Mercurial > repos > galaxyp > mqppep_preproc
changeset 3:bae3a23461c9 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3dcf0d08f006b888061ff83eadc65e550d751869
| author | galaxyp |
|---|---|
| date | Tue, 31 Jan 2023 22:27:00 +0000 |
| parents | a5e7469dfdfa |
| children | 5c2beb4eb41c |
| files | MaxQuantProcessingScript.R macros.xml mqppep_anova.R mqppep_anova_script.Rmd |
| diffstat | 4 files changed, 374 insertions(+), 218 deletions(-) [+] |
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--- a/MaxQuantProcessingScript.R Mon Dec 12 22:01:21 2022 +0000 +++ b/MaxQuantProcessingScript.R Tue Jan 31 22:27:00 2023 +0000 @@ -73,7 +73,9 @@ } # Generate phosphopeptide and build list when applied +# nolint start: squash un-actionable cyclomatic_complexity warning phosphopeptide_func <- function(df) { +# nolint end # generate peptide sequence and list of phosphopositions phosphoprobsequence <- strsplit(as.character(df["Phospho (STY) Score diffs"]), "")[[1]]
--- a/macros.xml Mon Dec 12 22:01:21 2022 +0000 +++ b/macros.xml Tue Jan 31 22:27:00 2023 +0000 @@ -1,5 +1,5 @@ <macros> - <token name="@TOOL_VERSION@">0.1.16</token> + <token name="@TOOL_VERSION@">0.1.17</token> <token name="@VERSION_SUFFIX@">0</token> <xml name="requirements"> <requirements>
--- a/mqppep_anova.R Mon Dec 12 22:01:21 2022 +0000 +++ b/mqppep_anova.R Tue Jan 31 22:27:00 2023 +0000 @@ -101,14 +101,15 @@ default = "FDR", type = "character", help = paste0("Method for missing-value imputation,", - " one of c('FDR','p.value'), but don't expect 'p.value' to work well.") + " one of c('FDR','p.value'), but don't expect 'p.value' to work well.") ), make_option( c("-t", "--ksea_cutoff_threshold"), action = "store", default = 0.05, type = "double", - help = paste0("Maximum score to be used to score a kinase enrichment as significant") + help = paste0( + "Maximum score to be used to score a kinase enrichment as significant") ), make_option( c("-c", "--kseaMinSubstrateCount"), @@ -269,7 +270,8 @@ ) ) < 1 ) { - print(sprintf("bad ksea_cutoff_statistic argument: %s", ksea_cutoff_statistic)) + print(sprintf( + "bad ksea_cutoff_statistic argument: %s", ksea_cutoff_statistic)) return(-1) } @@ -313,7 +315,6 @@ cat(sprintf("not a file: '%s'\n", fname)) fname } - #AC print(paste0("read_config_file_string: opening file '", as.character(fname), "'")) # eliminate any leading whitespace result <- gsub("^[ \t\n]*", "", result) # eliminate any trailing whitespace @@ -347,8 +348,10 @@ cat(paste0("regex_sample_names: ", regex_sample_names, "\n")) if (group_filter != "none") { - cat(paste0("group_filter_patterns file: '", args$sampleGroupFilterPatterns, "'\n")) - group_filter_patterns <- read_config_file_string(args$sampleGroupFilterPatterns, nc) + cat(paste0("group_filter_patterns file: '", + args$sampleGroupFilterPatterns, "'\n")) + group_filter_patterns <- + read_config_file_string(args$sampleGroupFilterPatterns, nc) } else { group_filter_patterns <- ".*" }
--- a/mqppep_anova_script.Rmd Mon Dec 12 22:01:21 2022 +0000 +++ b/mqppep_anova_script.Rmd Tue Jan 31 22:27:00 2023 +0000 @@ -48,11 +48,11 @@ # for small random value imputation, what should `s / mean(x)` ratio be? sdPercentile: 1.0 # output path for imputed data file - imputedDataFilename: "test-data/limbo/imputedDataFilename.txt" + imputedDataFilename: "test-data/imputedDataFilename.txt" # output path for imputed/quantile-normalized/log-transformed data file - imputedQNLTDataFile: "test-data/limbo/imputedQNLTDataFile.txt" + imputedQNLTDataFile: "test-data/imputedQNLTDataFile.txt" # output path for contents of `stats_metadata_v` table - anovaKseaMetadata: "test-data/limbo/anovaKseaMetadata.txt" + anovaKseaMetadata: "test-data/anovaKseaMetadata.txt" # how to test one variable with > 2 categories (e.g., aov or kruskal.test) oneWayManyCategories: !r c("aov", "kruskal.test", "oneway.test")[1] # how to test one variable with 2 categories (e.g., oneway.test) @@ -99,8 +99,17 @@ showEnrichedSubstrates: FALSE # should debugging nb/nbe messages be printed? printNBMsgs: FALSE - # showld row-scaling be applied to heatmaps: "none" or "row" - defaultHeatMapRowScaling: "none" + #printNBMsgs: TRUE + # should row-scaling be applied to heatmaps: "none" or "row" + defaultHeatMapRowScaling: !r c("none", "row")[1] + # how missing values be displayed on heat maps: "NA" or " " + heatMapNAcellNote: !r c(" ", "NA")[1] + # how missing values be displayed on heat maps: "NA" or " " + heatMapNAgrey: "#D8D8D8" + # temporary hack + heatMapNAsubstitute: TRUE + # what color should be used for missing values be displayed on heat maps + heatMapNAcellColor: "grey15" # should debugging trace messages be printed? printTraceMsgs: FALSE # when debugging files are needed, set debugFileBasePath to the path @@ -124,7 +133,7 @@ } else { function(..., f = cat, file = stderr()) { cat( - stringi::stri_unescape_unicode("\nNBE \\u2203\\u2283\\u2200"), + stringi::stri_unescape_unicode("\nN.B. \\u2203\\u2283\\u2200"), ..., file = file ) @@ -148,7 +157,7 @@ library(gplots) if (print_nb_messages) nbe("library(caret)") # load caret for nearZeroVar -if (print_nb_messages) nbe("Please ignore the messages about systemd, if any.\n") +if (print_nb_messages) nbe("Please ignore any messages about systemd.