# HG changeset patch # User galaxyp # Date 1538512233 14400 # Node ID bb199421f73136ba7825dec315c99cd305116948 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/lfq_protein_quant commit 26ff08776f90f96646598a19cfcf57d42aa4a43b diff -r 000000000000 -r bb199421f731 lfq_protein_quant.xml --- /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 @@ + + + summarisation and quantitation + + + bioconductor-msnbase + bioconductor-mzr + r-tidyverse + r-lme4 + r-furrr + r-msqrob + + + + + + + + + + + + + not only_summarisation + + + + + + + + + + + + + + + + + 10.1074/mcp.M115.055897 + 10.1093/bioinformatics/btr645 + + diff -r 000000000000 -r bb199421f731 quantitation.r --- /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) + diff -r 000000000000 -r bb199421f731 test-data/meta.tab --- /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 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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 0.031050234425502816 0.3346874803222741 +O00151 ecoli_2-ecoli_1 0 1 1 +O00151 ecoli_3-ecoli_1 0 1 1 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1 +O75390 ecoli_3-ecoli_1 0 1 1 +O75390 ecoli_7-ecoli_1 0 1 1 +O75390 ecoli_3-ecoli_2 0 1 1 +O75390 ecoli_7-ecoli_2 0 1 1 +O75390 ecoli_7-ecoli_3 0 1 1 +O75533 ecoli_2-ecoli_1 0.3169426749019405 0.03935896775592092 1 +O75533 ecoli_3-ecoli_1 -0.11935706151634232 0.36406826123496344 0.9125977748289751 +O75533 ecoli_7-ecoli_1 -0.03610533570198816 0.7767379504279389 1 +O75533 ecoli_3-ecoli_2 -0.4362997364182828 0.010969442439346803 0.07931750686912303 +O75533 ecoli_7-ecoli_2 -0.35304801060392865 0.026376452972486385 0.3541980827733886 +O75533 ecoli_7-ecoli_3 0.08325172581435417 0.5192725751641845 1 +O75643 ecoli_2-ecoli_1 5.317609740520191e-19 0.9999999993619537 1 +O75643 ecoli_3-ecoli_1 -1.6961687453489036e-18 0.9999999979648111 1 +O75643 ecoli_7-ecoli_1 -7.42652156227463e-19 0.9999999991089109 1 +O75643 ecoli_3-ecoli_2 -2.227929719400923e-18 0.9999999973267648 1 +O75643 ecoli_7-ecoli_2 -1.2744131302794821e-18 0.9999999984708647 1 +O75643 ecoli_7-ecoli_3 9.535165891214406e-19 0.9999999988559001 1 +O94826 ecoli_2-ecoli_1 0.17434957181919067 0.17737706443743412 1 +O94826 ecoli_3-ecoli_1 -0.22939359524001673 0.09054145615373821 0.35597409673867186 +O94826 ecoli_7-ecoli_1 0.041563698630037116 0.729030659100311 1 +O94826 ecoli_3-ecoli_2 -0.40374316705920743 0.011708495616390742 0.07990512966638055 +O94826 ecoli_7-ecoli_2 -0.13278587318915355 0.28933832290881495 1 +O94826 ecoli_7-ecoli_3 0.27095729387005385 0.05444355013372875 0.38966496981959714 +O94906 ecoli_2-ecoli_1 -0.002877957436813919 0.9818280610439835 1 +O94906 ecoli_3-ecoli_1 0.3888779382145671 0.017600220557025887 