", + default = 100, + type = 'integer', + help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]" + ), + make_option( + c("-o", "--out"), + default = "res.tab", + type = 'character', + help = "Output name [default : '%default' ]" + ) +) + +opt = parse_args(OptionParser(option_list = option_list), + args = commandArgs(trailingOnly = TRUE)) + +if (opt$sep == "tab") {opt$sep = "\t"} + +data = read.table( + opt$data, + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = opt$sep +) + +## Import data as a SingleCellExperiment object +sce <- SingleCellExperiment(list(counts=as.matrix(data))) + + +if(opt$cluster){ + clusters <- quickCluster(sce, min.size = opt$size, method = opt$method) + + ## Compute sum factors + sce <- computeSumFactors(sce, cluster = clusters) +} else { + + ## Compute sum factors + sce <- computeSumFactors(sce) +} + +sce <- normalize(sce) + +logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits=5), check.names = F) + + +write.table( + logcounts, + opt$out, + col.names = T, + row.names = F, + quote = F, + sep = "\t" +) |
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| diff -r 000000000000 -r 252eded61848 scran_normalize.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scran_normalize.xml Thu Sep 26 10:50:55 2019 -0400 |
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| @@ -0,0 +1,107 @@ +<tool id="scran_normalize" name="scran_normalize" version="0.2.0"> + <description>Normalize raw counts expression values using deconvolution size factors</description> + <requirements> + <requirement type="package" version="1.6.2">r-optparse</requirement> + <requirement type="package" version="1.12.1=r36he1b5a44_0">bioconductor-scran</requirement> + </requirements> + <stdio> + <exit_code range="1:" level="fatal" description="Tool exception" /> + </stdio> + <command detect_errors="exit_code"><![CDATA[ + Rscript $__tool_directory__/scran-normalize.R + --data '$input' + --sep '$input_sep' + #if $metacell.cluster == "Yes": + --cluster + --method '$metacell.method' + --size '$metacell.size' + #end if + -o ${output} +]]></command> + <inputs> + <param name="input" type="data" format="txt,tabular" label="Raw counts of expression data" help = "Must have an header"/> + <param name="input_sep" type="select" label="Input column separator"> + <option value="tab" selected="true">Tabulation</option> + <option value=",">Comma</option> + </param> + <conditional name="metacell"> + <param name="cluster" type="select" label = "Do you want to cluster cells ?" help="Perform scaling method on metacell, see Details"> + <option value="Yes">Yes</option> + <option value="No" selected="true">No</option> + </param> + <when value="Yes"> + <param name="method" type="select" label="Clustering method"> + <option value="hclust" selected="true">hclust</option> + <option value="igraph">igprah</option> + </param> + <param name="size" type="integer" value="100" label="Minimum size of each cluster"/> + </when> + <when value="No"/> + </conditional> + </inputs> + <outputs> + <data name="output" format="tabular" label="Normalized Log counts of ${on_string}"> + </data> + </outputs> + <tests> + <test> + <param name="input" value="counts.tab" ftype="tabular"/> + <output name="output" file="logcounts.tab" ftype="tabular"/> + </test> + <test> + <param name="input" value="counts.tab" ftype="tabular"/> + <param name="cluster" value="Yes"/> + <param name="method" value="igraph"/> + <param name="size" value="25"/> + <output name="output" file="logcounts_igraph.tsv" ftype="tabular"/> + </test> + <test> + <param name="input" value="counts.tab" ftype="tabular"/> + <param name="cluster" value="Yes"/> + <param name="method" value="hclust"/> + <param name="size" value="25"/> + <output name="output" file="logcounts_hclust.tsv" ftype="tabular"/> + </test> + </tests> + <help> + +**What it does** + +Takes a raw count expression matrix and returns a table of log transformed scran-normalized expression values. + +This computes size factors that are used to scale the counts in each cell. The assumption is that +most genes are not differentially expressed (DE) between cells, such that any differences in +expression across the majority of genes represents some technical bias that should be removed. + +Cell-specific biases are normalized using the computeSumFactors method, which implements the +deconvolution strategy for scaling normalization (A. T. Lun, Bach, and Marioni 2016). It creates a reference : + - if no clustering step : the average count of all transcriptomes + - if you choose to cluster your cells : the average count of each cluster. +Then it pools cells and then sum their expression profiles. The size factor is described as the median ration +between the count sums and the average across all genes. Finally it constructs a linear distribution (deconvolution method) +of size factors by taking multiple pools of cells. + +You can apply this method on cell cluster instead of your all set of cells by using quickCluster. +It defines cluster using distances based on Spearman correlation on counts between cells, there is two available methods : + + - *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut. + - *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities. + + +Note: First header row must NOT start with a '#' comment character + + </help> + <citations> + <citation type="bibtex"> + @Article{, + author = {Aaron T. L. Lun and Davis J. McCarthy and John C. Marioni}, + title = {A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor}, + journal = {F1000Res.}, + year = {2016}, + volume = {5}, + pages = {2122}, + doi = {10.12688/f1000research.9501.2}, + } + </citation> + </citations> +</tool> |
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| diff -r 000000000000 -r 252eded61848 test-data/counts.tab --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/counts.tab Thu Sep 26 10:50:55 2019 -0400 |
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|
| b |
| diff -r 000000000000 -r 252eded61848 test-data/logcounts.tab --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/logcounts.tab Thu Sep 26 10:50:55 2019 -0400 |
| b |
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|
| b |
| diff -r 000000000000 -r 252eded61848 test-data/logcounts_hclust.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/logcounts_hclust.tsv Thu Sep 26 10:50:55 2019 -0400 |
| b |
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|
| b |
| diff -r 000000000000 -r 252eded61848 test-data/logcounts_igraph.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/logcounts_igraph.tsv Thu Sep 26 10:50:55 2019 -0400 |
| b |
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