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Changeset 6:43c146e25a43 (2021-04-15)

Previous changeset 5:7608d5faee41 (2020-01-29) Next changeset 7:d45f092caf24 (2021-12-02)

Commit message:
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit 53916f6803b93234f992f5fd4fad61d7013d82af"

modified:
macros.xml
macros_cheetah.xml
raceid_filtnormconf.xml
scripts/cluster.R
scripts/clusterinspect.R
scripts/pseudotemporal.R
scripts/trajectoryinspect.R
test-data/intestinal.pdf
test-data/intestinal_advanced.filter.pdf
test-data/intestinal_advanced.pdf
test-data/matrix.filter.geqone.pdf
test-data/matrix.filter.pdf
test-data/matrix.filter.rdat
test-data/matrix2.pdf
test-data/matrix2.rdat
test-data/out_cluster_default.rdat
test-data/out_traject_adv_nondef.pdf
test-data/out_traject_default.ltree.rdat
test-data/out_traject_default.pdf
test-data/out_traject_inspect_allthree.pdf
test-data/out_traject_inspect_fateid.pdf
test-data/out_traject_inspect_stemid.pdf

removed:
test-data/intestinal.genelist
test-data/intestinal_advanced.genelist
test-data/matrix2.genelist

diff -r 7608d5faee41 -r 43c146e25a43 macros.xml
--- a/macros.xml Wed Jan 29 17:16:02 2020 -0500
+++ b/macros.xml Thu Apr 15 19:00:38 2021 +0000

[

@@ -25,8 +25,8 @@
     return(unlist(strsplit(string,",")))
}
     </token>
-    <token name="@VERSION_RACEID@">3</token>
-    <token name="@VERSION_WRAPPER@">1</token>
+    <token name="@VERSION_RACEID@">0.2.3</token>
+    <token name="@VERSION_WRAPPER@">0</token>

     <macro name="version_command_config" token_prog="temp" token_cheetah="temp2" token_out="2> '$outlog'">
         <version_command><![CDATA[
@@ -47,8 +47,8 @@

     <macro name="requirements" >
         <requirements>
-            <requirement type="package" version="0.1.1">r-raceid</requirement>
-            <requirement type="package" version="1.6.2">bioconductor-scran</requirement>
+            <requirement type="package" version="@VERSION_RACEID@" >r-raceid</requirement>
+            
         </requirements>
     </macro>
     <macro name="yesno_checkedno" >

diff -r 7608d5faee41 -r 43c146e25a43 macros_cheetah.xml
--- a/macros_cheetah.xml Wed Jan 29 17:16:02 2020 -0500
+++ b/macros_cheetah.xml Thu Apr 15 19:00:38 2021 +0000

@@ -127,15 +127,19 @@
outlier.rfcorrect\$nbfactor = as.integer( '$outlier.use.nbfactor' )
#end if

+cluster.compumap = formals(compumap)
cluster.comptsne = formals(comptsne)
cluster.compfr = formals(compfr)

cluster.comptsne\$perplexity = as.integer( '$tsne.perplexity' )
cluster.compfr\$knn = as.integer( '$tsne.knn' )
+cluster.compumap\$n_neighbors = as.integer( '$tsne.umap_nn' )
#if str($tsne.use.def) == "no":
cluster.comptsne\$initial_cmd = as.logical( '$tsne.use.initial_cmd' )
cluster.comptsne\$rseed = as.integer( '$tsne.use.rseed_tsne' )
cluster.compfr\$rseed = as.integer( '$tsne.use.rseed_fr' )
+cluster.compumap\$n_epochs = as.integer( '$tsne.use.umap_epochs' )
+cluster.compumap\$min_dist = as.numeric( '$tsne.use.umap_min_dist' )
#end if

genelist.tablelim = as.integer( '$extra.tablelim' )
@@ -227,6 +231,7 @@
perform.diffgene = TRUE
plotdiffg\$Aname = '$diffgtest.set_a.name_set'
plotdiffg\$Bname = '$diffgtest.set_b.name_set'
+plotdiffg\$... = NULL