\n") library(caret) if (print_nb_messages) nbe("library(DBI)") library(DBI) @@ -342,6 +351,18 @@ } } +divert_warnings <- + function(expr, classes = "warning") { + withCallingHandlers( + expr, + warning = function(w) { + cat(" divert_warnings: ", w$message, "\n", file = stderr()) + if (inherits(w, classes)) + tryInvokeRestart("muffleWarning") + } + ) + } + # ref: https://tug.org/texinfohtml/latex2e.html # LaTeX sets aside the following characters for special purposes. # For example, the percent sign % is for comments. @@ -360,24 +381,68 @@ # receiving a tilde accent). # Similarly, to get a text body font circumflex use \^{}. # To get a backslash in the font of the text body enter \textbackslash{}. -whack_math <- +whack_math <- if (TRUE) { function(v) { v <- as.character(v) + w <- v w <- gsub("\\", "\\textbackslash ", v, fixed = TRUE) w <- Reduce( f = function(l, r) { - gsub(r, paste0("\\", r), l, fixed = TRUE) + ptrn <- paste0("\\", r) + for (i in seq_along(l)) { + if (!grepl(ptrn, l[i], fixed = TRUE)) { + l[i] <- gsub(r, ptrn, l[i], fixed = TRUE) + } + } + l }, x = c("#", "$", "%", "&", "{", "}", "_"), init = w ) w <- gsub("^", "\\^{}", w, fixed = TRUE) + w <- gsub("\\textbackslash \\_", "\\_", w, fixed = TRUE) + return(w) + } +} else { + function(v) { + v <- as.character(v) + w <- v + w <- gsub("\\", "\\textbackslash ", v, fixed = TRUE) + w <- Reduce( + f = function(l, r) { + if (length(l) > 1) { + cat("whack_math: deparse1(v) = `", deparse1(v), "`\n", + file = stderr()) + cat("whack_math: v = `", v, "`\n", file = stderr()) + cat(" Reduce f: l = `", l, + "` r = `", r, + "`\n", file = stderr() + ) + divert_warnings(grepl(r, l, fixed = TRUE)) + } + ptrn <- paste0("\\", r) + cat("ptrn: `", ptrn, "`\n", file = stderr()) + for (i in seq_along(l)) { + cat(" before l[i] = ", l[i], "\n", file = stderr()) + if (!grepl(ptrn, l[i], fixed = TRUE)) { + l[i] <- gsub(r, ptrn, l[i], fixed = TRUE) + } + cat(" after l[i] = ", l[i], "\n", file = stderr()) + } + l + }, + x = c("#", "$", "%", "&", "{", "}", "_"), + init = w + ) + w <- gsub("^", "\\^{}", w, fixed = TRUE) + w <- gsub("\\textbackslash \\_", "\\_", w, fixed = TRUE) return(w) if (all(v == w)) v else paste0("\\texttt{", w, "}") } +} whack_underscores <- whack_math ## dump params to stderr (remove this eventually) @@ -580,13 +645,13 @@ cat("invalid intensityMinValuesPerGroup (must be integer > -1") knitr::knit_exit() } - -if (is.na(as.logical(g_correlate_substrates <- params$correlateSubstrates))) { +g_correlate_substrates <- params$correlateSubstrates +if (is.na(as.logical(g_correlate_substrates))) { cat("invalid correlateSubstrates (must be TRUE or FALSE)") knitr::knit_exit() } - -if (is.na(as.logical(g_filter_cov_var_gt_1 <- params$filterCovVarGT1))) { +g_filter_cov_var_gt_1 <- params$filterCovVarGT1 +if (is.na(as.logical(g_filter_cov_var_gt_1))) { cat("invalid filterCovVarGT1 parameter (must be TRUE or FALSE)") knitr::knit_exit() } @@ -619,6 +684,9 @@ ) invisible(rvalue) } +# Infix concatenation +`%||%` <- function(lvalue, rvalue) paste0(lvalue, rvalue) + ### FUNCTIONS @@ -705,7 +773,7 @@ ) grpl_rslt <- grpl_rslt + grpl_rslt_v } - rslt <- unname(grpl_rslt > 0) + unname(grpl_rslt > 0) } ##' Produce positions in a vector where succeeding value != current valus @@ -828,7 +896,10 @@ # This code adapted from matrixcalc::is.positive.definite # Notably, this simply tests without calling stop() +# nolint start +# squash un-actionable cyclomatic_complexity warning is_positive_definite <- function(x, tol = 1e-08) { +# nolint end if (!is.matrix(x)) return(FALSE) if (!is.numeric(x)) @@ -972,8 +1043,12 @@ } # Use this like print.data.frame, from which it is adapted: +# nolint start +# squash un-actionable cyclomatic_complexity warning +# squash un-actionable "no visible global for ..." data_frame_tabbing_latex <- function( +# nolint end x, # vector of tab stops, in inches tabstops, @@ -1151,38 +1226,30 @@ nolatex_verbatim <- function(expr) eval(expr) +error_knitrexit_stop <- + function(e) { + cat("Caught error:\n\n") + str(e) + knitr::knit_exit() + stop(e) + } + latex_verbatim <- function(expr) { - arg_string <- deparse1(substitute(expr)) cat("\n\\begin{verbatim}\n___\n") tryCatch( expr = expr, error = param_df_exit, - #ACE error = - #ACE function(e) { - #ACE cat("Caught error:\n\n") - #ACE str(e) - #ACE knitr::knit_exit() - #ACE stop(e) - #ACE }, finally = cat("...\n\\end{verbatim}\n") ) } latex_samepage <- function(expr) { - arg_string <- deparse1(substitute(expr)) cat("\n\\begin{samepage}\n") tryCatch( expr = expr, error = param_df_exit, - #ACE error = - #ACE function(e) { - #ACE cat("Caught error:\n\n") - #ACE str(e) - #ACE knitr::knit_exit() - #ACE stop(e) - #ACE }, finally = cat("\n\\end{samepage}\n") ) } @@ -1192,7 +1259,6 @@ latex_show_invocation <- function(f, f_name = deparse1(substitute(f)), head_patch = FALSE) { function(...) { - my_env <- (as.list(environment())) va <- list(...) my_rslt <- new_env() my_rslt$rslt <- NULL @@ -1202,7 +1268,8 @@ str(va) if (!head_patch) { # return this result - # ref: https://www.r-bloggers.com/2013/08/a-new-r-trick-for-me-at-least/ + # ref: + # https://www.r-bloggers.com/2013/08/a-new-r-trick-for-me-at-least/ cat(sprintf("\n .. Invoking '%s'\n", f_name)) tryCatch( { @@ -1210,13 +1277,6 @@ rslt <- do.call(f, va) }, error = param_df_exit, - #ACE error = function(e) { - #ACE cat("\n\\begin{verbatim}\n") - #ACE str(e) - #ACE cat("\n\\end{verbatim}\n") - #ACE knitr::knit_exit() - #ACE stop(e) - #ACE }, finally = cat("\n\\begin{verbatim}\n") ) cat(sprintf("\n .. '%s' returned:\n", f_name)) @@ -1248,7 +1308,8 @@ ) } -latex_itemized_collapsed <- function(collapse_string, v, underscore_whack = TRUE) { +latex_itemized_collapsed <- + function(collapse_string, v, underscore_whack = TRUE) { cat("\\begin{itemize}\n\\item ") latex_collapsed_vector(collapse_string, v, underscore_whack) cat("\n\\end{itemize}\n") @@ -1258,7 +1319,8 @@ latex_itemized_collapsed("\n\\item ", v, underscore_whack) } -latex_enumerated_collapsed <- function(collapse_string, v, underscore_whack = TRUE) { +latex_enumerated_collapsed <- + function(collapse_string, v, underscore_whack = TRUE) { cat("\\begin{enumerate}\n\\item ") latex_collapsed_vector(collapse_string, v, underscore_whack) cat("\n\\end{enumerate}\n") @@ -1302,7 +1364,7 @@ # N.B. use con = "" to emulate regular cat fcat0 <- function(..., sprtr = " ", cnnctn = file()) { - cat0(..., sep = sprtr, file = cnnctn) + gsubfn::cat0(..., sep = sprtr, file = cnnctn) invisible(cnnctn) } @@ -1428,7 +1490,10 @@ #' #' Horn et al. (2014) Nature Methods 11(6):603-4 #' +# nolint start +# squash un-actionable cyclomatic_complexity