Mercurial > repos > artbio > gsc_signature_score
changeset 0:baf7a079bce0 draft
planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_signature_score commit 09dcd74dbc01f448518cf3db3e646afb0675a6fe
| author | artbio |
|---|---|
| date | Mon, 24 Jun 2019 13:40:08 -0400 |
| parents | |
| children | 01b2c4fcada8 |
| files | signature_score.R signature_score.xml test-data/gene_filtered_input.tsv test-data/gene_stats.tsv test-data/signature.pdf test-data/signature.tsv |
| diffstat | 6 files changed, 515 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/signature_score.R Mon Jun 24 13:40:08 2019 -0400 @@ -0,0 +1,230 @@ +######################### +# Signature score # +######################### + +# Compute the signature score based on the geometric mean of the target gene expression +# and split cells in 2 groups (high/low) using this signature score. + +# Example of command +# Rscript 4-signature_score.R --input <input.tsv> --genes ARNT2,SALL2,SOX9,OLIG2,POU3F2 +# --output <output.tab> --pdf <output.pdf> + +# 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(psych) +library(ggplot2) +library(gridExtra) + +# Arguments +option_list = list( + make_option( + "--input", + default = NA, + type = 'character', + help = "Input file that contains log2(CPM +1) values" + ), + make_option( + "--sep", + default = '\t', + type = 'character', + help = "File separator [default : '%default' ]" + ), + make_option( + "--colnames", + default = TRUE, + type = 'logical', + help = "Consider first line as header ? [default : '%default' ]" + ), + make_option( + "--genes", + default = NA, + type = 'character', + help = "List of genes comma separated" + ), + make_option( + "--percentile_threshold", + default = 20, + type = 'integer', + help = "detection threshold to keep a gene in signature set [default : '%default' ]" + ), + make_option( + "--output", + default = "./output.tab", + type = 'character', + help = "Output path [default : '%default' ]" + ), + make_option( + "--stats", + default = "./statistics.tab", + type = 'character', + help = "statistics path [default : '%default' ]" + ), + make_option( + "--pdf", + default = "~/output.pdf", + type = 'character', + help = "pdf path [default : '%default' ]" + ) +) + +opt = parse_args(OptionParser(option_list = option_list), + args = commandArgs(trailingOnly = TRUE)) + +if (opt$sep == "tab") {opt$sep = "\t"} +if (opt$sep == "comma") {opt$sep = ","} + +# Take input data +data.counts <- read.table( + opt$input, + h = opt$colnames, + row.names = 1, + sep = opt$sep, + check.names = F +) + +# Get vector of target genes +genes <- unlist(strsplit(opt$genes, ",")) + +if (length(unique(genes %in% rownames(data.counts))) == 1) { + if (unique(genes %in% rownames(data.counts)) == F) + stop("None of these genes are in your dataset: ", opt$genes) +} + +logical_genes <- rownames(data.counts) %in% genes + +# Retrieve target genes in counts data +signature.counts <- subset(data.counts, logical_genes) + + +## Descriptive Statistics Function +descriptive_stats = function(InputData) { + SummaryData = data.frame( + mean = rowMeans(InputData), + SD = apply(InputData, 1, sd), + Variance = apply(InputData, 1, var), + Percentage_Detection = apply(InputData, 1, function(x, y = InputData) { + (sum(x != 0) / ncol(y)) * 100 + }) + ) + return(SummaryData) +} + +signature_stats <- descriptive_stats(signature.counts) + +# Find poorly detected genes from the signature +kept_genes <- signature_stats$Percentage_Detection >= opt$percentile_threshold + +# Add warnings +if (length(unique(kept_genes)) > 1) { + cat( + "WARNINGS ! Following genes were removed from further analysis due to low gene expression :", + paste(paste(rownames(signature.counts)[!kept_genes], round(signature_stats$Percentage_Detection[!kept_genes], 2), sep = " : "), collapse = ", "), + "\n" + ) +} else { + if (unique(kept_genes) == F) { + stop( + "None of these genes are detected in ", + opt$percent, + "% of your cells: ", + paste(rownames(signature_stats), collapse = ", "), + ". You can be less stringent thanks to --percent parameter." + ) + } +} + +# Remove genes poorly detected in the dataset +signature.counts <- signature.counts[kept_genes,] + +# Replace 0 by 1 counts +signature.counts[signature.counts == 0] <- 1 + +# Geometric mean by cell +score <- apply(signature.counts, 2, geometric.mean) # geometric.mean requires psych + +# Add results in signature_output +signature_output <- data.frame( + cell = names(score), + score = score, + rate = ifelse(score > mean(score), "HIGH", "LOW"), + nGenes = colSums(data.counts != 0), + total_counts = colSums(data.counts) + ) + +# statistics of input genes, signature genes first lines +statistics.counts <- rbind(subset(data.counts, logical_genes), + subset(data.counts, !logical_genes)) +statistics <- descriptive_stats(statistics.counts) +statistics <- cbind(gene=rownames(statistics), statistics) + + + +# Re-arrange score matrix for plots +score <- data.frame(score = score, + order = rank(score, ties.method = "first"), + signature = signature_output$rate, + stringsAsFactors = F) + +pdf(file = opt$pdf) + +ggplot(score, aes(x = order, y = score)) + + geom_line() + + geom_segment(x = 0, xend = max(score$order[score$signature == "LOW"]), y = mean(score$score), yend = mean(score$score)) + + geom_area(aes(fill = signature), alpha = .7) + + scale_fill_manual(values=c("#ff0000", "#08661e")) + + geom_text(aes(x = 1, y = mean(score)), label = "Mean", vjust = -0.3, colour = "black") + + labs(title = "Ordered cell signature scores", x = "Cell index", y = "Score") + +density_score <- density(score$score) +ggplot(data.frame(density_score[1:2]), aes(x, y, fill = ifelse(x < mean(score$score), "LOW", "HIGH"))) + + geom_line() + + geom_vline(xintercept = mean(score$score)) + + geom_text(x = mean(score$score), y = max(density_score$y), label = "Mean", hjust = -0.3, colour = "black") + + geom_area(alpha = .7) + + scale_fill_manual(values=c("#ff0000", "#08661e")) + + ylim(0, max(density_score$y)) + + labs( + title = "Distribution of Cell signature scores", + x = paste("N =", density_score$n, "Bandwidth =", density_score$bw), + y = "Density", + fill = "Signature" + ) + +# Check score independant of low expression +p_gene <- ggplot(signature_output, aes(rate, nGenes)) + + geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) + + scale_fill_manual(values=c("#ff0000", "#08661e")) + + geom_jitter() + labs(y = "Number of detected genes", x = "Signature") + +p_counts <- ggplot(signature_output, aes(rate, total_counts)) + + geom_violin(aes(fill = rate), alpha = .5, trim = F, show.legend = F) + + scale_fill_manual(values=c("#ff0000", "#08661e")) + + geom_jitter() + labs(y = "Total counts", x = "Signature") + +grid.arrange(p_gene, p_counts, ncol = 2, top = "Influence of library sequencing depth on cell signature scores") + +dev.off() + +# Save file +write.table( + signature_output, + opt$output, + sep = "\t", + quote = F, + col.names = T, + row.names = F +) + +write.table( + statistics, + opt$stats, + sep = "\t", + quote = F, + col.names = T, + row.names = F +) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/signature_score.xml Mon Jun 24 13:40:08 2019 -0400 @@ -0,0 +1,98 @@ +<tool id="signature_score" name="Compute signature scores" version="0.9.0"> + <description>in single cell RNAseq</description> + <requirements> + <requirement type="package" version="1.3.2=r3.3.2_0">r-optparse</requirement> + <requirement type="package" version="2.2.1=r3.3.2_0">r-ggplot2</requirement> + <requirement type="package" version="2.3=r3.3.2_0">r-gridextra</requirement> + <requirement type="package" version="1.6.9=r3.3.2_0">r-psych</requirement> + </requirements> + <stdio> + <exit_code range="1:" level="fatal" description="Tool exception" /> + </stdio> + <command detect_errors="exit_code"><![CDATA[ + Rscript $__tool_directory__/signature_score.R + --input $input + --sep + #if $sep == 'tab': + 'tab' + #elif $sep == 'comma': + 'comma' + #end if + --genes '$genes' + --percentile_threshold '$threshold' + --output '$output' + --stats '$stats' + --pdf '$pdf' +]]></command> + <inputs> + <param name="input" type="data" format="txt,tabular" label="Raw counts of expression data"/> + <param name="sep" type="select" label="Indicate column separator"> + <option value="tab" selected="true">Tabs</option> + <option value="comma">Comma</option> + </param> + <param name="genes" type="text" value="" label="Comma-separated list of genes to include in signature" + help="Comma-separated list of genes to include in signature, eg "ZNF454,GAPDH,LAIR1,ACAD9,CHTOP"" /> + <param name="threshold" type="float" value="20.0" label="Threshold to keep a proposed gene in the effective signature" + help="signature gene that are not expressed in at least this percentage of cells will not be kept to compute the effective signature" /> + </inputs> + <outputs> + <data name="pdf" format="pdf" label="Plots from ${on_string}" /> + <data name="output" format="tabular" label="signature scores from ${on_string}" /> + <data name="stats" format="tabular" label="genes statistics in ${on_string}" /> + </outputs> + <tests> + <test> + <param name="input" value="gene_filtered_input.tsv" ftype="txt"/> + <param name="sep" value='tab' /> + <param name="genes" value="ZNF454,GAPDH,LAIR1,ACAD9,CHTOP" /> + <output name="pdf" file="signature.pdf" ftype="pdf" compare="sim_size" delta="200" /> + <output name="output" file="signature.tsv" ftype="tabular"/> + <output name="stats" file="gene_stats.tsv" ftype="tabular"/> + </test> + </tests> + <help> + +**What it does** + +The tools takes a table of _normalized_ gene expression values (rows) in single cell RNAseq library sequencing (columns) +and a comma-separated list of genes, and returns in a table the geometric mean of expression of these +genes for each cell/library. This geometric mean is considered as the score for the genes signature for a given cell/library. + + +**Inputs** + +A table of comma (csv) or tabulation (tsv) separated values. +Gene names should be in the first column and cell names should be in the first row. +Note that in a number of a csv files, header of the gene column is omitted, resulting in +a first row with one item less than in other rows. This is handled by the tool that +recognises this situation. + +A comma separated list of genes desired as contributing to the signature score of the cell/library + +The expression threshold for signature genes (the aforementioned gene list) to be effectively +taken into account to compute signature scores. By default, genes are taken into account if +they are expressed in at least 20% of cells/libraries + +** Outputs ** +The tools returns a table "signature scores" that contains + +* the cell/library identifier +* the signature score for this identifier +* the rate of the signature score, ie whether it is higher (HIGH) or lower (LOW) that the average signature score in all cells/libraries +* the number of genes detected in the cell/library +* the total number of aligned reads in the cell/library + + </help> + <citations> + <citation type="bibtex"> + @Manual{, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2014}, + url = {http://www.R-project.org/}, + } + </citation> + </citations> +</tool>