0.13901624882226976 +O94906 ecoli_7-ecoli_1 0.0656978954068384 0.6071911635424636 1 +O94906 ecoli_3-ecoli_2 0.391755895651381 0.017070352801343478 0.09941469785383684 +O94906 ecoli_7-ecoli_2 0.06857585284365232 0.5919086753149504 1 +O94906 ecoli_7-ecoli_3 -0.3231800428077287 0.036116823402158556 0.3346874803222741 +O95831 ecoli_2-ecoli_1 0.0245985326669663 0.7997674961092389 1 +O95831 ecoli_3-ecoli_1 -0.09820206747941315 0.3275525920663132 0.8797126758352412 +O95831 ecoli_7-ecoli_1 -0.07227801164675313 0.4640316114219951 1 +O95831 ecoli_3-ecoli_2 -0.12280060014637945 0.22946339401310026 0.5829610550603087 +O95831 ecoli_7-ecoli_2 -0.09687654431371943 0.3336825703627786 1 +O95831 ecoli_7-ecoli_3 0.025924055832660012 0.7892801024896633 1 +P00350 ecoli_2-ecoli_1 -0.4777664147983703 0.44958084528097886 1 +P00350 ecoli_3-ecoli_1 0.581930291766587 0.36157573562441137 0.9125977748289751 +P00350 ecoli_7-ecoli_1 0.06452981264404109 0.9170206378854528 1 +P00350 ecoli_3-ecoli_2 1.0596967065649572 0.11745233416704542 0.3619842430066318 +P00350 ecoli_7-ecoli_2 0.5422962274424113 0.3934251191950704 1 +P00350 ecoli_7-ecoli_3 -0.5174004791225458 0.41445902597928685 1 +P00961, P22570 ecoli_2-ecoli_1 5.400092721761121e-21 0.9999999999848024 1 +P00961, P22570 ecoli_3-ecoli_1 -2.3704757498732066e-20 0.9999999999332867 1 +P00961, P22570 ecoli_7-ecoli_1 -2.1306213703114063e-20 0.9999999999400371 1 +P00961, P22570 ecoli_3-ecoli_2 -2.910485022049319e-20 0.9999999999180891 1 +P00961, P22570 ecoli_7-ecoli_2 -2.6706306424875185e-20 0.9999999999248393 1 +P00961, P22570 ecoli_7-ecoli_3 2.3985437956180052e-21 0.9999999999932496 1 +P02413 ecoli_2-ecoli_1 -1.5127435505183047e-19 0.9999999999314704 1 +P02413 ecoli_3-ecoli_1 5.796905849079552e-19 0.9999999997373914 1 +P02413 ecoli_7-ecoli_1 2.2797391996958357e-19 0.9999999998967244 1 +P02413 ecoli_3-ecoli_2 7.309649399597857e-19 0.9999999996688618 1 +P02413 ecoli_7-ecoli_2 3.7924827502141405e-19 0.9999999998281948 1 +P02413 ecoli_7-ecoli_3 -3.517166649383716e-19 0.999999999840667 1 +P04075 ecoli_2-ecoli_1 0 1 1 +P04075 ecoli_3-ecoli_1 0 1 1 +P04075 ecoli_7-ecoli_1 0 1 1 +P04075 ecoli_3-ecoli_2 0 1 1 +P04075 ecoli_7-ecoli_2 0 1 1 +P04075 ecoli_7-ecoli_3 0 1 1 +P05091 ecoli_2-ecoli_1 0.011413176435064246 0.9289917033015069 1 +P05091 ecoli_3-ecoli_1 -0.27617246190930644 0.06225431955625532 0.29259530191440003 +P05091 ecoli_7-ecoli_1 -0.1619662609443243 0.23269346676877603 1 +P05091 ecoli_3-ecoli_2 -0.2875856383443707 0.0545002690555956 0.2276900129433772 +P05091 ecoli_7-ecoli_2 -0.17337943737938852 0.20485876002125494 0.8905353073693035 +P05091 ecoli_7-ecoli_3 0.11420620096498216 0.38686158529993253 1 +P05386 ecoli_2-ecoli_1 0.053736872116817186 