warning ksea_scores <- function( +# nolint end # For human data, typically, ksdata = KSEAapp::ksdata ksdata, @@ -1456,6 +1521,18 @@ minimum_substrate_count ) { + if (FALSE) { + if (print_nb_messages) nbe("Output contents of `ksdata` table\n") + cat_variable(ksdata) + cat("\n\\clearpage\n") + data_frame_tabbing_latex( + x = ksdata[order(ksdata$GENE, ksdata$SUB_GENE), ], + tabstops = c(0.3, 0.3, 1.2, 0.3, 0.3, 0.3, 0.3, + 0.8, 0.3, 0.8, 0.3, 0.3, 0.3), + caption = "ksdata dump", + use_subsubsection_header = FALSE + ) + } # no px$FC should be <= 0, but abs(px$FC) is used below as a precaution. if (length(grep(";", px$Residue.Both)) == 0) { # There are no Residue.Both entries having semicolons, so new is @@ -1509,7 +1586,10 @@ # # Should KSEA be performed aggregating signed log2FC or absolute? # FALSE use raw log2FC for KSEA as for KSEAapp::KSEA.Scores +# nolint start +# squash un-actionable "no visible global for ..." if (params$kseaUseAbsoluteLog2FC) { +# nolint end # TRUE use abs(log2FC) for KSEA as Justin requested; this is a # justifiable deviation from the KSEAapp::KSEA.Scores algorithm. new$log2_fc <- abs(new$log2_fc) @@ -1522,7 +1602,7 @@ cat(see_variable(networkin, "\n")) } ksdata_filtered <- - sqldf( + sqldf::sqldf( sprintf("%s %s", "select * from ksdata where not Source = 'NetworKIN'", if (networkin) @@ -1531,10 +1611,11 @@ "" ) ) + if (FALSE) write.csv(x = ksdata_filtered, file = "ksdata_filtered.csv") if (monitor_filtration_on_stderr) { - cat(see_variable(sqldf( + cat(see_variable(sqldf::sqldf( "select count(*), Source from ksdata group by Source"), "\n")) - cat(see_variable(sqldf( + cat(see_variable(sqldf::sqldf( "select count(*), Source from ksdata_filtered group by Source"), "\n")) sink() } @@ -1604,6 +1685,44 @@ ksdata_dataset_abbrev$p), ] if (print_nb_messages) nbe(see_variable(ksdata_dataset_abbrev)) + if (FALSE) write.csv( + x = ksdata_dataset_abbrev, + file = "ksdata_dataset_abbrev_no_aggregation.csv", + row.names = FALSE + ) + + # For some kinases, the only difference between kinase names in two databases + # is the case of the the letters; dedup to eliminate this false distinction + # The only imperfection with this approach is when the NetworKIN-only fliter + # is in force and there is a duplication between NetworKIN and HPRD (which + # outranks NetworKIN). Presently, the script does not invoke with this + # function with the NetworKIN-only filter active. + dedup_sql <- " + select + r.'Kinase.Gene', r.'Substrate.Gene', r.'Substrate.Mod', + r.Peptide, r.p, r.FC, r.log2FC, r.Source + from + ( select + rowid, + rank() over + (partition by + lower(k.'Kinase.Gene'), k.p, k.FC + order by + k.Source + ) as rank, + * + from + ksdata_dataset_abbrev k + ) as r + where r.rank = 1 + " + ksdata_dataset_abbrev <- sqldf::sqldf(x = dedup_sql) + if (FALSE) write.csv( + x = ksdata_dataset_abbrev, + file = "ksdata_dataset_abbrev_no_aggregation_dedup.csv", + row.names = FALSE + ) + # First aggregation step to account for multiply phosphorylated peptides # and differing peptide sequences; the goal here is to combine results # for all measurements of the same substrate. @@ -1656,6 +1775,7 @@ FUN = mean ) if (print_nb_messages) nbe(see_variable(mean_fc), "\n") + if (FALSE) write.csv(x = mean_fc, file = "mean_fc.csv") # for contrast j # for each kinase i @@ -1681,6 +1801,7 @@ # Reorder mean_fc, although I don't see why # (KSEA.Scores.R line 128 mean_fc <- mean_fc[order(mean_fc[, 1]), ] + if (FALSE) write.csv(x = mean_fc, file = "mean_fc.csv") # mean_fc columns so far: "Kinase.Gene", "log2FC" # - Kinase.Gene # indicates the gene name for each kinase. @@ -1736,7 +1857,6 @@ ] } - #ACE nb(see_variable(nrow(mean_fc)), "\n") # Create column 8: FDR, deduced by Benjamini-Hochberg adustment from p-value # - FDR # is the p-value adjusted for multiple hypothesis testing @@ -1747,7 +1867,10 @@ # It makes no sense to leave Z-scores negative when using # absolute value of fold-change +# nolint start +# squash un-actionable "no visible global for ..." if (params$kseaUseAbsoluteLog2FC) { +# nolint end mean_fc$z_score <- abs(mean_fc$z_score) } @@ -1810,8 +1933,6 @@ k <- k[selector < ksea_cutoff_threshold, ] nrow_k <- nrow(k) - #ACE nbe(see_variable(fdr_barplot_dataframe <- k)) - if (nrow_k > 0) { max_nchar_rowname <- max(nchar(rownames(k))) my_cex_names <- 1.0 / (1 + nrow_k / 50) @@ -1978,8 +2099,10 @@ "p_value", "fdr" ) - #ACE output_order <- with(output_df, order(fdr)) + +# nolint start output_order <- with(output_df, order(p_value)) +# nolint end output_df <- output_df[output_order, ] output_df[, 2] <- sprintf("%0.3g", output_df[, 2]) @@ -1988,10 +2111,9 @@ output_df$z_score <- sprintf("%0.2f", output_df$z_score) output_df$m_s <- sprintf("%d", output_df$m_s) output_df$enrichment <- sprintf("%0.3g", output_df$enrichment) - output_ncol <- ncol(output_df) colnames(output_df) <- c( - "Kinase", + "Kinase or motif", "\\(\\overline{{\\lvert}\\log_2 (\\text{fold-change}){\\rvert}}\\)", "Enrichment", "Substrates", @@ -2022,15 +2144,11 @@ if (print_nb_messages) nbe(see_variable(output_df)) math_caption <- gsub("{", "\\{", caption, fixed = TRUE) math_caption <- gsub("}", "\\}", math_caption, fixed = TRUE) - # with ( - # output_df, - # ) if (TRUE) { - output_df$Kinase <- whack_underscores(output_df$Kinase) data_frame_tabbing_latex( x = output_df, # vector of tab stops, in inches - tabstops = c(1.0, 1.2, 1.0, 1.0, 1.0, 1.0), + tabstops = c(1.8, 1.2, 0.8, 0.8, 0.8, 0.8, 0.8), # vector of headings, registered with tab-stops headings = colnames(output_df), # digits to pass to format.data.frame @@ -2057,20 +2175,6 @@ # set verbatim to TRUE to debug output verbatim = FALSE ) - } else { - data_frame_table_latex( - x = output_df, - justification = "l c c c c c c", - centered = TRUE, - caption = sprintf( - "\\text{%s}, KSEA %s < %s", - math_caption, - ksea_cutoff_statistic, - ksea_cutoff_threshold - ), - anchor = anchor, - underscore_whack = FALSE - ) } } else { cat( @@ -2132,7 +2236,10 @@ return(color_breaks) } +# nolint start +# squash un-actionable cyclomatic_complexity warning hm2plus <- function( +# nolint end x, mat = matrix( c( @@ -2161,11 +2268,10 @@ ... ) { - varargs <- list(...) if (FALSE) # this is to avoid commenting out code to pass linting... - my_hm2 <- latex_show_invocation(heatmap.2, head_patch = FALSE) + my_hm2 <- latex_show_invocation(gplots::heatmap.2, head_patch = FALSE) else - my_hm2 <- heatmap.2 + my_hm2 <- gplots::heatmap.2 x <- as.matrix(x) if (sum(!is.na(x)) < 1) @@ -2177,7 +2283,7 @@ if (print_nb_messages) nb("within hm2plus", see_variable(divergent), "\n") if (divergent) { zlim <- max(abs(min_nonax), abs(max_nonax)) - if (print_nb_messages) nb(see_variable(pre_zlim <- zlim, "\n")) + if (print_nb_messages) nb(see_variable(zlim, "\n")) breaks <- (zlim) / (break_count:1) if (print_nb_messages) nb(see_variable(breaks, "\n")) breaks <- breaks - median(breaks) @@ -2185,7 +2291,7 @@ if (print_nb_messages) nb(see_variable(zlim, "\n")) } else { zlim <- max(abs(min_nonax), abs(max_nonax)) - if (print_nb_messages) nb(see_variable(pre_zlim <- zlim, "\n")) + if (print_nb_messages) nb(see_variable(zlim, "\n")) breaks <- zlim / (break_count:1) if (print_nb_messages) nb(see_variable(breaks, "\n")) if (max_nonax < 0) { @@ -2197,7 +2303,10 @@ if (print_nb_messages) nb(see_variable(zlim, "\n")) } nonax <- x - nonax[is.na(x)] <- min_nonax +# nolint start +# squash un-actionable "no visible global for ..." + if (params$heatMapNAsubstitute) nonax[is.na(x)] <- min_nonax +# nolint end if (is.null(widths)) widths <- c(denwid, 4 - denwid, 1.5) if (is.null(heights)) heights <- c(0.4, denhgt, 4.0) colors <- @@ -2212,21 +2321,20 @@ colorRampPalette(c("red", "white"))(color_count) } else { # "non-divergent" colors including zero - hcl.colors(color_count, "YlOrRd", rev = TRUE) + tmp <- hcl.colors(color_count, "YlOrRd", rev = TRUE) +# nolint start +# squash un-actionable "no visible global for ..." + tmp[1] <- params$heatMapNAgrey +# nolint end + tmp } - #ACE if (print_nb_messages) nb("within hm2plus", see_variable(key_par), "\n") - #ACE if (print_nb_messages) nb(see_variable(colors, "\n")) - #ACE key_par$col = colors - #ACE key_par$breaks = breaks - - if (print_nb_messages) nb(see_variable(par(), "\n")) #ACE TODO remove me - if (print_nb_messages) cat("\\leavevmode\n\\linebreak\n") #ACE TODO remove me + nbe("There are ", sum(is.na(x)), " NA values") + suppressWarnings( my_hm2( x = x, col = colors, - #ACE symkey = FALSE, density.info = density_info, srtCol = srtcol, srtRow = srtrow, @@ -2241,7 +2349,6 @@ trace = trace, bg = "yellow", hclustfun = hclustfun, - #ACE breaks = breaks, oldstyle = FALSE, ... # varargs ) @@ -2250,7 +2357,10 @@ } # draw_kseaapp_summary_heatmap is a helper function for ksea_heatmap +# nolint start +# squash un-actionable cyclomatic_complexity warning draw_kseaapp_summary_heatmap <- function( +# nolint end x, # matrix with row/col names already formatted sample_cluster, # a binary input of TRUE or FALSE, # indicating whether or not to perform @@ -2295,18 +2405,17 @@ } max_nchar_rowname <- max(nchar(rownames(x))) max_nchar_colname <- max(nchar(colnames(x))) - my_limit <- g_intensity_hm_rows my_row_cex_scale <- master_cex * 150 / nrow_x - #ACE row cex shrink hack begin + # row cex shrink hack begin my_row_cex_scale <- master_cex * 150 / max(nrow_x, ncol_x) - #ACE row cex shrink hack end + # row cex shrink hack end my_col_cex_scale <- 3.0 - #ACE col cex shrink hack begin + # col cex shrink hack begin if (ncol_x > 40) my_col_cex_scale <- 3.0 * 40 / ncol_x - #ACE col cex shrink hack end + # col cex shrink hack end my_asterisk_scale <- 0.4 * my_row_cex_scale my_row_warp <- 1 @@ -2340,14 +2449,6 @@ margins[2] * 0.04 * max_nchar_rowname * my_row_cex ) - my_notecex <- - my_scale * - min( - 1.1, - my_row_cex_asterisk * my_note_warp, - my_col_cex * my_note_warp - ) - if (print_trace_messages) { cat_variable(my_heights, suffix = "; ") cat_variable(my_margins, suffix = "\n\n") @@ -2403,7 +2504,10 @@ # function drawing heatmap of contrast fold-change for each kinase, # adapted from KSEAapp::KSEA.Heatmap +# nolint start +# squash un-actionable cyclomatic_complexity warning ksea_heatmap <- function( +# nolint end # the data frame outputs from the KSEA.Scores() function, in list format score_list, # a character vector of all the sample names for heatmap annotation: @@ -2429,7 +2533,7 @@ # a binary input of TRUE or FALSE, indicating whether or not to export # the heatmap as a .png image into the working directory export = FALSE, - # bottom and right margins; adjust as needehttps://tex.stackexchange.com/a/56795d if contrast names are too long + # bottom and right margins; adjust as needed if contrast names are too long margins = c(6, 6), # print which kinases? # - Mandatory argument, must be one of const_ksea_.*_kinases @@ -2484,8 +2588,6 @@ names(asterisk_rows) <- all_rows non_asterisk_rows <- names(asterisk_rows[asterisk_rows == FALSE]) asterisk_rows <- names(asterisk_rows[asterisk_rows == TRUE]) - merged_scores_asterisk <- merged_scores[names(asterisk_rows), , drop = FALSE] - merged_scores_non_asterisk <- merged_scores[names(non_asterisk_rows), , drop = FALSE] row_list <- list() row_list[[const_ksea_astrsk_kinases]] <- asterisk_rows @@ -2613,7 +2715,10 @@ keep_var_gtr_0 <- keep_var_gtr_min(0) # function drawing heatmap of phosphopeptide intensities +# nolint start +# squash un-actionable cyclomatic_complexity warning ppep_heatmap <- +# nolint end function( m, # matrix with rownames already formatted cutoff, # cutoff used by hm_heading_function @@ -2630,7 +2735,6 @@ g_default_heatmap_row_scaling, ... # passthru to hm2plus or heatmap.2 ) { - use_heatmap_1 <- FALSE peptide_count <- 0 # emit the heading for the heatmap if (hm_heading_function(m, cutoff)) { @@ -2643,8 +2747,10 @@ col_mchar_max <- max(nchar(colnames(m_margin))) row_margin <- master_cex * row_mchar_max * 2.6 col_margin <- master_cex * col_mchar_max * 2.6 - if (print_trace_messages) cat(sprintf("row_margin = %0.3f; ", row_margin)) - if (print_trace_messages) cat(sprintf("col_margin = %0.3f; ", col_margin)) + if (print_trace_messages) cat( + sprintf("row_margin = %0.3f; ", row_margin)) + if (print_trace_messages) cat( + sprintf("col_margin = %0.3f; ", col_margin)) hm_call <- NULL tryCatch( { @@ -2656,16 +2762,21 @@ m_hm <- m[peptide_count:1, , drop = FALSE] if (is.null(cellnote)) { cellnote <- matrix("", nrow = nrow(m_hm), ncol = ncol(m_hm)) - cellnote[is.na(m_hm)] <- "NA" +# nolint start +# squash un-actionable "no visible global for ..." + cellnote[is.na(m_hm)] <- params$heatMapNAcellNote +# nolint end } else { cellnote <- cellnote[peptide_count:1, , drop = FALSE] } - m_hm[is.na(m_hm)] <- 0 +# nolint start +# squash un-actionable "no visible global for ..." + if (params$heatMapNAsubstitute) m_hm[is.na(m_hm)] <- 0 +# nolint end nrow_m_hm <- nrow(m_hm) ncol_m_hm <- ncol(m_hm) if (nrow_m_hm < 1 || ncol_m_hm < 1) return(peptide_count) # return zero as initialized above - my_limit <- g_intensity_hm_rows my_row_cex <- master_cex * (100 / (2 + row_mchar_max)) @@ -2673,8 +2784,10 @@ my_col_adj <- min(my_col_cex, my_row_cex) / my_col_cex my_col_cex <- min(my_col_cex, my_row_cex) col_margin <- sqrt(my_col_adj) * col_margin - if (print_trace_messages) cat(sprintf("my_row_cex = %0.3f; ", my_row_cex)) - if (print_trace_messages) cat(sprintf("my_col_cex = %0.3f; ", my_col_cex)) + if (print_trace_messages) cat( + sprintf("my_row_cex = %0.3f; ", my_row_cex)) + if (print_trace_messages) cat( + sprintf("my_col_cex = %0.3f; ", my_col_cex)) if (is.null(margins)) my_margins <- c( (my_col_cex + col_margin), # col @@ -2690,7 +2803,9 @@ ) ) my_hm2_cex <- 2 * master_cex - my_key_cex <- 0.9 - 0.1 * (g_intensity_hm_rows + nrow_m_hm) / g_intensity_hm_rows + my_key_cex <- + 0.9 - + 0.1 * (g_intensity_hm_rows + nrow_m_hm) / g_intensity_hm_rows my_key_warp <- 1.5 * 22.75 / row_margin my_key_cex <- min(1.10, my_key_warp * my_key_cex) my_hgt_scale <- 3.70 - 0.4 * (max(1, 0.9 * my_row_cex) - 1) @@ -2705,7 +2820,8 @@ my_plot_height <- (0.566 + 0.354 * (nrow_m / g_intensity_hm_rows)) * min(my_hgt_scale, my_hgt_scale * my_warp) - my_plot_height <- min(3.65, my_plot_height * g_intensity_hm_rows / 50) + my_plot_height <- + min(3.65, my_plot_height * g_intensity_hm_rows / 50) my_heights <- c( 0.3, # title and top dendrogram my_plot_height, # plot and bottom margin @@ -2770,6 +2886,12 @@ srtrow = 0, xlab = "", cellnote = cellnote, + # ref for na.color: + # https://cran.r-project.org/web/packages/gplots/gplots.pdf +# nolint start +# squash un-actionable "no visible global for ..." + na.color = params$heatMapNAcellColor, +# nolint end notecex = my_note_cex, ... ) @@ -2833,8 +2955,8 @@ cat( sprintf( "\n%s %s Internal message: %s\n\\newline\n\n", - "Failure drawing heatmap,", - "possibly because of too many missing values.\n\\newline\n\n", + "Failure drawing heatmap, possibly because of ", + "too many missing values.\n\\newline\n\n", r$message ) ) @@ -2843,7 +2965,8 @@ ) } else { cat( - "\nFailure drawing heatmap, possibly because of too many missing values.\n" + "\nFailure drawing heatmap, ", + "possibly because of too many missing values.\n" ) } } @@ -2855,17 +2978,19 @@ } # function drawing heatmap of correlations if they exist, else covariances +# nolint start +# squash un-actionable cyclomatic_complexity warning +# squash un-actionable "no visible global for ..." cov_heatmap <- function( +# nolint end m, # matrix with rownames already formatted + kinase_name, top_substrates = FALSE, ... # passthru to hm2plus or heatmap.2 ) { - if (print_nb_messages) nbe(see_variable(m), " [", nrow(m), "x", ncol(m), "\n") - #ACE nb(rowSums(m, na.rm = TRUE)) - #ACE bad_rows <- (rowSums(m, na.rm = TRUE) == 0) - #ACE nb(see_variable(bad_rows)) - #ACE m <- m[-bad_rows, , drop = FALSE] + if (print_nb_messages) nbe( + see_variable(m), " [", nrow(m), "x", ncol(m), "\n") colnames_m <- colnames(m) is_na_m <- is.na(m) tmp <- m @@ -2903,7 +3028,6 @@ t_m <- t(tmp) t_m[is.na(t_m)] <- 0 - prefiltered_nrow <- ncol(t_m) my_corcov <- cov(t_m) did_filter_rows <- did_filter_cols <- FALSE @@ -2944,7 +3068,7 @@ function(is_corr) { cat( sprintf( - "Below is the %s plot for %s substrates", + "Below is the %s plot for %s sites", if (is_corr) "correlation" else "covariance", sprintf( if (top_substrates) @@ -3006,7 +3130,7 @@ on.exit(par(parjust)) my_corcov <- my_corcov[order(rownames(my_corcov)), ] my_main <- - sprintf("%s among %s substrates %s", + sprintf("%s among %s sites %s", if (my_correlate_substrates) "Correlation" else "Covariance", kinase_name, @@ -3025,13 +3149,17 @@ ) if (print_trace_messages) cat(sprintf("max_nchar = %0.3f; ", max_nchar)) if (print_trace_messages) cat(sprintf("my_margin = %0.3f; ", my_margin)) - if (print_trace_messages) cat(sprintf("my_plot_height = %0.3f\n\n", my_plot_height)) + if (print_trace_messages) cat( + sprintf("my_plot_height = %0.3f\n\n", my_plot_height)) if (print_trace_messages) cat(sprintf("master_cex = %0.3f; ", master_cex)) if (print_trace_messages) cat(sprintf("my_row_cex = %0.3f; ", my_row_cex)) if (print_trace_messages) cat(sprintf("my_col_cex = %0.3f; ", my_col_cex)) - if (print_trace_messages) cat(sprintf("my_key_cex = %0.3f\n\n", my_key_cex)) - if (print_trace_messages) cat(sprintf("my_hgt_scale = %0.3f\n\n", my_hgt_scale)) - if (print_trace_messages) cat(sprintf("legend height = %0.3f\n\n", my_legend_height)) + if (print_trace_messages) cat( + sprintf("my_key_cex = %0.3f\n\n", my_key_cex)) + if (print_trace_messages) cat( + sprintf("my_hgt_scale = %0.3f\n\n", my_hgt_scale)) + if (print_trace_messages) cat( + sprintf("legend height = %0.3f\n\n", my_legend_height)) if (print_trace_messages) cat( sprintf( "my_heights = c(%s); sum = %0.3f\n\n", @@ -3673,7 +3801,8 @@ # Take the accurate ln(x+1) because the data are log-normally distributed # and