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gene_stats.tsv Mon Jun 24 13:40:08 2019 -0400 @@ -0,0 +1,43 @@ +gene mean SD Variance Percentage_Detection +ZNF454 0.922860300134436 3.46176149331655 11.9837926366092 7 +ACAD9 3.21757044598762 6.03354965169885 36.4037213995154 23 +LAIR1 5.14073096940228 7.67228689265092 58.8639861631431 32 +GAPDH 17.6727587681588 4.98787838098139 24.8789307434615 93 +CHTOP 4.95213087424945 6.69826385145611 44.8667386237237 37 +BAI3 1.40915813751371 4.12336508696455 17.0021396403981 11 +FAM63B 2.44551578150534 5.13806432586195 26.3997050166952 20 +TMEM132C 0.822592049405892 3.30504805041197 10.923342615532 6 +CNOT10 1.35116986328472 4.15039762997951 17.2258004869395 10 +NCAPH2 2.09924645828476 4.9160781393554 24.167824272248 16 +COQ7 1.81109934358668 4.59246883791874 21.0907700272547 14 +CDKN2A 3.14847608508595 5.97643617835182 35.7177893939125 22 +SIN3A 4.00526302988229 6.32973969419456 40.0656045962622 30 +SLC15A4 2.6490939213568 5.36541152568955 28.7876408400022 21 +BPHL 1.8725575575664 4.76402741808616 22.6959572402767 14 +H3F3C 2.69750936927498 4.35063868472497 18.9280569650254 29 +ISOC2 1.35653919273248 4.13500630208934 17.0982771183186 10 +PARVB 2.21851609168455 4.93366814223579 24.3410813377123 18 +CYTH4 3.15940473609362 6.446078714101 41.5519307883859 20 +ITGB1 5.73749610754924 6.96917363119079 48.569381101685 42 +TBC1D15 2.64803303609142 5.30241142820209 28.1155669539282 21 +SLC4A5 1.85823253159878 4.89934798569655 24.0036106849488 13 +CLPB 1.20688850619883 3.94292278228986 15.5466400671004 9 +RABL5 3.2703554589894 5.97444372569743 35.6939778315254 24 +C3orf62 2.21928690308303 5.23726221704512 27.4289155300884 16 +ZFHX4 2.71178769759176 5.4146903421813 29.3188715017114 21 +AKT1 3.4403135511678 6.3127111628083 39.8503222250445 24 +ZNF780B 1.00358719015216 3.48539676962484 12.1479906417113 8 +RNF185 1.46932887276529 4.13672007089318 17.1124529449304 12 +SMYD3 1.11766527425951 3.45565630150241 11.9415604741133 10 +CCT4 5.58991399452656 7.04582138621306 49.6435990064173 40 +OBFC2B 2.56257992189769 5.37946105314442 28.9386012222977 19 +APIP 1.48573169701519 4.42462910898839 19.5773427521074 11 +CHFR 3.2793294035845 6.00856983357212 36.1029114449129 24 +PSPH 3.45898367616203 5.82691731716674 33.9529654210976 27 +MYO7A 2.08943827729343 5.13674406841985 26.3861396244465 15 +DUSP14 1.34957110029642 4.17409370205428 17.4230582335292 10 +LSAMP 5.40538485683009 7.20340373028298 51.8890253014547 38 +IFRD2 1.56542610972806 4.50279575682847 20.2751696277125 11 +PSMC3 6.17973106872374 7.13113762893614 50.8531238828289 45 +MUM1 3.59113963428165 6.20728875372071 38.5304336720676 26 +DIAPH2 2.45173786931265 5.19109016758801 26.9474171280289 19
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/signature.tsv Mon Jun 24 13:40:08 2019 -0400 @@ -0,0 +1,101 @@ +cell score rate nGenes total_counts +1001000235.G10 3.91067297444198 LOW 5 77.9909221954615 +1001000174.B1 2.10477034315281 LOW 12 168.400445734346 +1001000177.C5 17.3373396414863 HIGH 11 159.937195211458 +1001000182.H4 4.10738125088592 LOW 14 192.634771492111 +1001000175.A2 8.44220431060683 HIGH 8 114.350084817451 +1001000230.B11 8.55876574549129 HIGH 5 79.5774386257488 +1001000237.E9 7.28301224420602 HIGH 15 201.765971829942 +1001000173.E10 2.10337163126214 LOW 15 201.227911442868 +1001000241.E7 8.19202003890676 HIGH 13 194.63692225468 +1001000249.A3 7.86252533808187 HIGH 13 186.873952274257 +1001000179.A7 8.17510090773342 HIGH 7 110.931451554681 +1001000247.F11 6.59043743407966 HIGH 14 155.769944463327 +1001000240.D9 3.96999174048949 LOW 5 73.5405420337799 +1001000178.G5 2.09541561385605 LOW 5 71.3112721859375 +1001000249.E3 2.09214340332731 LOW 10 132.36632558755 +1001000174.H6 7.66677807322868 HIGH 17 226.760822446058 +1001000181.F8 2.10709400933102 LOW 5 67.2424519090522 +1001000177.A10 7.59091943449932 HIGH 16 204.694215735197 +1001000235.D2 7.18077413419506 HIGH 10 143.430575780825 +1001000240.G10 3.96828356565184 LOW 4 68.9408902670553 +1001000248.E7 3.79680269738866 LOW 7 106.704357399606 +1001000012.B6 2.09064719814766 LOW 8 115.939391977434 +1001000179.H4 3.86977998672059 LOW 5 78.7940286817071 +1001000230.C1 3.84572709101563 LOW 9 129.591633959121 +1001000175.H9 2.10863142203113 LOW 8 114.115259209976 +1001000241.G11 4.19313861629077 LOW 8 117.738892714724 +1001000174.G2 2.08036012270756 LOW 4 69.3580162624446 +1001000252.C6 4.15779109562327 LOW 11 158.806427511089 +1001000186.D11 4.25199167750475 