0.6783047673791209 1 +P05386 ecoli_3-ecoli_1 -0.2492280590698629 0.08645250278874242 0.35332762009312124 +P05386 ecoli_7-ecoli_1 -0.09602235244720372 0.4651356999042514 1 +P05386 ecoli_3-ecoli_2 -0.3029649311866801 0.04630366297677119 0.20244392185192983 +P05386 ecoli_7-ecoli_2 -0.1497592245640209 0.2681548586881306 1 +P05386 ecoli_7-ecoli_3 0.15320570662265917 0.25831146002216976 0.827472304138815 +P06454 ecoli_2-ecoli_1 -1.1228321841946492e-16 0.9999999901218006 1 +P06454 ecoli_3-ecoli_1 -1.683631100604687e-17 0.999999998518813 1 +P06454 ecoli_7-ecoli_1 -5.3384998556761025e-17 0.9999999953034151 1 +P06454 ecoli_3-ecoli_2 9.544690741341806e-17 0.9999999916029876 1 +P06454 ecoli_7-ecoli_2 5.88982198627039e-17 0.9999999948183854 1 +P06454 ecoli_7-ecoli_3 -3.6548687550714154e-17 0.999999996784602 1 +P06753 ecoli_2-ecoli_1 0.030764238397642828 0.7692670590403783 1 +P06753 ecoli_3-ecoli_1 -0.11951301496792001 0.273952041176718 0.8112564488214594 +P06753 ecoli_7-ecoli_1 -0.009085565242102123 0.9307696365821019 1 +P06753 ecoli_3-ecoli_2 -0.15027725336556283 0.17877869255211773 0.4894184628313271 +P06753 ecoli_7-ecoli_2 -0.039849803639744955 0.7045624992700769 1 +P06753 ecoli_7-ecoli_3 0.11042744972581789 0.3091735785494371 0.9579312515712068 +P07813 ecoli_2-ecoli_1 0.2372026226229671 0.5357974877426259 1 +P07813 ecoli_3-ecoli_1 4.079746187782321 3.544612141574623e-5 0.006663870826160292 +P07813 ecoli_7-ecoli_1 -0.8802967437092978 0.051682618005187614 1 +P07813 ecoli_3-ecoli_2 3.842543565159354 4.9510118300063056e-5 0.004653951120205928 +P07813 ecoli_7-ecoli_2 -1.117499366332265 0.022031646374075873 0.3541980827733886 +P07813 ecoli_7-ecoli_3 -4.9600429314916195 1.180266732530396e-5 0.0022307041244824482 +P07814 ecoli_2-ecoli_1 0.12053228429934824 0.427736966073307 1 +P07814 ecoli_3-ecoli_1 -0.3491030605145049 0.04702260889408572 0.23919768442343972 +P07814 ecoli_7-ecoli_1 -0.14395223428589754 0.3486345091227212 1 +P07814 ecoli_3-ecoli_2 -0.46963534481385316 0.014873632432230164 0.09642216887100934 +P07814 ecoli_7-ecoli_2 -0.2644845185852458 0.10938724514552361 0.6426500652299513 +P07814 ecoli_7-ecoli_3 0.20515082622860736 0.19658463485981414 0.7010282261982052 +P07954 ecoli_2-ecoli_1 1.3578020624933786e-22 0.9999999999994804 1 +P07954 ecoli_3-ecoli_1 5.550525730112217e-23 0.9999999999997876 1 +P07954 ecoli_7-ecoli_1 4.0178476379979607e-23 0.9999999999998462 1 +P07954 ecoli_3-ecoli_2 -8.02749489482157e-23 0.9999999999996928 1 +P07954 ecoli_7-ecoli_2 -9.560172986935827e-23 0.9999999999996342 1 +P07954 ecoli_7-ecoli_3 -1.532678092114256e-23 0.9999999999999414 1 +P08312 ecoli_2-ecoli_1 -0.4394716448398446 0.2474016902991572 1 +P08312 ecoli_3-ecoli_1 0.23073988934796155 