gfdat.A.use = list()
gfdat.B.use = list()
@@ -294,7 +299,7 @@
pstc.plotgraph\$showCells = as.logical( '$plotgraph.showcells' )
pstc.plotgraph\$scthr = as.numeric( '$plotgraph.scthr' )
#if str($plotgraph.use.def) == "no":
-pstc.plotgraph\$showTsne = as.logical( '$plotgraph.use.showtsne' )
+##pstc.plotgraph\$showTsne = as.logical( '$plotgraph.use.showtsne' )
pstc.plotgraph\$tp = as.numeric( '$plotgraph.use.tp' )
#end if

diff -r 7608d5faee41 -r 43c146e25a43 raceid_filtnormconf.xml
--- a/raceid_filtnormconf.xml Wed Jan 29 17:16:02 2020 -0500
+++ b/raceid_filtnormconf.xml Thu Apr 15 19:00:38 2021 +0000

@@ -1,4 +1,4 @@
-<tool id="raceid_filtnormconf" name="Initial processing using RaceID" version="@VERSION_RACEID@.@VERSION_WRAPPER@" >
+<tool id="raceid_filtnormconf" name="Initial processing using RaceID" version="@VERSION_RACEID@+galaxy@VERSION_WRAPPER@" >
     <description>performs filtering, normalisation, and confounder removal to generate a normalised and filtered count matrix of single-cell RNA data</description>
     <macros>
         <import>macros.xml</import>
@@ -27,7 +27,7 @@
                 <param name="ccor" type="float" value="0.4" label="CCor" help="Correlation coefficient used as a threshold for determining correlated genes" />
                 <param name="bmode" type="select" label="Batch Mode" help="Method to regress out batch effects" >
                     <option value="RaceID" selected="true" >RaceID</option>
-                    <option value="scran">SCRAN</option>
+                    
                 </param>
                 <conditional name="ccc" >
                     <param name="use" type="select" label="Perform Cell-cycle correction?" >
@@ -120,7 +120,7 @@
                     <param name="CGenes" value="Gga3,Ggact,Ggct" />
                     <param name="FGenes" value="Zxdc,Zyg11a,Zyg11b,Zyx" />
                     <param name="LBatch_regexes" value="^I5,^II5,^III5,^IV5d,^V5d" />
-                    <param name="bmode" value="scran" />
+                    
                     <conditional name="ccc" >
                         <param name="use" value="yes" />
                         <param name="pvalue" value="0.05" />

diff -r 7608d5faee41 -r 43c146e25a43 scripts/cluster.R
--- a/scripts/cluster.R Wed Jan 29 17:16:02 2020 -0500
+++ b/scripts/cluster.R Thu Apr 15 19:00:38 2021 +0000