because median can involve an average of two measurements. quant_data_imp <- log1p(quant_data_imp) - quant_data_imp[ind] <- row_apply(quant_data_imp, median, na.rm = TRUE)[ind[, 1]] + quant_data_imp[ind] <- + row_apply(quant_data_imp, median, na.rm = TRUE)[ind[, 1]] # Take the accurate e^x - 1 to match scaling of original input. quant_data_imp <- round(expm1(quant_data_imp_ln <- quant_data_imp)) good_rows <- !is.nan(rowMeans(quant_data_imp)) @@ -3689,14 +3818,16 @@ # this will have to be quant_data_imp <- log1p(quant_data_imp) # Assign to NA cells the mean for the row - quant_data_imp[ind] <- row_apply(quant_data_imp, mean, na.rm = TRUE)[ind[, 1]] + quant_data_imp[ind] <- + row_apply(quant_data_imp, mean, na.rm = TRUE)[ind[, 1]] # Take the accurate e^x - 1 to match scaling of original input. quant_data_imp <- round(expm1(quant_data_imp_ln <- quant_data_imp)) good_rows <- !is.nan(rowMeans(quant_data_imp)) } , "random" = { quant_data_imp <- quant_data - m1 <- median(sds, na.rm = TRUE) * sd_percentile #sd to be used is the median sd + # sd to be used is the median sd + m1 <- median(sds, na.rm = TRUE) * sd_percentile # If you want results to be reproducible, you will want to call # base::set.seed before calling stats::rnorm imputation_method_description <- @@ -3735,7 +3866,8 @@ imp_smry_missing_values_after <- sum(is.na(quant_data_imp[good_rows, ])) # From ?`%in%`: -# %in% is currently defined as function(x, table) match(x, table, nomatch = 0) > 0 +# %in% is currently defined +# as function(x, table) match(x, table, nomatch = 0) > 0 stock_in <- names(good_rows) %in% @@ -3798,27 +3930,26 @@ ```{r filter_good_rows, echo = FALSE} -if (print_nb_messages) nbe("before name extraction, ", see_variable(length(good_rows)), " ", see_variable(good_rows), "\n") +if (print_nb_messages) nbe( + "before name extraction, ", see_variable(length(good_rows)), " ", + see_variable(good_rows), "\n") good_rows <- names(good_rows[names(great_enough_row_names)]) -if (print_nb_messages) nbe("after name extraction, ", see_variable(length(good_rows)), see_variable(good_rows), "\n") - -#ACE min_group_obs_count <- min_group_obs_count[names(great_enough_row_names)] -#ACE nbe("good_rows") -#ACE nbe(see_variable(good_rows)) -#ACE nbe("names(min_group_obs_count) before filter for good rows") -#ACE nbe(see_variable(names(min_group_obs_count))) +if (print_nb_messages) nbe( + "after name extraction, ", see_variable(length(good_rows)), + see_variable(good_rows), "\n") + min_group_obs_count <- min_group_obs_count[good_rows] -#ACE nbe("min_group_obs_count after filter for good rows") -#ACE nbe(see_variable(names(min_group_obs_count))) # Zap rows where imputation was insufficiently effective full_data <- full_data [good_rows, ] quant_data <- quant_data [good_rows, ] quant_data_log <- quant_data_log [good_rows, ] -if (print_nb_messages) nbe("before row filter, ", see_variable(nrow(quant_data_imp)), "\n") +if (print_nb_messages) nbe( + "before row filter, ", see_variable(nrow(quant_data_imp)), "\n") quant_data_imp <- quant_data_imp[good_rows, ] -if (print_nb_messages) nbe("after row filter, ", see_variable(nrow(quant_data_imp)), "\n") +if (print_nb_messages) nbe( + "after row filter, ", see_variable(nrow(quant_data_imp)), "\n") write_debug_file(quant_data_imp) quant_data_imp_good_rows <- quant_data_imp @@ -3947,7 +4078,8 @@ , sample_name_grow = sample_name_grow , main = "Peptide intensities after eliminating unusable peptides" , varwidth = boxplot_varwidth - , sub = paste(boxplot_sub, "Box widths reflect number of peptides for sample") + , sub = paste(boxplot_sub, + "Box widths reflect number of peptides for sample") , xlab = "Sample" , ylab = latex2exp::TeX("$log_{10}$(peptide intensity)") , col = const_boxplot_fill @@ -4283,7 +4415,7 @@ For each phosphopeptide, ANOVA analysis was performed to compute a $p$-value representing the evidence against the "null hypothesis" ($H_0$) that the intensity does not vary significantly among sample groups. -However, because as more and more phosphopeptides are tested, there is increasd probability that, by random chance, a given peptide will have a $p$-value that appears to indicate significance. For this reason, the $p$-values were adjusted by applying the False Discovery Rate (FDR) correction from Benjamini and Hochberg (1995) [doi:10.1111/j.2517-6161.1995.tb02031.x](https:/doi.org/10.1111/j.2517-6161.1995.tb02031.x). +However, because as more and more phosphopeptides are tested, there is increasd probability that, by random chance, a given peptide will have a $p$-value that appears to indicate significance. For this reason, the $p$-values were adjusted by applying the False Discovery Rate (FDR) correction from Benjamini and Hochberg (1995) [doi:10.1111/j.2517-6161.1995.tb02031.x](https://doi.org/10.1111/j.2517-6161.1995.tb02031.x). Furthermore, to give more weight to phosphopeptides having fewer missing values, an (arbitrarily defined) quality score was assigned to each, defined as: @@ -4313,7 +4445,11 @@ ```{r anova, echo = FALSE, fig.dim = c(10, 12), results = 'asis'} g_can_run_ksea <- FALSE old_oma <- par("oma") + +# nolint start +# squash un-actionable cyclomatic_complexity warning if (count_of_treatment_levels < 2) { +# nolint end cat( "ERROR!!!! Cannot perform ANOVA analysis", "(see next page)\\newpage\n" @@ -4367,7 +4503,6 @@ if (print_nb_messages) nbe("computing p_value_data_anova_ps\n") if (print_nb_messages) nbe(see_variable(nrow(quant_data_imp_qn_log)), "\n") if (print_nb_messages) nbe(see_variable(ncol(quant_data_imp_qn_log)), "\n") - if (print_nb_messages) nbe(see_variable(quant_data_imp_qn_log[, ".NE.7C"]), "\n") if (print_nb_messages) nbe(see_variable(quant_data_imp_qn_log), "\n") if (print_nb_messages) nbe(see_variable(anova_func), "\n") if (print_nb_messages) nbe(see_variable(smpl_trt), "\n") @@ -4477,6 +4612,11 @@ break } + if (print_trace_messages) { + cat_variable(cutoff) + cat("\n\n") + } + bool_1 <- (p_value_data$fdr_adjusted_anova_p < cutoff) bool_2 <- (p_value_data$min_group_obs_count >= g_intensity_min_per_class) g_can_run_ksea <- g_can_run_ksea || (sum(bool_2) > 0) @@ -4509,6 +4649,7 @@ cat_variable(filtered_p, force_str = TRUE) have_remaining_peptides <- sum(bool_3, na.rm = TRUE) > 0 + filter_result_string <- sprintf( "%s, %s of %0.0f peptides remained having both %s and %s.