0.5293527509484754 1 +P08312 ecoli_7-ecoli_1 0.021428843678297428 0.9527442129239073 1 +P08312 ecoli_3-ecoli_2 0.6702115341878061 0.09512026362106919 0.3083208544958794 +P08312 ecoli_7-ecoli_2 0.460900488518142 0.22720371265529773 0.9285716951999126 +P08312 ecoli_7-ecoli_3 -0.20931104566966413 0.5673092352486693 1 +P08559 ecoli_2-ecoli_1 0.27819571954397754 0.07909668879162723 1 +P08559 ecoli_3-ecoli_1 -0.020082641712841312 0.885837186923053 1 +P08559 ecoli_7-ecoli_1 0.00793522705323247 0.95472943746152 1 +P08559 ecoli_3-ecoli_2 -0.29827836125681884 0.0636576342002103 0.2546305368008412 +P08559 ecoli_7-ecoli_2 -0.2702604924907451 0.08618450641372631 0.63197938142333 +P08559 ecoli_7-ecoli_3 0.028017868766073782 0.8413624823038703 1 +P08574 ecoli_2-ecoli_1 0 1 1 +P08574 ecoli_3-ecoli_1 0 1 1 +P08574 ecoli_7-ecoli_1 0 1 1 +P08574 ecoli_3-ecoli_2 0 1 1 +P08574 ecoli_7-ecoli_2 0 1 1 +P08574 ecoli_7-ecoli_3 0 1 1 +P0A6F5 ecoli_2-ecoli_1 0.07172120898327407 0.8127981238931172 1 +P0A6F5 ecoli_3-ecoli_1 0.7976298281343743 0.03162563888936624 0.19179419713551138 +P0A6F5 ecoli_7-ecoli_1 -0.5816336487388689 0.08955762368298222 1 +P0A6F5 ecoli_3-ecoli_2 0.7259086191511003 0.04447339730466057 0.19907139745895686 +P0A6F5 ecoli_7-ecoli_2 -0.653354857722143 0.06313705332691727 0.5652269535933546 +P0A6F5 ecoli_7-ecoli_3 -1.3792634768732432 0.002723153307611922 0.07352513930552189 +P0A6L0 ecoli_2-ecoli_1 -1.128890324624692e-15 0.9999999756545496 1 +P0A6L0 ecoli_3-ecoli_1 2.808339883481543e-15 0.9999999394358354 1 +P0A6L0 ecoli_7-ecoli_1 1.177954948735006e-15 0.9999999745964305 1 +P0A6L0 ecoli_3-ecoli_2 3.9372302081062355e-15 0.9999999150903849 1 +P0A6L0 ecoli_7-ecoli_2 2.306845273359698e-15 0.99999995025098 1 +P0A6L0 ecoli_7-ecoli_3 -1.6303849347465372e-15 0.9999999648394049 1 +P0A6P9 ecoli_2-ecoli_1 -0.6397452813193203 0.006480010453258999 1 +P0A6P9 ecoli_3-ecoli_1 1.3632149179429331 1.2994478866425288e-4 0.012214810134439771 +P0A6P9 ecoli_7-ecoli_1 0.3628009789777882 0.059507214278955806 1 +P0A6P9 ecoli_3-ecoli_2 2.0029601992622537 1.4556306532762226e-5 0.0027365856281592986 +P0A6P9 ecoli_7-ecoli_2 1.0025462602971085 6.937222217360124e-4 0.13041977768637034 +P0A6P9 ecoli_7-ecoli_3 -1.000413938965145 7.0159005728607e-4 0.04420017360902241 +P0A6Y8 ecoli_2-ecoli_1 0.010446266892863362 0.9370492737982021 1 +P0A6Y8 ecoli_3-ecoli_1 -0.22449565088625906 0.12168429848431493 0.46687036969492257 +P0A6Y8 ecoli_7-ecoli_1 -0.0911472986060722 0.49812407843205864 1 +P0A6Y8 ecoli_3-ecoli_2 -0.2349419177791224 0.10791395604879575 0.33813039561956004 +P0A6Y8 ecoli_7-ecoli_2 -0.10159356549893556 0.4520315302896627 1 +P0A6Y8 ecoli_7-ecoli_3 0.13334835228018685 0.33063708072376463 1 +P0A705 