Mercurial > repos > galaxyp > lfq_protein_quant
changeset 0:bb199421f731 draft default tip
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/lfq_protein_quant commit 26ff08776f90f96646598a19cfcf57d42aa4a43b
| author | galaxyp |
|---|---|
| date | Tue, 02 Oct 2018 16:30:33 -0400 |
| parents | |
| children | |
| files | lfq_protein_quant.xml quantitation.r test-data/meta.tab test-data/moff.tab test-data/quantitation.tsv test-data/summarised_proteins.tsv |
| diffstat | 6 files changed, 2105 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/lfq_protein_quant.xml Tue Oct 02 16:30:33 2018 -0400 @@ -0,0 +1,98 @@ +<tool id="lfq_protein_quant" name="Label free protein" version="1.0"> + <description> + summarisation and quantitation + </description> + <requirements> + <requirement type="package" version="2.2.0">bioconductor-msnbase</requirement> + <requirement type="package" version="2.12.0">bioconductor-mzr</requirement> + <requirement type="package" version="1.2.1">r-tidyverse</requirement> + <requirement type="package" version="1.1_18_1">r-lme4</requirement> + <requirement type="package" version="0.1.0">r-furrr</requirement> + <requirement type="package" version="0.7.5">r-msqrob</requirement> + </requirements> + <command detect_errors="exit_code"> + <![CDATA[ + ln -s '${moff_file}' moff.tsv && + ln -s '${meta_file}' meta.tsv && + Rscript '$__tool_directory__/quantitation.r' + moff.tsv + meta.tsv + $only_summarisation + \${GALAXY_SLOTS:-4} + ]]> + </command> + <inputs> + <param name="moff_file" type="data" format="tabular" + label="Peptide summary output from moFF or other peptide quantitation tools" /> + <param name="meta_file" type="data" format="tabular" label="Metadata with sample names and condition" /> + <param name="only_summarisation" type="boolean" truevalue="1" falsevalue="0" + label="Only summarisiation" help="This option will deactivate the quantifiation of data." /> + + </inputs> + <outputs> + <data format="tabular" name="quant" from_work_dir="quantitation.tsv" label="${tool.name} on ${on_string}: quantification"> + <filter>not only_summarisation</filter> + </data> + <data format="tabular" name="summary" from_work_dir="summarised_proteins.tsv" label="${tool.name} on ${on_string}: summary"/> + </outputs> + <tests> + <test> + <param name="moff_file" value="moff.tab"/> + <param name="meta_file" value="meta.tab"/> + <param name="only_summerisation" value="true"/> + <output name="quant" file="quantitation.tsv" /> + <output name="summary" file="summarised_proteins.tsv" /> + </test> + </tests> + <help> + <![CDATA[ +**What it does** + +Protein summarisation and label free quantitation. + +---- + + **Inputs** + + - Quantification input: Tabular file with the summary output from moFF or in case other tools were used for peptide quantitation with the following columns: 'peptides' containing the amino acid sequence; 'prot' with the Uniprot Accession and then one column per sample with the quantitation values. + + :: + + peptides prot sample1 sample2 + AAABDEK B9DM54 1809446 563862 + TELATASDR Q9CFE8 294282 457023 + AMGLATK P85660 194023 428277 + ... + ... + + + - Metadata input: Separate columns with sample names, condition and optionally also lab and machine. Sample names must match exactly the sample names from the peptide quantitation columns in the previous tabular file. + + + :: + + sample condition + sample1 healthy + sample2 disease + ... + ... + + + **Options** + + - Summarisation only: Summarisation is done through robust regression to take also the peptide effect into account. In case only sample column is provided median protein intensity is calculated. + - Summarisation and quantification: Relative quantification is performed according to the information provided in the metadata file. + + + **Outputs** + + - Summarised protein output: Uniprot accession and quantitation values per sample and protein + - Quantification output: Uniprot accession, comparison between conditions, fold change, p-value, q-value + + ]]> + </help> + <citations> + <citation type="doi">10.1074/mcp.M115.055897</citation> + <citation type="doi">10.1093/bioinformatics/btr645</citation> + </citations> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/quantitation.r Tue Oct 02 16:30:33 2018 -0400 @@ -0,0 +1,452 @@ +################################# +library(tidyverse) +library(furrr) +library(lme4) +library(MSnbase) +library(MSqRob) + +##Import and preprocess data +############################ +MSnSet2df = function(msnset){ + ## Converts Msnset to a tidy dataframe + ## Always creates feature and