[

b'@@ -1,91 +1,77 @@\n #!/usr/bin/env R\n-VERSION = "0.5"\n+VERSION <- "0.5" # nolint\n \n-args = commandArgs(trailingOnly = T)\n+args <- commandArgs(trailingOnly = T)\n \n-if (length(args) != 1){\n+if (length(args) != 1) {\n message(paste("VERSION:", VERSION))\n stop("Please provide the config file")\n }\n \n suppressWarnings(suppressPackageStartupMessages(require(RaceID)))\n-suppressWarnings(suppressPackageStartupMessages(require(scran)))\n+## suppressWarnings(suppressPackageStartupMessages(require(scran))) # nolint\n source(args[1])\n \n \n-do.filter <- function(sc){\n- if (!is.null(filt.lbatch.regexes)){\n+do.filter <- function(sc) { # nolint\n+ if (!is.null(filt.lbatch.regexes)) {\n lar <- filt.lbatch.regexes\n nn <- colnames(sc@expdata)\n- filt$LBatch <- lapply(1:length(lar), function(m){ return( nn[grep(lar[[m]], nn)] ) })\n+ filt$LBatch <- lapply(1:length(lar), function(m) { # nolint\n+ return(nn[grep(lar[[m]], nn)])})\n }\n \n sc <- do.call(filterdata, c(sc, filt))\n \n ## Get histogram metrics for library size and number of features\n- raw.lib <- log10(colSums(as.matrix(sc@expdata)))\n- raw.feat <- log10(colSums(as.matrix(sc@expdata)>0))\n- filt.lib <- log10(colSums(getfdata(sc)))\n- filt.feat <- log10(colSums(getfdata(sc)>0))\n+ raw_lib <- log10(colSums(as.matrix(sc@expdata)))\n+ raw_feat <- log10(colSums(as.matrix(sc@expdata) > 0))\n+ filt_lib <- log10(colSums(as.matrix(getfdata(sc))))\n+ filt_feat <- log10(colSums(as.matrix(getfdata(sc) > 0)))\n \n- if (filt.geqone){\n- filt.feat <- log10(colSums(getfdata(sc)>=1))\n+ if (filt.geqone) {\n+ filt_feat <- log10(colSums(as.matrix(getfdata(sc) >= 1))) # nolint\n }\n \n br <- 50\n- ## Determine limits on plots based on the unfiltered data\n- ## (doesn\'t work, R rejects limits and norm data is too different to compare to exp data\n- ## so let them keep their own ranges)\n-\n- ## betterrange <- function(floatval){\n- ## return(10 * (floor(floatval / 10) + 1))\n- ## }\n-\n- ## tmp.lib <- hist(raw.lib, breaks=br, plot=F)\n- ## tmp.feat <- hist(raw.feat, breaks=br, plot=F)\n-\n- ## lib.y_lim <- c(0,betterrange(max(tmp.lib$counts)))\n- ## lib.x_lim <- c(0,betterrange(max(tmp.lib$breaks)))\n-\n- ## feat.y_lim <- c(0,betterrange(max(tmp.feat$counts)))\n- ## feat.x_lim <- c(0,betterrange(max(tmp.feat$breaks)))\n-\n- par(mfrow=c(2,2))\n- print(hist(raw.lib, breaks=br, main="RawData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)\n- print(hist(raw.feat, breaks=br, main="RawData Log10 NumFeat")) #, xlim=feat.x_lim, ylim=feat.y_lim)\n- print(hist(filt.lib, breaks=br, main="FiltData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)\n- tmp <- hist(filt.feat, breaks=br, main="FiltData Log10 NumFeat") # , xlim=feat.x_lim, ylim=feat.y_lim)\n+ par(mfrow = c(2, 2))\n+ print(hist(raw_lib, breaks = br, main = "RawData Log10 LibSize"))\n+ print(hist(raw_feat, breaks = br, main = "RawData Log10 NumFeat"))\n+ print(hist(filt_lib, breaks = br, main = "FiltData Log10 LibSize"))\n+ tmp <- hist(filt_feat, breaks = br, main = "FiltData Log10 NumFeat")\n print(tmp)\n ## required, for extracting midpoint\n- unq <- unique(filt.feat)\n- if (length(unq) == 1){\n- abline(v=unq, col="red", lw=2)\n- text(tmp$mids, table(filt.feat)[[1]] - 100, pos=1, paste(10^unq, "\\nFeatures\\nin remaining\\nCells", sep=""), cex=0.8)\n+ unq <- unique(filt_feat)\n+ if (length(unq) == 1) {\n+ abline(v = unq, col = "red", lw = 2)\n+ text(tmp$mids, table(filt_feat)[[1]] - 100, pos = 1,\n+ paste(10^unq, "\\nFeatures\\nin remaining\\nCells",\n+ sep = ""), cex = 0.8)\n }\n \n- if (filt.use.ccorrect){\n- par(mfrow=c(2,2))\n+ if (filt.use.ccorrect) {\n+ par(mfrow = c(2, 2))\n sc <- do.call(CCcorrect, c(sc, filt.ccc))\n- print(plotdimsat(sc, change=T))\n- print(plotdimsat(sc, change=F))\n+ print(plotdimsat(sc, '..b' test <- list()\n- test$side = 3\n- test$line = 0 #1 #3\n- test$cex = 0.8\n+ test$side <- 4\n+ test$line <- -2\n+ test$cex <- 0.8\n \n df <- c()\n options(cex = 1)\n- lapply(unique(sc@cpart), function(n){\n- dg <- clustdiffgenes(sc, cl=n, pvalue=genelist.pvalue)\n+ plot.new()\n+ lapply(unique(sc@cpart), function(n) {\n+ dg <- clustdiffgenes(sc, cl = n, pvalue = genelist.pvalue)$dg\n \n- dg.goi <- dg[dg$fc > genelist.foldchange,]\n- dg.goi.table <- head(dg.goi, genelist.tablelim)\n- df <<- rbind(df, cbind(n, dg.goi.table))\n+ dg_goi <- dg[dg$fc > genelist.foldchange, ]\n+ dg_goi_table <- head(dg_goi, genelist.tablelim)\n+ df <<- rbind(df, cbind(n, dg_goi_table))\n \n- goi <- head(rownames(dg.goi.table), genelist.plotlim)\n+ goi <- head(rownames(dg_goi_table), genelist.plotlim)\n+\n print(plotmarkergenes(sc, goi))\n- buffer <- paste(rep("", 36), collapse=" ")\n- print(do.call(mtext, c(paste(buffer, "Cluster ",n), test))) ## spacing is a hack\n- test$line=-1\n- print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test))) ## spacing is a hack\n- test$line=-2\n- print(do.call(mtext, c(paste(buffer, "(fc > ", genelist.foldchange,")"), test))) ## spacing is a hack\n-\n+ buffer <- paste(rep("", 36), collapse = " ")\n+ print(do.call(mtext, c(paste(buffer, "Cluster ", n), test)))\n+ test$line <- -1\n+ print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))\n+ test$line <- 0\n+ print(do.call(mtext, c(paste(buffer, "(fc > ",\n+ genelist.foldchange, ")"), test)))\n })\n- write.table(df, file=out.genelist, sep="\\t", quote=F)\n+ write.table(df, file = out.genelist, sep = "\\t", quote = F)\n }\n \n \n-writecellassignments <- function(sc){\n+writecellassignments <- function(sc) {\n dat <- sc@cluster$kpart\n tab <- data.frame(row.names = NULL,\n cells = names(dat),\n@@ -148,30 +137,38 @@\n cluster.final = sc@cpart,\n is.outlier = names(dat) %in% sc@out$out)\n \n- write.table(tab, file=out.assignments, sep="\\t", quote=F, row.names = F)\n+ write.table(tab, file = out.assignments, sep = "\\t",\n+ quote = F, row.names = F)\n }\n \n \n pdf(out.pdf)\n \n-if (use.filtnormconf){\n+if (use.filtnormconf) {\n sc <- do.filter(sc)\n- message(paste(" - Source:: genes:",nrow(sc@expdata),", cells:",ncol(sc@expdata)))\n- message(paste(" - Filter:: genes:",nrow(getfdata(sc)),", cells:",ncol(getfdata(sc))))\n+ message(paste(" - Source:: genes:", nrow(sc@expdata),\n+ ", cells:", ncol(sc@expdata)))\n+ message(paste(" - Filter:: genes:", nrow(as.matrix(getfdata(sc))),\n+ ", cells:", ncol(as.matrix(getfdata(sc)))))\n message(paste(" :: ",\n- sprintf("%.1f", 100 * nrow(getfdata(sc))/nrow(sc@expdata)), "% of genes remain,",\n- sprintf("%.1f", 100 * ncol(getfdata(sc))/ncol(sc@expdata)), "% of cells remain"))\n- write.table(as.matrix(sc@ndata), file=out.table, col.names=NA, row.names=T, sep="\\t", quote=F)\n+ sprintf("%.1f", 100 * nrow(as.matrix(\n+ getfdata(sc))) / nrow(sc@expdata)),\n+ "% of genes remain,",\n+ sprintf("%.1f", 100 * ncol(as.matrix(\n+ getfdata(sc))) / ncol(sc@expdata)),\n+ "% of cells remain"))\n+ write.table(as.matrix(sc@ndata), file = out.table, col.names = NA,\n+ row.names = T, sep = "\\t", quote = F)\n }\n \n-if (use.cluster){\n- par(mfrow=c(2,2))\n+if (use.cluster) {\n+ par(mfrow = c(2, 2))\n sc <- do.cluster(sc)\n \n- par(mfrow=c(2,2))\n+ par(mfrow = c(2, 2))\n sc <- do.outlier(sc)\n \n- par(mfrow=c(2,2), mar=c(1,1,6,1))\n+ par(mfrow = c(2, 2), mar = c(1, 1, 6, 1))\n sc <- do.clustmap(sc)\n \n mkgenelist(sc)\n'