ecoli_2-ecoli_1 -0.2776073627249899 0.5066907187774404 1 +P0A705 ecoli_3-ecoli_1 1.353603982580575 0.013025514846200445 0.12243983955428418 +P0A705 ecoli_7-ecoli_1 0.5455205883564509 0.2137914521544211 1 +P0A705 ecoli_3-ecoli_2 1.631211345305565 0.005566068896419054 0.05680098562596089 +P0A705 ecoli_7-ecoli_2 0.8231279510814408 0.079931954835758 0.6261336462134377 +P0A705 ecoli_7-ecoli_3 -0.8080833942241241 0.08434802243138385 0.43897455293348076 +P0A7J3 ecoli_2-ecoli_1 -0.40260181357523495 0.21522525216564958 1 +P0A7J3 ecoli_3-ecoli_1 0.7343489507252339 0.04351036921904897 0.23919768442343972 +P0A7J3 ecoli_7-ecoli_1 -0.032962376897683235 0.9138082908809273 1 +P0A7J3 ecoli_3-ecoli_2 1.1369507643004688 0.007145522013016109 0.06396943516414422 +P0A7J3 ecoli_7-ecoli_2 0.3696394366775517 0.2507489039862672 0.9662502439995886 +P0A7J3 ecoli_7-ecoli_3 -0.7673113276229171 0.03718749781358601 0.3346874803222741 +P0A7V8 ecoli_2-ecoli_1 -0.4551390189734907 0.2565303162295177 1 +P0A7V8 ecoli_3-ecoli_1 1.4562823736775312 0.006728592940907021 0.08025252987765204 +P0A7V8 ecoli_7-ecoli_1 0.47742012063006417 0.23638743057182635 1 +P0A7V8 ecoli_3-ecoli_2 1.9114213926510217 0.0017638342835875273 0.027371442899113155 +P0A7V8 ecoli_7-ecoli_2 0.9325591396035549 0.04178221531229738 0.4620621458065828 +P0A7V8 ecoli_7-ecoli_3 -0.978862253047467 0.035148296318077 0.3346874803222741 +P0A7Z4 ecoli_2-ecoli_1 0.4268318387095561 0.4162693984035334 1 +P0A7Z4 ecoli_3-ecoli_1 1.192574893096724 0.0469060955460424 0.23919768442343972 +P0A7Z4 ecoli_7-ecoli_1 0.8319672096439363 0.13643857368154097 1 +P0A7Z4 ecoli_3-ecoli_2 0.7657430543871678 0.165433018844041 0.4712334476163592 +P0A7Z4 ecoli_7-ecoli_2 0.40513537093438023 0.43923933816339766 1 +P0A7Z4 ecoli_7-ecoli_3 -0.3606076834527876 0.48913043083678 1 +P0A8T7 ecoli_2-ecoli_1 0.014062646577311932 0.9795720057246751 1 +P0A8T7 ecoli_3-ecoli_1 1.0370554306929216 0.09088700342263963 0.35597409673867186 +P0A8T7 ecoli_7-ecoli_1 0.07755360254357391 0.8877837234304259 1 +P0A8T7 ecoli_3-ecoli_2 1.0229927841156097 0.09452309054247585 0.3083208544958794 +P0A8T7 ecoli_7-ecoli_2 0.06349095596626197 0.9080088350192859 1 +P0A8T7 ecoli_7-ecoli_3 -0.9595018281493477 0.11277792641579794 0.4810629649021967 +P0A910 ecoli_2-ecoli_1 0 1 1 +P0A910 ecoli_3-ecoli_1 0 1 1 +P0A910 ecoli_7-ecoli_1 0 1 1 +P0A910 ecoli_3-ecoli_2 0 1 1 +P0A910 ecoli_7-ecoli_2 0 1 1 +P0A910 ecoli_7-ecoli_3 0 1 1 +P0ABB4 ecoli_2-ecoli_1 -2.0067131029240672e-15 0.9999999202780272 1 +P0ABB4 ecoli_3-ecoli_1 5.810536287888249e-16 0.9999999769161114 1 +P0ABB4 ecoli_7-ecoli_1 5.628256791736849e-16 0.9999999776402648 1 +P0ABB4 ecoli_3-ecoli_2 2.587766731712892e-15 0.9999998971941386 1 +P0ABB4 