vector column so these shouldn't be defined by user. + ## convenient for downstream analysis steps. + if(any(c("sample", "feature", "expression") %in% c(colnames(fData(msnset)),colnames(pData(msnset))))){ + stop("Column names in the \"fData\" or \"pData\" slot of the \"msnset\" object cannot be named + \"sample\", \"feature\" or \"expression\". Please rename these columns before running the analysis.") + } + + dt <- as.data.frame(Biobase::exprs(msnset)) %>% mutate(feature = rownames(.)) %>% + gather(sample, expression, - feature, na.rm=TRUE) + dt <- fData(msnset) %>% mutate(feature = rownames(.)) %>% left_join(dt,. , by = 'feature') + dt <- pData(msnset) %>% mutate(sample = rownames(.)) %>% left_join(dt,. , by = 'sample') + as_data_frame(dt) +} + +## robust summarisation +do_robust_summaristion = function(msnset, group_var = Proteins, keep_fData_cols = NULL, nIter = 20, + sum_fun = summarizeRobust){ + ## TODO use funture_map instead of mutate to speed up + ## Uses assumption that featureNames and sampleNames exist in every msnset + ## Can also be used for multiple rounds of normalization, e.g. first from PSMs to peptides, then from peptides to proteins + system.time({## Time how long it takes + group_var <- enquo(group_var) ;#group_var = quo(Proteins) + ## Make tidy dataframe from Msnset + df <- MSnSet2df(msnset) + ## Do summarisision according defined groups + dt <- filter(df, !is.na(expression)) %>% group_by(!!group_var) %>% + mutate(expression = sum_fun(expression, feature, sample, nIter = nIter)) %>% + dplyr::select(!!group_var, sample, expression) %>% + ## collapse to one value per group + distinct + ## Construct an Msnset object from dataframe + dt_exprs <- spread(dt, sample, expression) %>% ungroup + exprs_data <- dplyr::select(dt_exprs, - !!group_var) %>% as.matrix + rownames(exprs_data) <- as.character(pull(dt_exprs, !!group_var)) + + fd <- dplyr::select(dt_exprs,!!group_var) + + ##Select the group variable and all variables you want to keep + if (!is.null(keep_fData_cols)){ + fd_ext <- dplyr::select(df, !!group_var, one_of(keep_fData_cols)) %>% distinct + if(nrow(fd)!=nrow(fd_ext)){ + stop("Values in the \"group_var\" column can only correspond to a single value in the \"keep_fData_cols\" column.") + } + fd <- left_join(fd,fd_ext) + } + + fd <- as.data.frame(fd) + rownames(fd) <- as.character(pull(fd, !!group_var)) + out <- MSnSet(exprs_data, fData = AnnotatedDataFrame(fd) , pData = pData(msnset)[colnames(exprs_data),,drop = FALSE]) + }) %>% print + out +} + +summarizeRobust <- function(expression, feature, sample, nIter=100,...) { + ## Assumes that intensities mx are already log-transformed + ## characters are faster to construct and work with then factors + feature <- as.character(feature) + ##If there is only one 1 peptide for all samples return expression of that peptide + if (length(unique(feature)) == 1L) return(expression) + sample <- as.character(sample) + ## modelmatrix breaks on factors with 1 level so make vector of ones (will be intercept) + if (length(unique(sample)) == 1L) sample = rep(1,length(sample)) + + ## sum contrast on peptide level so sample effect will be mean over all peptides instead of reference level + X = model.matrix(~ -1 + sample + feature,contrasts.arg = list(feature = 'contr.sum')) + ## MasS::rlm breaks on singulare values. + ## check with base lm if singular values are present. + ## if so, these coefficients will be zero, remove this collumn from model matrix + ## rinse and repeat on reduced modelmatrx till no singular values are present + repeat { + fit = .lm.fit(X,expression) + id = fit$coefficients != 0 + X = X[ , id, drop =FALSE] + if (!any(!id)) break + } + ## Last step is always rlm + fit = MASS::rlm(X,expression,maxit = nIter,...) + ## Only return the estimated effects effects as summarised values + sampleid = seq_along(unique(sample)) + return(X[,sampleid,drop = FALSE] %*% fit$coefficients[sampleid]) +} + + + + + + + + +## mixed models +############### +setGeneric ( + name= "getBetaB", + def=function(model,...){standardGeneric("getBetaB")} +) + +.getBetaBMermod = function(model) { + betaB <- c(as.vector(getME(model,"beta")),as.vector(getME(model,"b"))) + names(betaB) <- c(colnames(getME(model,"X")),rownames(getME(model,"Zt"))) + betaB +} +setMethod("getBetaB", "lmerMod", .getBetaBMermod) + +.getBetaBGlm = function(model) + model$coefficients + +setGeneric ( + name= "getVcovBetaBUnscaled", + def=function(model,...){standardGeneric("getVcovBetaBUnscaled")} +) + +setMethod("getBetaB", "glm", .getBetaBGlm) + +.getVcovBetaBUnscaledMermod = function(model){ + ## TODO speed up (see code GAM4) + p <- ncol(getME(model,"X")) + q <- nrow(getME(model,"Zt")) + Ct <- rbind2(t(getME(model,"X")),getME(model,"Zt")) + Ginv <- solve(tcrossprod(getME(model,"Lambda"))+Diagonal(q,1e-18)) + vcovInv <- tcrossprod(Ct) + vcovInv[((p+1):(q+p)),((p+1):(q+p))] <- vcovInv[((p+1):(q+p)),((p+1):(q+p))]+Ginv + + solve(vcovInv) +} + +setMethod("getVcovBetaBUnscaled", "lmerMod", .getVcovBetaBUnscaledMermod) + +.getVcovBetaBUnscaledGlm = function(model) + ## cov.scaled is scaled with the dispersion, "cov.scaled" is without the dispersion! + ## MSqRob::getSigma is needed because regular "sigma" function can return "NaN" when sigma is very small! + ## This might cause contrasts that can be estimated using summary() to be NA with our approach! + summary(model)$cov.scaled/MSqRob::getSigma(model)^2 + +setMethod("getVcovBetaBUnscaled", "glm", .getVcovBetaBUnscaledGlm) + +## Estimate pvalues contrasts +contrast_helper = function(formula, msnset, contrast = NULL){ + ## Gives back the coefficients you can use to make contrasts with given the formula and dataset + ## If a factor variable is specified (that is present in the formula) all the possible contrasts + ## within this variable are returned + contrast <- enquo(contrast) ;#contrast = quo(condition) + df <- MSnSet2df(msnset) + all_vars <- formula %>% terms %>% delete.response %>% all.vars + names(all_vars) <- all_vars + df[,all_vars] <- map2_dfr(all_vars,df[,all_vars],paste0) + coefficients <- c("(Intercept)", df %>% dplyr::select(all_vars) %>% unlist %>% unique %>% as.character) + if (contrast != ~NULL) { + c <- pull(df,!! contrast) %>% unique %>% sort %>% as.factor + comp <- combn(c,2,simplify = FALSE) + ## condIds = map(comp,~which( coefficients %in% .x)) + ## L = rep(0,length(coefficients)) + ## L = sapply(condIds,function(x){L[x]=c(-1,1);L}) + ## rownames(L) = coefficients + ## colnames(L) = map_chr(comp, ~paste(.x,collapse = '-')) + condIds <- map(comp, ~which(coefficients %in% .x)) + L <- rep(0,nlevels(c)) + L <- sapply(comp,function(x){L[x]=c(-1,1);L}) + rownames(L) <- levels(c) + colnames(L) <- map_chr(comp, ~paste(rev(.x),collapse = '-')) + L + } else coefficients +} + +setGeneric ( + name= "getXLevels", + def=function(model,...){standardGeneric("getXLevels")} +) + +.getXLevelsGlm = function(model) + map2(names(model$xlevels), model$xlevels, paste0) %>% unlist + +setMethod("getXLevels", "glm", .getXLevelsGlm) + +.getXLevelsMermod = function(model) + getME(model,"flist") %>% map(levels) %>% unlist %>% unname + +setMethod("getXLevels", "lmerMod", .getXLevelsMermod) + +contEst <- function(model, contrasts, var, df, lfc = 0){ + #TODO only contrast of random effect possible and not between fixed regression terms + betaB <- getBetaB(model) + vcov <- getVcovBetaBUnscaled(model) + coefficients <- names(betaB) + id <- coefficients %in% rownames(contrasts) + + coefficients <- coefficients[id] + vcov <- vcov[id,id] + betaB <- betaB[id] + + xlevels <- getXLevels(model) + id <- !apply(contrasts,2,function(x){any(x[!(rownames(contrasts) %in% xlevels)] !=0)}) + contrasts <- contrasts[coefficients, id, drop = FALSE] + ## If no contrasts could be found, terminate + if (is.null(colnames(contrasts))) return(new_tibble(list())) + + se <- sqrt(diag(t(contrasts)%*%vcov%*%contrasts)*var) + logFC <- (t(contrasts)%*%betaB)[,1] + + ### Addition to allow testing against another log FC (lfc) + ### See https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2654802/ + + lfc <- abs(lfc) + aest <- abs(logFC) + + Tval <- setNames(rep(0, length(logFC)),names(se)) + tstat.right <- (aest - lfc)/se + tstat.left <- (aest + lfc)/se + pval <- pt(tstat.right, df = df, lower.tail = FALSE) + + pt(tstat.left, df = df, lower.tail = FALSE) + tstat.right <- pmax(tstat.right, 0) + fc.up <- (logFC >= lfc) + fc.up[is.na(fc.up)] <- FALSE + fc.down <- (logFC < -lfc) + fc.down[is.na(fc.down)] <- FALSE + Tval[fc.up] <- tstat.right[fc.up] + Tval[fc.down] <- -tstat.right[fc.down] + Tval[is.na(logFC)] <- NA + + new_tibble(list(contrast = colnames(contrasts), + logFC = logFC, + se = se, + t = Tval, + df = rep(df, length(se)), + pvalue = pval)) +} + +do_lmerfit = function(df, form, nIter = 10, tol = 1e-6, control = lmerControl(calc.derivs = FALSE)){ + fit <- lmer(form, data = df, control = control) + ##Initialize SSE + res <- resid(fit) + ## sseOld=sum(res^2) + sseOld <- fit@devcomp$cmp['pwrss'] + while (nIter > 0){ + nIter = nIter-1 + fit@frame$`(weights)` <- MASS::psi.huber(res/(mad(res))) + fit <- refit(fit) + res <- resid(fit) + ## sse=sum(res^2) + sse <- fit@devcomp$cmp['pwrss'] + if(abs(sseOld-sse)/sseOld <= tol) break + sseOld <- sse + } + return(fit) +} + +calculate_df = function(df, model, vars){ + ## Get all the variables in the formula that are not defined in vars + form <- attributes(model@frame)$formula + vars_formula <- all.vars(form) + vars_drop <- vars_formula[!vars_formula %in% vars] + ## Sum of number of columns -1 of Zt mtrix of each random effect that does not involve a variable in vars_drop + mq <- getME(model,'q_i') + id <- !map_lgl(names(mq),~{any(stringr::str_detect(.x,vars_drop))}) + p <- sum(mq[id]) - sum(id) + ## Sum of fixed effect parameters that do not involve a variable in vars_drop + mx <- getME(model,'X') + id <- !map_lgl(colnames(mx),~{any(stringr::str_detect(.x,vars_drop))}) + p <- p + sum(id) + + ## n is number of sample because 1 protein defined per sample + n <- n_distinct(df$sample) + n-p +} + +do_mm = function(formulas, msnset, group_vars = feature,type_df = 'traceHat' + , contrasts = NULL, lfc = 0, p.adjust.method = "BH", max_iter = 20L + , squeeze_variance = TRUE + , control = lmerControl(calc.derivs = FALSE) + ## choose parallel = plan(sequential) if you don't want parallelisation + ## , parallel_plan = plan(cluster, workers = makeClusterPSOCK(availableCores())) + , parallel = TRUE, cores = availableCores() + ){ + if(!(type_df %in% c("conservative", "traceHat"))) + stop("Invalid input `type_df`.") + + system.time({## can take a while + if (parallel){ + cl <- makeClusterPSOCK(cores) + plan(cluster, workers = cl) + } else { + plan(sequential)} + + ## future::plan(parallel_plan,gc = TRUE) + formulas <- map(c(formulas), ~update(.,expression ~ . )) + group_vars <- enquo(group_vars) # group_var = quo(protein) + df <- MSnSet2df(msnset) + + ## Glm adds variable name to levels in catogorical (eg for contrast) + ## lme4 doesnt do this for random effect, so add beforehand + ## Ludger needs this for Hurdle + df = formulas %>% map(lme4:::findbars) %>% unlist %>% map_chr(all.vars) %>% unique %>% + purrr::reduce(~{mutate_at(.x,.y,funs(paste0(.y,.)))}, .init=df) + + cat("Fitting mixed models\n") + ## select only columns needed for fitting + df_prot <- df %>% + group_by_at(vars(!!group_vars)) %>% nest %>% + mutate(model = furrr::future_map(data,~{ + for (form in formulas){ + fit = try(do_lmerfit(.x, form, nIter = max_iter,control = control)) + if (class(fit) == "lmerMod") return(fit) + } + fit + })) + + ## Return also failed ones afterward + df_prot_failed <- filter(df_prot, map_lgl(model,~{class(.x) != "lmerMod"})) + df_prot <- filter(df_prot, map_lgl(model, ~{class(.x)=="lmerMod"})) + + if(nrow(df_prot) == 0) {print("No models could be fitted"); return(df_prot_failed)} + + df_prot <- + mutate(df_prot + , formula = map(model,~{attributes(.@frame)$formula}) + ## get trace hat df for squeezeVar + , df = map_dbl(model, ~getDf(.x)) + , sigma = map_dbl(model,~{MSqRob::getSigma(.x)})) %>% + ## Squeeze variance + bind_cols(as_data_frame(MSqRob::squeezeVarRob(.$sigma^2, .$df, robust = TRUE))) %>% + ## mutate(var_protein = ifelse(squeeze_variance,var.post,sigma^2), + mutate(var_protein = if (squeeze_variance) var.post else sigma^2, + df_post = df + df.prior + , df_protein = + if (type_df == "conservative") + ## Calculate df on protein level, assumption is that there is only one protein value/run, + map2_dbl(data, model,~calculate_df(.x,.y, vars = colnames(pData(msnset)))) + else if (type_df == "traceHat") + ## Alternative: MSqRob implementation with trace(Hat): + if(squeeze_variance) df_post else df + ) + + ## Calculate fold changes and p values for contrast + cat("Estimating p-values contrasts\n") + df_prot <- df_prot %>% + mutate(contrasts = furrr::future_pmap(list(model = model, contrasts = list(contrasts), + var = var_protein, + df = df_protein, lfc = lfc), contEst)) %>% + ## Calculate qvalues BH + select_at(vars(!!group_vars, contrasts)) %>% + unnest %>% + group_by(contrast) %>% + mutate(qvalue = p.adjust(pvalue, method = p.adjust.method)) %>% + group_by_at(vars(!!group_vars)) %>% nest(.key = contrasts) %>% + left_join(df_prot,.) + } + ) %>% print + if (parallel) stopCluster(cl) + bind_rows(df_prot,df_prot_failed) +} + +read_moff = function(moff,meta){ + print('START READING MOFF DATA') + set = readMSnSet2(moff, ecol = -c(1,2),fnames = 'peptide', + sep = '\t',stringsAsFactors = FALSE) + colnames(fData(set)) = c('peptide','protein') + pd = read_tsv(meta) %>% column_to_rownames('sample') %>% as.data.frame + + ## fix msnbase bug 1 + ## if there is only 1 sample. Msnbase doesn't name it + if (length(sampleNames(set) ==1)) + sampleNames(set) = rownames(pd) + + pData(set) = pd + ## fix msnbase bug 2 + ## bug in msnbase in summarisation (samplenames should be alphabetically) + sample_order = order(sampleNames(set)) + set = MSnSet(exprs(set)[,sample_order,drop = FALSE] + , fData = AnnotatedDataFrame(fData(set)) + , pData = AnnotatedDataFrame(pData(set)[sample_order,,drop = FALSE])) + print('END READING MOFF DATA') + set +} + +preprocess = function(set){ + print('START PREPROCESSING') + if (ncol(set) == 1){ + exprs(set)[0 == (exprs(set))] <- NA + set = log(set, base = 2) + ## keep smallest unique groups + groups2 <- smallestUniqueGroups(fData(set)$protein,split = ',') + sel <- fData(set)$protein %in% groups2 + set <- set[sel,] + } else { + ## normalisation + exprs(set)[0 == (exprs(set))] <- NA + set <- normalize(set, 'vsn') + ## keep smallest unique groups + groups2 <- smallestUniqueGroups(fData(set)$protein,split = ',') + sel <- fData(set)$protein %in% groups2 + set <- set[sel,] + ## remove peptides with less then 2 observations + sel <- rowSums(!is.na(exprs(set))) >= 2 + set <- set[sel] + } + print('END PREPROCESSING') + set +} + +summarise = function(set){ + print('START SUMMARISATION') + ## Summarisation + if (ncol(set) == 1){ + set = combineFeatures(set,fun="median", groupBy = fData(set)$protein,cv = FALSE) + } else { + ## set = combineFeatures(set,fun="robust", groupBy = fData(set)$protein,cv = FALSE) + set = do_robust_summaristion(set,protein) + } + exprs(set) %>% as.data.frame %>% rownames_to_column('protein') %>% write_tsv('summarised_proteins.tsv') + print('END SUMMARISATION') + set +} + +quantify = function(set, cpu = 0){ + print('START QUANTITATION') + if ((cpu == 0) | is.na(cpu)) cpu = availableCores() + print(cpu) + form = colnames(pData(set)) %>% paste0('(1|',.,')',collapse='+') %>% paste('~',.) %>% as.formula + contrasts <- contrast_helper(form, set, condition) + res = do_mm(formulas = form, set, group_vars = c(protein) + , contrasts = contrasts,type_df = 'traceHat', parallel = TRUE,cores = cpu) %>% + filter(!map_lgl(contrasts, is.null)) %>% + transmute(protein, contrasts) %>% unnest %>% + transmute(protein + , comparison = str_replace_all(contrast, 'condition', '') + , logFC,pvalue,qvalue) %>% + write_tsv('quantitation.tsv') + print('END QUANTITATION') +} + +args = commandArgs(trailingOnly=TRUE) +moff = args[1] +meta = args[2] +summarise_only = args[3] +cpu = strtoi(args[4]) + +res = read_moff(moff, meta) %>% + preprocess %>% + summarise +if (summarise_only != 1) + quantify(res, cpu) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/meta.tab Tue Oct 02 16:30:33 2018 -0400 @@ -0,0 +1,9 @@ +sample condition +sumIntensity_HEK.Ecoli_7.1_replicate1 ecoli_7 +sumIntensity_HEK.Ecoli_3.1_replicate2 ecoli_3 +sumIntensity_HEK.Ecoli_7.1_replicate2 ecoli_7 +sumIntensity_HEK.Ecoli_20.1_replicate2 ecoli_2 +sumIntensity_HEK.Ecoli_20.1_replicate1 ecoli_2 +sumIntensity_HEK.Ecoli_10.1_replicate1 ecoli_1 +sumIntensity_HEK.Ecoli_10.1_replicate2 ecoli_1 +sumIntensity_HEK.Ecoli_3.1_replicate1 ecoli_3
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/quantitation.tsv Tue Oct 02 16:30:33 2018 -0400 @@ -0,0 +1,1130 @@ +protein comparison logFC pvalue qvalue +A1L0T0 ecoli_2-ecoli_1 -9.39530454360043e-18 0.9999999967240936 1 +A1L0T0 ecoli_3-ecoli_1 -3.5591253620981224e-17 0.9999999875902251 1 +A1L0T0 ecoli_7-ecoli_1 -2.3165346387522672e-17 0.9999999919228264 1 +A1L0T0 ecoli_3-ecoli_2 -2.6195949077380796e-17 0.9999999908661314 1 +A1L0T0 ecoli_7-ecoli_2 -1.3770041843922244e-17 0.9999999951987328 1 +A1L0T0 ecoli_7-ecoli_3 1.242590723345855e-17 0.9999999956673987 1 +O00139 ecoli_2-ecoli_1 0.0018448291421766516 0.9958340324121795 1 +O00139 ecoli_3-ecoli_1 1.2321715267791753 0.009783597971314853 0.1081950834474819 +O00139 ecoli_7-ecoli_1 0.29765360883577596 0.41270893111828805 1 +O00139 ecoli_3-ecoli_2 1.2303266976369986 0.00985050554661475 0.07870172578796708 +O00139 ecoli_7-ecoli_2 0.2958087796935993 0.41545686820524286 1 +O00139 ecoli_7-ecoli_3 -0.9345179179433993 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ecoli_7-ecoli_3 1.745649327476704e-20 0.9999999999876837 1 +P0ADE8 ecoli_2-ecoli_1 0.39365087405649857 0.3703018663623666 1 +P0ADE8 ecoli_3-ecoli_1 1.8832216944827607 0.003319174213002223 0.0564017163422299 +P0ADE8 ecoli_7-ecoli_1 0.6772549203132548 0.14605600562537666 1 +P0ADE8 ecoli_3-ecoli_2 1.489570820426262 0.010047028823995798 0.07870172578796708 +P0ADE8 ecoli_7-ecoli_2 0.2836040462567562 0.5117891910441578 1 +P0ADE8 ecoli_7-ecoli_3 -1.205966774169506 