LOW 6 90.5743065128312 +1001000239.G7 1 LOW 3 53.8312977258561 +1001000258.G11 2.09764420198528 LOW 4 68.8136405730902 +1001000264.A1 1 LOW 3 50.3162837331849 +1001000174.A10 7.98562122525758 HIGH 15 208.015032616852 +1001000255.E9 2.10073269844653 LOW 11 149.820225707172 +1001000242.B5 12.5164936079671 HIGH 10 122.074396411925 +1001000179.F3 2.05845744438262 LOW 9 135.996997725798 +1001000185.F9 3.53206364846174 LOW 17 224.09943144577 +1001000267.F8 4.32495829491009 LOW 9 125.301684349634 +1001000183.G10 7.53706170420164 HIGH 16 211.36350992629 +1001000247.E7 4.21579990374917 LOW 6 77.4072007755929 +1001000031.A2 2.11149638317858 LOW 4 51.7593186999109 +1001000271.B1 7.60942105665243 HIGH 11 152.809396775473 +1001000187.G6 4.22100958480346 LOW 7 114.257745856889 +1001000236.C6 8.36290290942702 HIGH 13 182.294634787411 +1001000238.C12 4.28963088394561 LOW 5 81.4960507923261 +1001000187.D6 2.09205283585927 LOW 11 155.323668089666 +1001000235.E10 7.64017123265904 HIGH 19 234.213178918161 +1001000036.C1 2.08230439607207 LOW 3 54.49819871696 +1001000253.H2 4.22409105033966 LOW 3 48.2453069486213 +1001000231.C2 4.25016840396507 LOW 12 170.278111490419 +1001000178.C10 8.57335954405515 HIGH 5 87.8940017030181 +1001000267.C1 3.58526739312882 LOW 13 175.964893632737 +1001000180.E4 4.06196578978747 LOW 7 97.0308781523434 +1001000173.E5 3.80026246037548 LOW 17 222.574071045438 +1001000179.F5 2.08540127056093 LOW 6 87.2394180794519 +1001000245.G11 3.96613100383082 LOW 9 119.008229357935 +1001000185.D5 7.60094833484929 HIGH 19 241.72067947581 +1001000012.A7 2.09661417545486 LOW 7 109.035996567905 +1001000010.B4 1 LOW 6 92.1067421506125 +1001000265.D11 4.29447112205169 LOW 12 175.219921893114 +1001000032.F1 2.09223901638632 LOW 9 119.387480482008 +1001000036.H9 2.08844173437915 LOW 8 119.186237761962 +1001000245.B3 1 LOW 4 69.2244101728074 +1001000185.A8 2.11293130373846 LOW 1 19.9315700120185 +1001000178.C6 4.23791793615157 LOW 7 111.69838561164 +1001000037.F10 2.10922856911855 LOW 8 101.146171233581 +1001000245.H4 6.5679063729165 HIGH 13 173.084715147083 +1001000012.B10 2.09828074476469 LOW 6 92.9678072935722 +1001000245.F2 2.1045748932988 LOW 8 108.412182761842 +1001000249.G2 2.07592481246152 LOW 7 111.6056344755 +1001000187.E11 4.30893139486157 LOW 4 63.8811927807031 +1001000266.A4 4.17632344655677 LOW 11 148.326980006354 +1001000266.G4 1.91469043406119 LOW 5 82.4573255689917 +1001000179.E3 2.06171189260098 LOW 3 55.0040683671297 +1001000178.C11 2.06549770393762 LOW 4 69.5597643920484 +1001000031.D12 2.08201588883004 LOW 5 81.3897422581072 +1001000037.D6 2.11095373452809 LOW 7 91.1467349085529 +1001000250.G2 2.10589657212701 LOW 17 195.818819521216 +1001000018.F11 4.17326263868245 LOW 5 84.6775441025479 +1001000175.F9 6.93134111715486 HIGH 21 257.740742874514 +1001000254.G1 2.06651359939214 LOW 7 113.394815959517 +1001000264.F12 3.67700474779531 LOW 7 92.16422172654 +1001000183.B3 2.10584413277124 LOW 9 125.510633274999 +1001000241.E6 8.09736172849892 HIGH 7 101.593090677913 +1001000183.E6 3.97527469176937 LOW 20 255.286657072729 +1001000181.F10 4.15633527864141 LOW 9 126.332552155196 +1001000176.B1 8.6015158477975 HIGH 11 158.025963389679 +1001000235.B7 4.29946403956211 LOW 10 151.487837590348 +1001000231.D12 4.28333021736497 LOW 10 135.454071983989 +1001000230.E7 4.14245296694746 LOW 13 178.515450700942 +1001000186.H6 4.14142724208693 LOW 6 93.3559602476448 +1001000258.H5 4.16335531296651 LOW 14 202.833522288167 +1001000237.H10 4.29135621546477 LOW 8 115.167490201189 +1001000231.B7 7.53675077824779 HIGH 22 287.069690968706 +1001000270.H8 4.25976367333017 LOW 9 123.795397333079 +1001000240.G1 2.05340984469825 LOW 3 50.5493043119434 +1001000177.D11 1.9045680155221 LOW 4 58.2921120403071 +1001000185.D3 7.84432752410517 HIGH 20 253.47465888226 +1001000238.B5 2.11167510212014 LOW 3 47.7358316527366 +1001000174.E10 4.00541245776673 LOW 11 143.710233042848