diff -r 7608d5faee41 -r 43c146e25a43 scripts/clusterinspect.R
--- a/scripts/clusterinspect.R Wed Jan 29 17:16:02 2020 -0500
+++ b/scripts/clusterinspect.R Thu Apr 15 19:00:38 2021 +0000

[

@@ -1,9 +1,9 @@
#!/usr/bin/env R
-VERSION = "0.5"
+VERSION <- "0.5"  # nolint

-args = commandArgs(trailingOnly = T)
+args <- commandArgs(trailingOnly = T)

-if (length(args) != 1){
+if (length(args) != 1) {
      message(paste("VERSION:", VERSION))
      stop("Please provide the config file")
}
@@ -13,117 +13,124 @@

## layout
test <- list()
-test$side = 3
-test$line = 3
+test$side <- 3
+test$line <- 3

-do.plotting <- function(sc){
+do.plotting <- function(sc) { # nolint

-    sc.tmp <- sc
+    sc_tmp <- sc

     ## If it's a subset, we need to get clever and subset specific parts
-    if (!(is.null(plotting.cln) || is.na(plotting.cln))){
-        cellstokeep <- names(sc.tmp@cpart[sc.tmp@cpart %in% plotting.cln])
+    if (!(is.null(plotting.cln) || is.na(plotting.cln))) {
+        cellstokeep <- names(sc_tmp@cpart[sc_tmp@cpart %in% plotting.cln])

         ## Subselect partitions for initial and final clusters
-        sc.tmp@cpart <- sc.tmp@cpart[cellstokeep]
-        sc.tmp@cluster$kpart <- sc.tmp@cluster$kpart[cellstokeep]
+        sc_tmp@cpart <- sc_tmp@cpart[cellstokeep]
+        sc_tmp@cluster$kpart <- sc_tmp@cluster$kpart[cellstokeep]

         ## Subselect tSNE and FR data
-        ## - Note: no names in tsne, so we assume it follows the ndata naming
-        sc.tmp@tsne <- sc.tmp@tsne[colnames(sc.tmp@ndata) %in% cellstokeep,]
-        sc.tmp@fr <- sc.tmp@fr[cellstokeep,]
+        sc_tmp@tsne <- sc_tmp@tsne[colnames(sc_tmp@ndata) %in% cellstokeep, ]
+        sc_tmp@umap <- sc_tmp@umap[colnames(sc_tmp@ndata) %in% cellstokeep, ]
+        sc_tmp@fr <- sc_tmp@fr[cellstokeep, ]
     }

-    print(plotmap(sc.tmp, final = FALSE, fr = FALSE))
+    print(plotmap(sc_tmp, final = FALSE, fr = FALSE))
     print(do.call(mtext, c("Initial Clustering tSNE", test)))
-    print(plotmap(sc.tmp, final = TRUE, fr = FALSE))
+    print(plotmap(sc_tmp, final = TRUE, fr = FALSE))
     print(do.call(mtext, c("Final Clustering tSNE", test)))
-    print(plotmap(sc.tmp, final = FALSE, fr = TRUE))
+    print(plotmap(sc_tmp, final = FALSE, um = TRUE))
+    print(do.call(mtext, c("Initial Clustering UMAP", test)))
+    print(plotmap(sc_tmp, final = TRUE, um = TRUE))
+    print(do.call(mtext, c("Final Clustering UMAP", test)))
+    print(plotmap(sc_tmp, final = FALSE, fr = TRUE))
     print(do.call(mtext, c("Initial Clustering Fruchterman-Reingold", test)))
-    print(plotmap(sc.tmp, final = TRUE, fr = TRUE))
+    print(plotmap(sc_tmp, final = TRUE, fr = TRUE))
     print(do.call(mtext, c("Final Clustering Fruchterman-Reingold", test)))
}