0.0243508673003319 0.32873670855448067 +P0AE08 ecoli_2-ecoli_1 -0.41004515870387526 0.37380141381569126 1 +P0AE08 ecoli_3-ecoli_1 1.3385455875682537 0.0189856292163143 0.14277193170668354 +P0AE08 ecoli_7-ecoli_1 0.4087329433487947 0.3752358667117934 1 +P0AE08 ecoli_3-ecoli_2 1.748590746272129 0.005740525143049239 0.05680098562596089 +P0AE08 ecoli_7-ecoli_2 0.81877810205267 0.10195310450223499 0.6389061215473393 +P0AE08 ecoli_7-ecoli_3 -0.929812644219459 0.07058687125223888 0.40210624801165656 +P0AEE5 ecoli_2-ecoli_1 2.2378089574830433e-20 0.9999999999802648 1 +P0AEE5 ecoli_3-ecoli_1 7.954963159990891e-20 0.9999999999298452 1 +P0AEE5 ecoli_7-ecoli_1 -1.8889096014495172e-20 0.9999999999833418 1 +P0AEE5 ecoli_3-ecoli_2 5.717154202507848e-20 0.9999999999495806 1 +P0AEE5 ecoli_7-ecoli_2 -4.12671855893256e-20 0.9999999999636064 1 +P0AEE5 ecoli_7-ecoli_3 -9.843872761440408e-20 0.999999999913187 1 +P0AES9 ecoli_2-ecoli_1 -0.7684364129453012 0.2556416380753555 1 +P0AES9 ecoli_3-ecoli_1 1.5956232750590744 0.03883310715168509 0.22814450451614987 +P0AES9 ecoli_7-ecoli_1 0.47131342585573555 0.47113149233491536 1 +P0AES9 ecoli_3-ecoli_2 2.3640596880043754 0.007591577300200818 0.06487347874717062 +P0AES9 ecoli_7-ecoli_2 1.2397498388010368 0.08774395648780897 0.63197938142333 +P0AES9 ecoli_7-ecoli_3 -1.1243098492033388 0.11453880116718969 0.4810629649021967 +P0AFH8 ecoli_2-ecoli_1 -0.7985202767891957 0.01163997211998907 1 +P0AFH8 ecoli_3-ecoli_1 1.235049478976554 0.001453235262992271 0.045534704907091154 +P0AFH8 ecoli_7-ecoli_1 0.22794725632821536 0.3469215454709561 1 +P0AFH8 ecoli_3-ecoli_2 2.0335697557657495 9.605006497199952e-5 0.006019137404911969 +P0AFH8 ecoli_7-ecoli_2 1.0264675331174111 0.0036708420831749536 0.17252957790922283 +P0AFH8 ecoli_7-ecoli_3 -1.0071022226483386 0.0040247129596145065 0.08451897215190464 +P0DMV8, P0DMV9 ecoli_2-ecoli_1 0.11203390195656374 0.3547656064135441 1 +P0DMV8, P0DMV9 ecoli_3-ecoli_1 0.07032663121477964 0.5528827312682907 1 +P0DMV8, P0DMV9 ecoli_7-ecoli_1 -0.19302763263276362 0.13346561207899982 1 +P0DMV8, P0DMV9 ecoli_3-ecoli_2 -0.0417072707417841 0.7223641032784058 1 +P0DMV8, P0DMV9 ecoli_7-ecoli_2 -0.3050615345893274 0.032730292754420784 0.3845809398644442 +P0DMV8, P0DMV9 ecoli_7-ecoli_3 -0.26335426384754324 0.0549199163371715 0.38966496981959714 +P0DN79, P35520 ecoli_2-ecoli_1 1.3185100439131234e-18 0.9999999994995394 1 +P0DN79, P35520 ecoli_3-ecoli_1 7.759966952577495e-18 0.9999999970545864 1 +P0DN79, P35520 ecoli_7-ecoli_1 6.570068969119878e-18 0.9999999975062304 1 +P0DN79, P35520 ecoli_3-ecoli_2 6.441456908664371e-18 0.999999997555047 1 +P0DN79, P35520 ecoli_7-ecoli_2 5.251558925206755e-18 0.9999999980066909 1 +P0DN79, P35520 ecoli_7-ecoli_3 -1.1898979834576164e-18 0.9999999995483562 1 +P10809 ecoli_2-ecoli_1 0.14892003195701303 0.2477175359502992 1 +P10809 ecoli_3-ecoli_1 -0.09484185918155355 0.44738107650928416 1 +P10809 ecoli_7-ecoli_1 -0.13657941040612798 0.28517105485353955 1 +P10809 ecoli_3-ecoli_2 -0.24376189113856658 0.07917650933327834 0.2756515510121542 +P10809 ecoli_7-ecoli_2 -0.285499442363141 0.04760532353913416 0.4710421487030117 +P10809 ecoli_7-ecoli_3 -0.04173755122457441 0.7332944099341776 1 +P11172 ecoli_2-ecoli_1 0.028476722979062417 0.8969691643859677 1 +P11172 ecoli_3-ecoli_1 -0.31172923224499083 0.18366383000950393 0.6640153854189758 +P11172 ecoli_7-ecoli_1 0.03343932189768808 0.8791691158474961 1 +P11172 ecoli_3-ecoli_2 -0.3402059552240533 0.15121890617078557 0.4512564184144077 +P11172 ecoli_7-ecoli_2 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ecoli_3-ecoli_1 -8.561234812787174e-23 0.9999999999996393 1 +P13667 ecoli_7-ecoli_1 2.04911594396969e-22 0.9999999999991367 1 +P13667 ecoli_3-ecoli_2 -5.1248652366772943e-23 0.9999999999997841 1 +P13667 ecoli_7-ecoli_2 2.3927529015806777e-22 0.9999999999989919 1 +P13667 ecoli_7-ecoli_3 2.905239425248407e-22 0.9999999999987759 1 +P13804 ecoli_2-ecoli_1 3.402848055486131e-20 0.9999999998871689 1 +P13804 ecoli_3-ecoli_1 1.326483693082478e-19 0.9999999995601665 1 +P13804 ecoli_7-ecoli_1 1.5726412288418334e-19 0.9999999994785459 1 +P13804 ecoli_3-ecoli_2 9.861988875338652e-20 0.9999999996729976 1 +P13804 ecoli_7-ecoli_2 1.2323564232932205e-19 0.9999999995913771 1 +P13804 ecoli_7-ecoli_3 2.461575357593553e-20 0.9999999999183795 1 +P13861 ecoli_2-ecoli_1 7.898156688176108e-15 0.9999998305798139 1 +P13861 ecoli_3-ecoli_1 -3.255392822691321e-15 0.9999999301698763 1 +P13861 ecoli_7-ecoli_1 5.594803178204473e-15 0.9999998799881247 1 +P13861 ecoli_3-ecoli_2 -1.1153549510867428e-14 0.9999997607496902 