\n\n", @@ -4518,7 +4659,8 @@ else "none", length(bool_3), - sprintf("adjusted p-value < %s", as.character(signif(cutoff, 2))), + adj_p_string <- + sprintf("adjusted p-value < %s", as.character(signif(cutoff, 2))), sprintf( "more than %0.0f observations in some groups", max(0, g_intensity_min_per_class - 1) @@ -4537,7 +4679,10 @@ , drop = FALSE ] - if (have_remaining_peptides && nrow(filtered_p) > 0 && nrow(filtered_data_filtered) > 0) { + if (have_remaining_peptides + && nrow(filtered_p) > 0 + && nrow(filtered_data_filtered) > 0 + ) { if (first_page_suppress == 1) { first_page_suppress <- 0 } else { @@ -4547,14 +4692,21 @@ cat(filter_result_string) if (nrow(filtered_data_filtered) > 1) { cat(subsection_header(sprintf( - "Intensity distributions for %d phosphopeptides\n", - nrow(filtered_data_filtered) + "Intensity distributions for %d phosphopeptides, %s\n", + nrow(filtered_data_filtered), + adj_p_string ))) } else { - cat(subsection_header(sprintf( - "Intensity distribution for one phosphopeptide (%s)\n", - rownames(filtered_data_filtered)[1] - ))) + cat( + subsection_header( + sprintf( + "Intensity distribution for one phosphopeptide, %s\n", + adj_p_string + ) + ), + rownames(filtered_data_filtered)[1], + "\n" + ) } }) # end latex_samepage @@ -4637,7 +4789,7 @@ ) )) } else { - if (nrow(m) == 0) { + if (nrow(m) < 2) { return(FALSE) } else { cat(subsection_header( @@ -4661,7 +4813,7 @@ if (nrow_m > 0) { rownames_m <- rownames(m) q <- data.frame(pepname = rownames_m) - g <- sqldf(" + g <- sqldf::sqldf(" SELECT q.pepname, substr(met.Gene_Name, 1, 30) AS gene_name FROM q, metadata_plus_p AS met WHERE q.pepname = met.Phosphopeptide @@ -4718,7 +4870,7 @@ } } } -cat(filter_result_string) + cat("\\leavevmode\n") if (!g_can_run_ksea) { @@ -4747,9 +4899,11 @@ sprintf("%0.3g", display_p_value_data$quality) headers_1st_line <- - c("", "Raw ANOVA", "FDR-adj.", "Missing", "Min. #", "", "") + c("", "Raw ANOVA", "FDR-adj.", "Missing", "Min. #", + "", "") headers_2nd_line <- - c("Phosphopeptide", "p-value", "p-value", "values", "group-obs", "Quality", "Ranking") + c("Phosphopeptide", "p-value", "p-value", "values", "group-obs", + "Quality", "Ranking") data_frame_tabbing_latex( x = display_p_value_data, tabstops = c(2.75, 0.80, 0.80, 0.5, 0.6, 0.60), @@ -5335,9 +5489,9 @@ Results of Kinase-Substrate Enrichment Analysis are presented here, if the substrates for any kinases are relatively enriched. Enrichments are found by the CRAN `KSEAapp` package: -- The package is available on CRAN, at https:/cran.r-project.org/package=KSEAapp -- The method used is described in Casado et al. (2013) [doi:10.1126/scisignal.2003573](https:/doi.org/10.1126/scisignal.2003573) and Wiredja et al (2017) [doi:10.1093/bioinformatics/btx415](https:/doi.org/10.1093/bioinformatics/btx415). -- An online alternative (supporting only analysis of human data) is available at [https:/casecpb.shinyapps.io/ksea/](https:/casecpb.shinyapps.io/ksea/). +- The package is available on CRAN, at https://cran.r-project.org/package=KSEAapp +- The method used is described in Casado et al. (2013) [doi:10.1126/scisignal.2003573](https://doi.org/10.1126/scisignal.2003573) and Wiredja et al (2017) [doi:10.1093/bioinformatics/btx415](https://doi.org/10.1093/bioinformatics/btx415). +- An online alternative (supporting only analysis of human data) is available at [https://casecpb.shinyapps.io/ksea/](https://casecpb.shinyapps.io/ksea/). For each kinase, $i$, and each two-way contrast of treatments, $j$, an enrichment $z$-score is computed as: @@ -5461,7 +5615,7 @@ kseaapp_input <- sqldf::sqldf( x = sprintf(" - SELECT `Protein`, `Gene`, `Peptide`, phosphopep AS `Residue.Both`, `p`, `FC` + SELECT `Protein`,`Gene`,`Peptide`,phosphopep AS `Residue.Both`,`p`,`FC` FROM v_kseaapp_input WHERE contrast = %d ", @@ -5503,21 +5657,19 @@ } if (FALSE) { + if (print_nb_messages) nbe( + "Output contents of `ksea_scores_rslt` table\n") + cat_variable(ksea_scores_rslt) + cat("\n\\clearpage\n") data_frame_tabbing_latex( x = ksea_scores_rslt, - tabstops = c(0.8, 0.8, 0.8, 0.8, 0.8, 0.8), + tabstops = c(1.8, 0.8, 0.8, 0.3, 0.8, 0.8), caption = paste("KSEA scores for contrast ", cntrst_b_level, "to", cntrst_a_level), use_subsubsection_header = FALSE ) } - if (FALSE) { - if (print_nb_messages) nbe("Output contents of `ksea_scores_rslt` table\n") - cat_variable(ksea_scores_rslt) - cat("\n\\clearpage\n") - } - if (0 < sum(!is.nan(ksea_scores_rslt$FDR))) { next_index <- 1 + next_index rslt$score_list[[next_index]] <- ksea_scores_rslt @@ -5627,7 +5779,7 @@ tryCatch( expr = { ksea_scores_rslt <- rslt$score_list[[next_index]] - if (print_nb_messages) nbe(see_variable(ksea_scores_rslt)) #ACE + if (print_nb_messages) nbe(see_variable(ksea_scores_rslt)) if (0 < sum(!is.nan(ksea_scores_rslt$FDR))) { sink(deferred <- file()) @@ -5760,12 +5912,6 @@ version = loaded_packages_df$loadedversion, date = loaded_packages_df$date ) - #ACE cat("\\clearpage\n\\section{R package versions}\n") - #ACE data_frame_tabbing_latex( - #ACE x = loaded_packages_df, - #ACE tabstops = c(2.5, 1.25), - #ACE caption = "R package versions" - #ACE ) cat("\\clearpage\n\\section{Input parameter settings}\n") data_frame_tabbing_latex( x = param_df, @@ -5786,7 +5932,10 @@ ``` ```{r kseabar, echo = FALSE, fig.dim = c(9.5, 12.3), results = 'asis'} +# nolint start +# squash un-actionable cyclomatic_complexity warning if (have_kinase_descriptions) { +# nolint end my_section_header <- sprintf( "inases whose KSEA %s < %s\n", @@ -5826,14 +5975,14 @@ } if (FALSE) { - cat_variable(sqldf("SELECT kinase FROM enriched_kinases")) - cat_variable(sqldf(" + cat_variable(sqldf::sqldf("SELECT kinase FROM enriched_kinases")) + cat_variable(sqldf::sqldf(" SELECT DISTINCT gene, sub_gene, SUB_MOD_RSD AS ppep FROM pseudo_ksdata WHERE gene IN (SELECT kinase FROM enriched_kinases) ")) data_frame_table_latex( - x = sqldf(" + x = sqldf::sqldf(" SELECT DISTINCT gene, sub_gene, SUB_MOD_RSD AS ppep FROM pseudo_ksdata WHERE gene IN (SELECT kinase FROM enriched_kinases) @@ -5845,7 +5994,7 @@ underscore_whack = TRUE ) data_frame_table_latex( - x = sqldf(" + x = sqldf::sqldf(" SELECT gene AS kinase, ppep, @@ -5871,7 +6020,7 @@ underscore_whack = TRUE ) } - all_enriched_substrates <- sqldf(" + all_enriched_substrates <- sqldf::sqldf(" SELECT gene AS kinase, ppep, @@ -5899,9 +6048,7 @@ ] all_enriched_substrates$sub_gene <- - sub( - " ///.