-do.inspect.symbolmap <- function(sc){
-    if (!is.null(plotsym.use.typeremoveregex)){
-        plotsym$types = sub(plotsym.use.typeremoveregex, "", colnames(sc@ndata))
+do.inspect.symbolmap <- function(sc) {  # nolint
+    if (!is.null(plotsym.use.typeremoveregex)) {
+        plotsym$types <- sub(plotsym.use.typeremoveregex, "",
+                             colnames(sc@ndata))

-        if (!is.null(plotsym.use.typeremoveregex.subselect)){
-            plotsym$subset = plotsym$types[grep(plotsym.use.typeremoveregex.subselect, plotsym$types)]
+        if (!is.null(plotsym.use.typeremoveregex.subselect)) {
+            plotsym$subset <- plotsym$types[grep(
+                                          plotsym.use.typeremoveregex.subselect,
+                                          plotsym$types)]
         }
     }
-    plotsym$fr = FALSE
+    plotsym$fr <- FALSE
     print(do.call(plotsymbolsmap, c(sc, plotsym)))
     print(do.call(mtext, c("Symbols tSNE", test)))
-    plotsym$fr = TRUE
+    plotsym$fr <- TRUE
     print(do.call(plotsymbolsmap, c(sc, plotsym)))
     print(do.call(mtext, c("Symbols FR", test)))
}

-do.inspect.diffgene <- function(sc){
+do.inspect.diffgene <- function(sc) {  # nolint

-    getSubNames <- function(lob, sc){
-        use.names <- NULL
-        if (!is.null(lob$manual)){
-            use.names <- lob$manual
+    getSubNames <- function(lob, sc) {  # nolint
+        use_names <- NULL
+        if (!is.null(lob$manual)) {
+            use_names <- lob$manual
         }
-        else if (!is.null(lob$regex)){
+        else if (!is.null(lob$regex)) {
             nm <- colnames(sc@ndata)
-            use.names <- nm[grep(lob$regex, nm)]
+            use_names <- nm[grep(lob$regex, nm)]
         }
-        else if (!is.null(lob$cln)){
-            use.names <- names(sc@cpart)[sc@cpart %in% lob$cln]
+        else if (!is.null(lob$cln)) {
+            use_names <- names(sc@cpart)[sc@cpart %in% lob$cln]
         }
-        if (is.null(use.names)){
+        if (is.null(use_names)) {
             stop("A or B names not given!")
         }
-        return(use.names)
+        return(use_names)
     }

-    A <- getSubNames(gfdat.A.use, sc)
-    B <- getSubNames(gfdat.B.use, sc)
+    A <- getSubNames(gfdat.A.use, sc) # nolint
+    B <- getSubNames(gfdat.B.use, sc) # nolint

-    fdat <- getfdata(sc, n=c(A,B))
-    dexp <- diffexpnb(fdat, A=A, B=B)
+    fdat <- getfdata(sc, n = c(A, B))
+    dexp <- diffexpnb(fdat, A = A, B = B)
     ## options for diffexpnb are mostly about DESeq, ignore
-    plotdiffg$x = dexp
+    plotdiffg$x <- dexp
     print(do.call(plotdiffgenesnb, c(plotdiffg)))
     print(do.call(mtext, c("Diff Genes", test)))
}

-do.inspect.genesofinterest <- function(sc){
-    if (is.null(plotexp$n)){ ## No title, and one gene? Use gene name
-        if (length(plotexp$g) == 1){
+do.inspect.genesofinterest <- function(sc) {  # nolint
+    if (is.null(plotexp$n)) { ## No title, and one gene? Use gene name
+        if (length(plotexp$g) == 1) {
             plotexp$n <- plotexp$g
         } else {
-            plotexp$n <- paste(plotexp$g, collapse=", ")
+            plotexp$n <- paste(plotexp$g, collapse = ", ")
         }
     }

     title <- paste(":", plotexp$n)
     plotexp$n <- ""