1 +P13861 ecoli_7-ecoli_2 -2.3033535099716338e-15 0.9999999505916892 1 +P13861 ecoli_7-ecoli_3 8.850196000895795e-15 0.9999998101580011 1 +P15639 ecoli_2-ecoli_1 -0.031479277614269024 0.783611645488742 1 +P15639 ecoli_3-ecoli_1 -0.2723511049861437 0.046058914464484756 0.23919768442343972 +P15639 ecoli_7-ecoli_1 -0.20949970260314466 0.10289644559377321 1 +P15639 ecoli_3-ecoli_2 -0.24087182737187465 0.06876479384099564 0.2693287758772329 +P15639 ecoli_7-ecoli_2 -0.17802042498887563 0.15395088652984723 0.80396574076698 +P15639 ecoli_7-ecoli_3 0.06285140238299902 0.5870076883219976 1 +P17858 ecoli_2-ecoli_1 -0.011553741218472635 0.9236488350094014 1 +P17858 ecoli_3-ecoli_1 -0.2839105905814028 0.04707614001950676 0.23919768442343972 +P17858 ecoli_7-ecoli_1 -0.1122855810961231 0.3677178012449065 1 +P17858 ecoli_3-ecoli_2 -0.27235684936293014 0.054193578178274394 0.2276900129433772 +P17858 ecoli_7-ecoli_2 -0.10073183987765047 0.41610895234339174 1 +P17858 ecoli_7-ecoli_3 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ecoli_3-ecoli_1 -3.1683433847600785e-15 0.9999998841803812 1 +Q14676 ecoli_7-ecoli_1 -8.432043490624875e-16 0.9999999691764451 1 +Q14676 ecoli_3-ecoli_2 -4.246993354150698e-15 0.9999998447500502 1 +Q14676 ecoli_7-ecoli_2 -1.921854318453108e-15 0.9999999297461142 1 +Q14676 ecoli_7-ecoli_3 2.325139035697591e-15 0.999999915003936 1 +Q14974 ecoli_2-ecoli_1 0.13132829843155394 0.3184441894804006 1 +Q14974 ecoli_3-ecoli_1 -0.06428728240742473 0.6148384561371222 1 +Q14974 ecoli_7-ecoli_1 0.2143454939548933 0.12389494305020687 1 +Q14974 ecoli_3-ecoli_2 -0.19561558083897868 0.15438348380695663 0.45216079987951757 +Q14974 ecoli_7-ecoli_2 0.08301719552333936 0.5185736085706267 1 +Q14974 ecoli_7-ecoli_3 0.27863277636231804 0.05772814367697735 0.38966496981959714 +Q14978 ecoli_2-ecoli_1 0.2133015824760371 0.10570506922586277 1 +Q14978 ecoli_3-ecoli_1 -0.14648774196610614 0.24025013179064786 0.757957661997404 +Q14978 ecoli_7-ecoli_1 0.018911579853216964 0.8722823123121851 1 +Q14978 ecoli_3-ecoli_2 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ecoli_7-ecoli_2 -0.017101173689058405 0.8977942205435446 1 +Q15084 ecoli_7-ecoli_3 0.17204910261843412 0.22034744915295118 0.7436726408912102 +Q15120 ecoli_2-ecoli_1 0.20449047795300865 0.19869462630983062 1 +Q15120 ecoli_3-ecoli_1 0.9476924984202942 5.281232447973197e-4 0.02016545530288657 +Q15120 ecoli_7-ecoli_1 0.03925830721847262 0.7909222453570472 1 +Q15120 ecoli_3-ecoli_2 0.7432020204672856 0.001892706157917399 0.027371442899113155 +Q15120 ecoli_7-ecoli_2 -0.16523217073453603 0.28745868009804365 1 +Q15120 ecoli_7-ecoli_3 -0.9084341912018217 6.631282764408766e-4 0.04420017360902241 +Q15181 ecoli_2-ecoli_1 0.08232489810629717 0.4575361567140638 1 +Q15181 ecoli_3-ecoli_1 -0.11156345466488857 0.3222784834451747 0.8797126758352412 +Q15181 ecoli_7-ecoli_1 -0.06418846939768084 0.5591659723003437 1 +Q15181 ecoli_3-ecoli_2 -0.19388835277118574 0.10724816636676099 0.33813039561956004 +Q15181 ecoli_7-ecoli_2 -0.14651336750397803 0.20498743053879417 0.8905353073693035 +Q15181 ecoli_7-ecoli_3 0.04737498526720772 0.6645162460710475 1 +Q15233 ecoli_2-ecoli_1 -1.9343373243961214e-19 0.9999999997058784 1 +Q15233 ecoli_3-ecoli_1 -2.7870969255168805e-19 0.9999999995762138 1 +Q15233 ecoli_7-ecoli_1 -9.294651428479756e-20 0.9999999998586722 1 +Q15233 ecoli_3-ecoli_2 -8.527596011207594e-20 0.9999999998703355 1 +Q15233 ecoli_7-ecoli_2 1.0048721815481455e-19 0.9999999998472062 1 +Q15233 ecoli_7-ecoli_3 1.857631782668905e-19 0.9999999997175417 1 +Q16891 ecoli_2-ecoli_1 0 1 1 +Q16891 ecoli_3-ecoli_1 0 1 1 +Q16891 ecoli_7-ecoli_1 0 1 1 +Q16891 ecoli_3-ecoli_2 0 1 1 +Q16891 ecoli_7-ecoli_2 0 1 1 +Q16891 ecoli_7-ecoli_3 0 1 1 +Q3ZAQ7 ecoli_2-ecoli_1 1.3685994348410747e-21 0.9999999999953806 1 +Q3ZAQ7 ecoli_3-ecoli_1 -1.0181032694406608e-21 0.9999999999965636 1 +Q3ZAQ7 ecoli_7-ecoli_1 6.055199382090307e-22 0.9999999999979562 1 +Q3ZAQ7 ecoli_3-ecoli_2 -2.3867027042817355e-21 0.9999999999919442 1 +Q3ZAQ7 ecoli_7-ecoli_2 -7.6307949663204405e-22 0.9999999999974244 1 +Q3ZAQ7 ecoli_7-ecoli_3 1.6236232076496913e-21 0.9999999999945198 1 +Q5JTZ9 ecoli_2-ecoli_1 5.9904686183013606e-18 0.9999999989600005 1 +Q5JTZ9 ecoli_3-ecoli_1 -6.486772661405592e-17 0.9999999887383763 1 +Q5JTZ9 ecoli_7-ecoli_1 6.384256759841226e-18 0.9999999988916353 1 +Q5JTZ9 ecoli_3-ecoli_2 -7.085819523235728e-17 0.9999999876983767 1 +Q5JTZ9 ecoli_7-ecoli_2 3.9378814153986556e-19 0.9999999999316348 1 +Q5JTZ9 ecoli_7-ecoli_3 7.125198337389714e-17 0.9999999876300115 1 +Q5T1J5, Q9Y6H1 ecoli_2-ecoli_1 