*", - " ...", + sub(" ///.*", "...", all_enriched_substrates$sub_gene ) @@ -5928,7 +6075,7 @@ if (nrow(m) > g_intensity_hm_rows) { cat(subsection_header( sprintf( - "Highest-quality %d (of %d) enriched %s-substrates", + "Highest-quality %d (of %d) enriched %s-sites", g_intensity_hm_rows, nrow(m), kinase @@ -5941,7 +6088,7 @@ nrow_m <- nrow(m) cat(subsection_header( sprintf( - "%d enriched %s-substrate%s", + "%d enriched %s-site%s", nrow_m, kinase, if (nrow_m > 1) "s" else "" @@ -6025,7 +6172,10 @@ ``` ```{r enriched, echo = FALSE, fig.dim = c(12, 13.7), results = 'asis'} +# nolint start +# squash un-actionable cyclomatic_complexity warning if (g_can_run_ksea) { +# nolint end g_did_enriched_header <- FALSE for (kinase_name in sort(enriched_kinases$kinase)) { enriched_substrates <- @@ -6048,11 +6198,13 @@ ) # Get the intensity values for the heatmap enriched_intensities <- - as.matrix(unimputed_quant_data_log[enriched_substrates$ppep, , drop = FALSE]) + as.matrix(unimputed_quant_data_log[ + enriched_substrates$ppep, , drop = FALSE + ]) # Remove rows having too many NA values to be relevant good_rows <- (rowSums(enriched_intensities, na.rm = TRUE) != 0) - #ACE nbe(see_variable(good_rows), "\n") + enriched_substrates <- enriched_substrates[good_rows, , drop = FALSE] enriched_intensities <- enriched_intensities[good_rows, , drop = FALSE] @@ -6077,16 +6229,6 @@ m <- as.matrix(enriched_intensities) rownames(m) <- trunc_enriched_substrate(rownames(m)) - #ACE nb("m with bad rows: ", see_variable(m), "\n") - #ACE good_rows <- (rowSums(m, na.rm = TRUE) != 0) - #ACE nb(see_variable(good_rows), "\n") - #ACE m <- m[good_rows, , drop = FALSE] - #ACE nb("m without(?) bad rows: ", see_variable(m), "\n") - #ACE nb(see_variable(short_row_names), "\n") - #ACE local_short_row_names <- short_row_names[good_rows] - #ACE local_long_row_names <- long_row_names[good_rows] - #ACE local_enriched_intensities <- enriched_intensities[local_long_row_names, ] - # Draw the heading and heatmap nrow_m <- nrow(m) if (nrow_m > 0) { @@ -6098,7 +6240,7 @@ is_na_m <- is.na(m) cellnote_m <- is_na_m cellnote_m[!is_na_m] <- "" - cellnote_m[is_na_m] <- "NA" + cellnote_m[is_na_m] <- params$heatMapNAcellNote cut_args <- new_env() cut_args$cutoff <- cutoff cut_args$kinase <- kinase_name @@ -6110,8 +6252,9 @@ cellnote = cellnote_m, cutoff = cut_args, hm_heading_function = cat_enriched_heading, - hm_main_title - = "Unnormalized (zero-imputed) intensities of enriched kinase-substrates", + hm_main_title = paste( + "Unnormalized (zero-imputed)", + "intensities of enriched kinase-substrates"), suppress_row_dendrogram = FALSE, master_cex = 0.35, sepcolor = "black", @@ -6123,7 +6266,11 @@ tryCatch( { rownames(m) <- short_row_names - cov_heatmap(m, nrow_m > g_intensity_hm_rows) + cov_heatmap( + m = m, + kinase_name = kinase_name, + top_substrates = nrow_m > g_intensity_hm_rows + ) }, error = function(e) { cat( @@ -6162,7 +6309,7 @@ cat("\n\\newpage\n") cat(subsubsection_header( sprintf( - "Details for %s%s-substrates", + "Details for %s%s-sites", if (excess_substrates) sprintf( "%s \"highest quality\" ", @@ -6190,7 +6337,7 @@ if (print_nb_messages) nb("kinase_ppep_label <- ...\n") if (print_nb_messages) nbe("kinase_ppep_label <- ...\n") - kinase_ppep_label <- sqldf(" + kinase_ppep_label <- sqldf::sqldf(" WITH t(ppep, label) AS ( @@ -6230,14 +6377,15 @@ WHERE f.Phosphopeptide = q.Phosphopeptide " - data_table_imputed <- sqldf(data_table_imputed_sql) + data_table_imputed <- sqldf::sqldf(data_table_imputed_sql) # Zap the duplicated 'Phosphopeptide' column named 'ppep' data_table_imputed <- data_table_imputed[, c(1:12, 14:ncol(data_table_imputed))] # Output imputed, un-normalized data if (print_nb_messages) nb("Output imputed, un-normalized data tabular file\n") - if (print_nb_messages) nbe("Output imputed, un-normalized data tabular file\n") + if (print_nb_messages) nbe( + "Output imputed, un-normalized data tabular file\n") write.table( data_table_imputed , file = imputed_data_filename @@ -6251,7 +6399,7 @@ #output quantile normalized data impish <- cbind(rownames(quant_data_imp_qn_log), quant_data_imp_qn_log) colnames(impish)[1] <- "Phosphopeptide" - data_table_imputed <- sqldf(data_table_imputed_sql) + data_table_imputed <- sqldf::sqldf(data_table_imputed_sql) # Zap the duplicated 'Phosphopeptide' column named 'ppep' data_table_imputed <- data_table_imputed[, c(1:12, 14:ncol(data_table_imputed))] @@ -6266,7 +6414,7 @@ quote = FALSE ) - ppep_kinase <- sqldf(" + ppep_kinase <- sqldf::sqldf(" SELECT DISTINCT k.ppep, k.kinase FROM ( SELECT DISTINCT gene AS kinase, SUB_MOD_RSD AS ppep @@ -6314,8 +6462,11 @@ " ) -if (print_nb_messages) nb("Output contents of `stats_metadata_v` table to tabular file\n") -if (print_nb_messages) nbe("Output contents of `stats_metadata_v` table to tabular file\n") +if (print_nb_messages) nb( + "Output contents of `stats_metadata_v` table to tabular file\n") +if (print_nb_messages) nbe( + "Output contents of `stats_metadata_v` table to tabular file\n") + write.table( dbReadTable(db, "stats_metadata_v"), file = anova_ksea_mtdt_file,