ecoli_7-ecoli_2 2.569538782097752e-15 0.999999897918292 1 +P0ABB4 ecoli_7-ecoli_3 -1.8227949615139985e-17 0.9999999992758466 1 +P0ABE2 ecoli_2-ecoli_1 -0.24793495189851011 0.5054677884151821 1 +P0ABE2 ecoli_3-ecoli_1 0.44717395264957727 0.22008112149189282 0.7388437650084974 +P0ABE2 ecoli_7-ecoli_1 0.11265796863678174 0.7390590760608566 1 +P0ABE2 ecoli_3-ecoli_2 0.6951089045480874 0.08069730188903677 0.27583805009343476 +P0ABE2 ecoli_7-ecoli_2 0.36059292053529185 0.31015422294783923 1 +P0ABE2 ecoli_7-ecoli_3 -0.33451598401279553 0.2947172242219663 0.9283592562991938 +P0ABS1 ecoli_2-ecoli_1 3.3604649899848793e-4 0.9985955339814282 1 +P0ABS1 ecoli_3-ecoli_1 0.8358588691835234 0.0036001095537593554 0.0564017163422299 +P0ABS1 ecoli_7-ecoli_1 -0.023911487566855155 0.9003927016357729 1 +P0ABS1 ecoli_3-ecoli_2 0.8355228226845249 0.0036072384713708213 0.048440059472693885 +P0ABS1 ecoli_7-ecoli_2 -0.024247534065853643 0.8990022205713011 1 +P0ABS1 ecoli_7-ecoli_3 -0.8597703567503785 0.0031312515045948297 0.07397581679605285 +P0ACF0 ecoli_2-ecoli_1 -4.089644028783471e-20 0.999999999971146 1 +P0ACF0 ecoli_3-ecoli_1 4.9328245049565106e-20 0.999999999965197 1 +P0ACF0 ecoli_7-ecoli_1 6.678473832433215e-20 0.9999999999528808 1 +P0ACF0 ecoli_3-ecoli_2 9.022468533739981e-20 0.9999999999363429 1 +P0ACF0 ecoli_7-ecoli_2 1.0768117861216685e-19 0.9999999999240268 1 +P0ACF0 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 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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 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ecoli_7-ecoli_1 -0.11593565147039078 0.42171912469484985 1 +P25787 ecoli_3-ecoli_2 -0.28825623281234247 0.07142742001113836 0.26964859225496696 +P25787 ecoli_7-ecoli_2 -0.1892470142217707 0.20620760645029193 0.8905353073693035 +P25787 ecoli_7-ecoli_3 0.09900921859057177 0.489834136993045 1 +P26641 ecoli_2-ecoli_1 1.2113764684270086e-21 0.9999999999966709 1 +P26641 ecoli_3-ecoli_1 -6.90683979018106e-21 0.999999999981019 1 +P26641 ecoli_7-ecoli_1 9.210457525185482e-22 0.9999999999974688 1 +P26641 ecoli_3-ecoli_2 -8.118216258608069e-21 0.9999999999776898 1 +P26641 ecoli_7-ecoli_2 -2.903307159084603e-22 0.9999999999992022 1 +P26641 ecoli_7-ecoli_3 7.827885542699609e-21 0.9999999999784879 1 +P27694 ecoli_2-ecoli_1 -1.1344807385957294e-21 0.999999999998636 1 +P27694 ecoli_3-ecoli_1 9.980665353838532e-22 0.9999999999988001 1 +P27694 ecoli_7-ecoli_1 2.3411926943546556e-21 0.9999999999971851 1 +P27694 ecoli_3-ecoli_2 2.1325472739795826e-21 0.999999999997436 1 +P27694 ecoli_7-ecoli_2 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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 -0.35978932444214323 