-    plotexp$logsc=FALSE; plotexp$fr = FALSE
+    plotexp$logsc <- FALSE; plotexp$fr <- FALSE
     print(do.call(plotexpmap, c(sc, plotexp)))
     print(do.call(mtext, c(paste("tSNE", title), test)))

-    plotexp$logsc=TRUE; plotexp$fr = FALSE
+    plotexp$logsc <- TRUE; plotexp$fr <- FALSE
     print(do.call(plotexpmap, c(sc, plotexp)))
     print(do.call(mtext, c(paste("tSNE (Log)", title), test)))

-    plotexp$logsc=FALSE; plotexp$fr = TRUE
+    plotexp$logsc <- FALSE; plotexp$fr <- TRUE
     print(do.call(plotexpmap, c(sc, plotexp)))
     print(do.call(mtext, c(paste("FR", title), test)))

-    plotexp$logsc=TRUE; plotexp$fr = TRUE
+    plotexp$logsc <- TRUE; plotexp$fr <- TRUE
     print(do.call(plotexpmap, c(sc, plotexp)))
     print(do.call(mtext, c(paste("FR (Log)", title), test)))

-    if (!is.null(plotmarkg$samples)){
+    if (!is.null(plotmarkg$samples)) {
         reg <- plotmarkg$samples
-        plotmarkg$samples <- sub("(\\_\\d+)$","", colnames(sc@ndata))
+        plotmarkg$samples <- sub("(\\_\\d+)$", "", colnames(sc@ndata))
     }
     print(do.call(plotmarkergenes, c(sc, plotmarkg)))
}

diff -r 7608d5faee41 -r 43c146e25a43 scripts/pseudotemporal.R
--- a/scripts/pseudotemporal.R Wed Jan 29 17:16:02 2020 -0500
+++ b/scripts/pseudotemporal.R Thu Apr 15 19:00:38 2021 +0000

[

@@ -1,9 +1,9 @@
#!/usr/bin/env R
-VERSION = "0.1"
+VERSION <- "0.1" # nolint

-args = commandArgs(trailingOnly = T)
+args <- commandArgs(trailingOnly = T) # nolint

-if (length(args) != 1){
+if (length(args) != 1) {
      message(paste("VERSION:", VERSION))
      stop("Please provide the config file")
}
@@ -12,37 +12,44 @@
source(args[1])

test <- list()
-test$side = 3
-test$line = 3
+test$side <- 3
+test$line <- 3
second <- test
-second$cex = 0.5
-second$line = 2.5
+second$cex <- 0.5
+second$line <- 2.5

-do.pseudotemp <- function(sc){
+do.pseudotemp <- function(sc) { # nolint
     pdf(out.pdf)
     ltr <- Ltree(sc)
     ltr <- compentropy(ltr)
     ltr <- do.call(projcells, c(ltr, pstc.projc))
     ltr <- do.call(projback, c(ltr, pstc.projb))
     ltr <- lineagegraph(ltr)
-    ltr <- do.call(comppvalue, c(ltr, pstc.comppval))
+    ltr <- do.call(comppvalue, c(ltr, pstc.comppval))
     x <- do.call(compscore, c(ltr, pstc.compscore))
     print(do.call(mtext, c("Compute Score", test)))
-    print(do.call(mtext, c("No. of inter-cluster links / Delta median entropy of each cluster / StemID2 score (combination of both)", second)))
+    print(do.call(mtext, c(paste0("No. of inter-cluster links / ",
+                                  "Delta median entropy of each cluster / ",
+                                  "StemID2 score (combination of both)"),
+                           second)))
     plotdistanceratio(ltr)
     print(do.call(mtext, c("Cell-to-Cell Distance Ratio", test)))
-    print(do.call(mtext, c("Original vs High-dimensional Embedded Space", second)))
+    print(do.call(mtext, c("Original vs High-dimensional Embedded Space",
+                           second)))
     do.call(plotgraph, c(ltr, pstc.plotgraph))
-    print(do.call(mtext, c("Lineage Trajectories                                                      ", test)))
-    print(do.call(mtext, c("Colour = Level of Significance, Width = Link Score                                                                                                          ", second)))
+    print(do.call(mtext, c(paste0(c("Lineage Trajectories", rep(" ", 54)),
+                                  collapse = ""), test)))
+    print(do.call(mtext, c(paste0(c(paste0("Colour = Level of Significance, ",
+                                           "Width = Link Score"),
+                                    rep(" ", 106)), collapse = ""), second)))
     plotspantree(ltr)
     print(do.call(mtext, c("Minimum Spanning Tree", test)))
-    plotprojections(ltr)
+    plotspantree(ltr, projections = TRUE)
     print(do.call(mtext, c("Minimum Spanning Tree", test)))
     print(do.call(mtext, c("Cells Projected onto Links", second)))
-    test$side = 4
-    test$line = 0
+    test$side <- 4
+    test$line <- 0
     plotlinkscore(ltr)
     print(do.call(mtext, c("Link Score", test)))
     projenrichment(ltr)