0.07669443544313348 0.5641706771130001 1 +Q5T1J5, Q9Y6H1 ecoli_3-ecoli_1 -0.028125483450498312 0.8306554530142143 1 +Q5T1J5, Q9Y6H1 ecoli_7-ecoli_1 0.25254759170648966 0.08733407465960519 1 +Q5T1J5, Q9Y6H1 ecoli_3-ecoli_2 -0.10481991889363179 0.43561301404781405 0.9413246740343568 +Q5T1J5, Q9Y6H1 ecoli_7-ecoli_2 0.17585315626335618 0.20842315704387956 0.8905353073693035 +Q5T1J5, Q9Y6H1 ecoli_7-ecoli_3 0.28067307515698797 0.06314151869256265 0.3977915677631447 +Q5T653 ecoli_2-ecoli_1 0.3718207912028474 0.1793950114788033 1 +Q5T653 ecoli_3-ecoli_1 -0.42564346112307816 0.13193316862976073 0.4960687140479003 +Q5T653 ecoli_7-ecoli_1 -0.3357355696064147 0.219587101107439 1 +Q5T653 ecoli_3-ecoli_2 -0.7974642523259254 0.016006081658335534 0.09706914037958324 +Q5T653 ecoli_7-ecoli_2 -0.7075563608092621 0.026174530943808998 0.3541980827733886 +Q5T653 ecoli_7-ecoli_3 0.08990789151666345 0.7276517713955419 1 +Q6PD74 ecoli_2-ecoli_1 0 1 1 +Q6PD74 ecoli_3-ecoli_1 0 1 1 +Q6PD74 ecoli_7-ecoli_1 0 1 1 +Q6PD74 ecoli_3-ecoli_2 0 1 1 +Q6PD74 ecoli_7-ecoli_2 0 1 1 +Q6PD74 ecoli_7-ecoli_3 0 1 1 +Q6PK04 ecoli_2-ecoli_1 -0.03238231890975527 0.8576445049580141 1 +Q6PK04 ecoli_3-ecoli_1 -0.17588716464086154 0.3436569479317015 0.8973264751549983 +Q6PK04 ecoli_7-ecoli_1 -0.06975759276494618 0.7002791575386312 1 +Q6PK04 ecoli_3-ecoli_2 -0.14350484573110628 0.43537573581372324 0.9413246740343568 +Q6PK04 ecoli_7-ecoli_2 -0.03737527385519092 0.8360421416302029 1 +Q6PK04 ecoli_7-ecoli_3 0.10612957187591536 0.5605582822688204 1 +Q6SZW1 ecoli_2-ecoli_1 0.14470360302319318 0.5450386653707164 1 +Q6SZW1 ecoli_3-ecoli_1 -1.5096626457362943 5.363153006086853e-4 0.02016545530288657 +Q6SZW1 ecoli_7-ecoli_1 -1.0073764235363194 0.004243428956855771 0.3383231185087619 +Q6SZW1 ecoli_3-ecoli_2 -1.6543662487594872 3.2609235726867525e-4 0.010217560527751824 +Q6SZW1 ecoli_7-ecoli_2 -1.1520800265595126 0.0021965702723490597 0.17252957790922283 +Q6SZW1 ecoli_7-ecoli_3 0.5022862221999749 0.0675697420221545 0.40210624801165656 +Q6UXF7, Q8NCF0 ecoli_2-ecoli_1 -0.5322348962386825 0.2649449274574432 1 +Q6UXF7, Q8NCF0 ecoli_3-ecoli_1 1.260816750248338 0.02572000339741912 0.1611786879571598 +Q6UXF7, Q8NCF0 ecoli_7-ecoli_1 0.32390361000065493 0.4835357271135141 1 +Q6UXF7, Q8NCF0 ecoli_3-ecoli_2 1.7930516464870205 0.005489404174745028 0.05680098562596089 +Q6UXF7, Q8NCF0 ecoli_7-ecoli_2 0.8561385062393374 0.09412458872262361 0.63197938142333 +Q6UXF7, Q8NCF0 ecoli_7-ecoli_3 -0.9369131402476831 0.07233657371638266 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0.999999713510392 1 +Q9Y520 ecoli_7-ecoli_1 -2.0051157417078776e-14 0.9999997492570054 1 +Q9Y520 ecoli_3-ecoli_2 -1.5880933921680886e-14 0.9999998014063305 1 +Q9Y520 ecoli_7-ecoli_2 -1.3022385572659273e-14 0.9999998371529439 1 +Q9Y520 ecoli_7-ecoli_3 2.8585483490216146e-15 0.9999999642533866 1 +Q9Y5B9 ecoli_2-ecoli_1 0.005615071799879606 0.8853387834778575 1 +Q9Y5B9 ecoli_3-ecoli_1 -0.0015531492547019557 0.968164582586059 1 +Q9Y5B9 ecoli_7-ecoli_1 -0.004969573956498814 0.8984304177816331 1 +Q9Y5B9 ecoli_3-ecoli_2 -0.007168221054581562 0.8539972324244992 1 +Q9Y5B9 ecoli_7-ecoli_2 -0.01058464575637842 0.7860801787908229 1 +Q9Y5B9 ecoli_7-ecoli_3 -0.0034164247017968584 0.9300563795409404 1 +P55263 ecoli_2-ecoli_1 0 1 1 +P55263 ecoli_3-ecoli_1 0 1 1 +P55263 ecoli_7-ecoli_1 0 1 1 +P55263 ecoli_3-ecoli_2 0 1 1 +P55263 ecoli_7-ecoli_2 0 1 1 +P55263 ecoli_7-ecoli_3 0 1 1 +P0A6Z3 ecoli_7-ecoli_3 -0.40906290318649974 0.062027760478629304 0.3977915677631447
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/summarised_proteins.tsv Tue Oct 02 16:30:33 2018 -0400 @@ -0,0 +1,190 @@ +protein sumIntensity_HEK.Ecoli_10.1_replicate1 sumIntensity_HEK.Ecoli_10.1_replicate2 sumIntensity_HEK.Ecoli_20.1_replicate1 sumIntensity_HEK.Ecoli_20.1_replicate2 sumIntensity_HEK.Ecoli_3.1_replicate1 sumIntensity_HEK.Ecoli_3.1_replicate2 sumIntensity_HEK.Ecoli_7.1_replicate1 sumIntensity_HEK.Ecoli_7.1_replicate2 +A1L0T0 17.429399406156225 17.33398240348946 17.410541528940552 17.169264537725507 16.96506289630559 17.102898046807514 16.810637234848606 17.4312146548945 +O00139 20.421077251892928 19.4956589466802 18.682046032353977 20.423674084830257 21.718035972736907 21.77141317029518 20.840274733873557 20.12149078546466 +O00151 17.81884023410664 17.531832922589587 17.62163842515729 18.09366919995733 17.67254437376755 17.427616936505473 17.96961117579886 17.425397847613464 +O00273 19.93242622242833 19.603203040939114 19.886945840734857 19.867923201248434 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