0.017807030699448844 0.09941469785383684 +Q14978 ecoli_7-ecoli_2 -0.19439000262282013 0.133814154258856 0.7380551296658859 +Q14978 ecoli_7-ecoli_3 0.1653993218193231 0.1912717150302932 0.6951991180908733 +Q14980 ecoli_2-ecoli_1 0.16229009224115606 0.1876132175110573 1 +Q14980 ecoli_3-ecoli_1 -0.1512822900588161 0.21534333738626213 0.7388437650084974 +Q14980 ecoli_7-ecoli_1 -0.01176481356517526 0.9181518259539676 1 +Q14980 ecoli_3-ecoli_2 -0.3135723822999722 0.027092718738771615 0.13766030061862333 +Q14980 ecoli_7-ecoli_2 -0.17405490580633132 0.16163022467626148 0.8212562767334366 +Q14980 ecoli_7-ecoli_3 0.13951747649364085 0.24898759523967362 0.8255904473736546 +Q15084 ecoli_2-ecoli_1 0.024387217433196183 0.8547469833132166 1 +Q15084 ecoli_3-ecoli_1 -0.16476305887429635 0.23858935764354405 0.757957661997404 +Q15084 ecoli_7-ecoli_1 0.007286043744137778 0.9563360597046059 1 +Q15084 ecoli_3-ecoli_2 -0.18915027630749254 0.18223027871379202 0.4894184628313271 +Q15084 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 0.40210624801165656 +Q7Z6I8 ecoli_2-ecoli_1 -0.14746333311104282 0.24092000300819788 1 +Q7Z6I8 ecoli_3-ecoli_1 -0.13539359882738633 0.27823526190855563 0.8112564488214594 +Q7Z6I8 ecoli_7-ecoli_1 -0.08863607211239362 0.4677940056249612 1 +Q7Z6I8 ecoli_3-ecoli_2 0.012069734283656479 0.9198306943541812 1 +Q7Z6I8 ecoli_7-ecoli_2 0.058827260998649177 0.6264399311192593 1 +Q7Z6I8 ecoli_7-ecoli_3 0.0467575267149927 0.6979955208485962 1 +Q86U86 ecoli_2-ecoli_1 -2.3214573172901237e-15 0.999999938074702 1 +Q86U86 ecoli_3-ecoli_1 -5.0775870621797184e-15 0.9999998645544375 1 +Q86U86 ecoli_7-ecoli_1 -1.9864235530109142e-15 0.9999999470117888 1 +Q86U86 ecoli_3-ecoli_2 -2.7561297448895943e-15 0.9999999264797356 1 +Q86U86 ecoli_7-ecoli_2 3.3503376427920966e-16 0.9999999910629132 1 +Q86U86 ecoli_7-ecoli_3 3.0911635091688038e-15 0.9999999175426486 1 +Q8IXK2 ecoli_2-ecoli_1 0.04310786918685115 0.6622039890539471 1 +Q8IXK2 ecoli_3-ecoli_1 -0.09822150471347692 0.3340225468789516 0.8844540677921535 +Q8IXK2 ecoli_7-ecoli_1 -0.02755355115494281 0.7791001268882691 1 +Q8IXK2 ecoli_3-ecoli_2 -0.14132937390032807 0.1798015651985339 0.4894184628313271 +Q8IXK2 ecoli_7-ecoli_2 -0.07066142034179397 0.4793955484292157 1 +Q8IXK2 ecoli_7-ecoli_3 0.0706679535585341 0.47935649349747933 1 +Q8N163 ecoli_2-ecoli_1 0 1 1 +Q8N163 ecoli_3-ecoli_1 0 1 1 +Q8N163 ecoli_7-ecoli_1 0 1 1 +Q8N163 ecoli_3-ecoli_2 0 1 1 +Q8N163 ecoli_7-ecoli_2 0 1 1 +Q8N163 ecoli_7-ecoli_3 0 1 1 +Q8N5M9 ecoli_2-ecoli_1 0.32094647326296943 0.24347795515999016 1 +Q8N5M9 ecoli_3-ecoli_1 0.5171523625104631 0.08084628441993025 0.33775781046548636 +Q8N5M9 ecoli_7-ecoli_1 1.0107446003305482 0.005843579044401133 0.3383231185087619 +Q8N5M9 ecoli_3-ecoli_2 0.1962058892474937 0.4604627263218879 0.9837158244149422 +Q8N5M9 ecoli_7-ecoli_2 0.6897981270675788 0.03057553520215804 0.3832133745337141 +Q8N5M9 ecoli_7-ecoli_3 0.4935922378200851 0.09248474609921227 0.44749269040098666 +Q8NB49 ecoli_2-ecoli_1 0.014303447247493717 0.9570640651710736 1 +Q8NB49 ecoli_3-ecoli_1 0.021884477966020652 0.9263887138385551 1 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0.6217660325428782 1 +Q96GQ7 ecoli_3-ecoli_1 -0.06017836867565262 0.5260585861802811 1 +Q96GQ7 ecoli_7-ecoli_1 0.01766746887464316 0.8505037743061767 1 +Q96GQ7 ecoli_3-ecoli_2 -0.10675155636619897 0.2737995824550741 0.6863242866873858 +Q96GQ7 ecoli_7-ecoli_2 -0.028905718815903193 0.7582746886754799 1 +Q96GQ7 ecoli_7-ecoli_3 0.07784583755029578 0.4162495693926358 1 +Q96HY6 ecoli_2-ecoli_1 0.07578678885530973 0.5957599295443268 1 +Q96HY6 ecoli_3-ecoli_1 -0.2803497871241131 0.08061066125717388 0.33775781046548636 +Q96HY6 ecoli_7-ecoli_1 -0.17688658725905881 0.23695216453157233 1 +Q96HY6 ecoli_3-ecoli_2 -0.35613657597942283 0.03631528972949722 0.1750583197216789 +Q96HY6 ecoli_7-ecoli_2 -0.2526733761143686 0.10807275160664583 0.6426500652299513 +Q96HY6 ecoli_7-ecoli_3 0.10346319986505431 0.4731219104137059 1 +Q96JM3 ecoli_2-ecoli_1 0.013839312371340909 0.9043642067070025 1 +Q96JM3 ecoli_3-ecoli_1 -0.11780820022055583 0.32669598086650625 0.8797126758352412 +Q96JM3 ecoli_7-ecoli_1 -0.3783408228481236 0.013658862308271347 0.5135732227910027 +Q96JM3 ecoli_3-ecoli_2 -0.13164751259189675 0.2777397813356862 0.6870405117251186 +Q96JM3 ecoli_7-ecoli_2 -0.3921801352194645 0.011702353405716 0.3431241521774416 +Q96JM3 ecoli_7-ecoli_3 -0.26053262262756777 0.055672052941647024 0.38966496981959714 +Q96JP5 ecoli_2-ecoli_1 -0.037951836240909184 0.7892633724694473 1 +Q96JP5 ecoli_3-ecoli_1 -0.1585139887195795 0.2817007393442287 0.8112564488214594 +Q96JP5 ecoli_7-ecoli_1 -0.05450056764126529 0.7018261228243217 1 +Q96JP5 ecoli_3-ecoli_2 -0.12056215247867032 0.4055692589222988 0.9298417155779533 +Q96JP5 ecoli_7-ecoli_2 -0.016548731400356113 0.9070477798200682 1 +Q96JP5 ecoli_7-ecoli_3 0.10401342107831421 0.47050486707414163 1 +Q96S52 ecoli_2-ecoli_1 0.10656756870040335 0.3739294680618427 1 +Q96S52 ecoli_3-ecoli_1 0.08870581652012047 0.4551272074178879 1 +Q96S52 ecoli_7-ecoli_1 -0.10702961883576737 0.3719830190329845 1 +Q96S52 ecoli_3-ecoli_2 -0.01786175218028288 0.8779652389740011 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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 diff -r 000000000000 -r bb199421f731 test-data/summarised_proteins.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ 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