diff -r 7608d5faee41 -r 43c146e25a43 scripts/trajectoryinspect.R
--- a/scripts/trajectoryinspect.R Wed Jan 29 17:16:02 2020 -0500
+++ b/scripts/trajectoryinspect.R Thu Apr 15 19:00:38 2021 +0000

[

@@ -1,9 +1,9 @@
#!/usr/bin/env R
-VERSION = "0.2"
+VERSION <- "0.2" # nolint

-args = commandArgs(trailingOnly = T)
+args <- commandArgs(trailingOnly = T)

-if (length(args) != 1){
+if (length(args) != 1) {
      message(paste("VERSION:", VERSION))
      stop("Please provide the config file")
}
@@ -13,17 +13,17 @@
source(args[1])

test <- list()
-test$side = 3
-test$line = 2.5
+test$side <- 3
+test$line <- 2.5
second <- test
-second$cex = 0.5
-second$line = 2.5
+second$cex <- 0.5
+second$line <- 2.5

-do.trajectoryinspection.stemID <- function(ltr){
-    makeBranchLink <- function(i,j,k){
-        ingoing <- paste(sort(c(i,j)), collapse=".")
-        outgoing <- paste(sort(c(j,k)), collapse=".")
-        messed <- sort(c(ingoing,outgoing))
+do.trajectoryinspection.stemID <- function(ltr) { # nolint
+    makeBranchLink <- function(i, j, k) { # nolint
+        ingoing <- paste(sort(c(i, j)), collapse = ".")
+        outgoing <- paste(sort(c(j, k)), collapse = ".")
+        messed <- sort(c(ingoing, outgoing))
         return(list(messed[[1]], messed[[2]]))
     }

@@ -34,88 +34,99 @@
     )
     write.table(
         head(bra$diffgenes$z, trjsid.numdiffgenes),
-        file=out.diffgenes)
+        file = out.diffgenes)

-    par(mfrow = c(2,2), cex=0.5)
-    print(do.call(plotmap, c(bra$scl, final=FALSE, fr=FALSE)))
+    par(mfrow = c(3, 2), cex = 0.5)
+    print(do.call(plotmap, c(bra$scl, final = FALSE, fr = FALSE)))
     print(do.call(mtext, c("Initial Clusters (tSNE)", test)))
-    print(do.call(plotmap, c(bra$scl, final=TRUE, fr=FALSE)))
+    print(do.call(plotmap, c(bra$scl, final = TRUE, fr = FALSE)))
     print(do.call(mtext, c("Final Clusters (tSNE)", test)))
-    print(do.call(plotmap, c(bra$scl, final=FALSE, fr=TRUE)))
+    print(do.call(plotmap, c(bra$scl, final = FALSE, um = TRUE)))
+    print(do.call(mtext, c("Initial Clusters (UMAP)", test)))
+    print(do.call(plotmap, c(bra$scl, final = TRUE, um = TRUE)))
+    print(do.call(mtext, c("Final Clusters (UMAP)", test)))
+    print(do.call(plotmap, c(bra$scl, final = FALSE, fr = TRUE)))
     print(do.call(mtext, c("Initial Clusters (F-R)", test)))
-    print(do.call(plotmap, c(bra$scl, final=TRUE, fr=TRUE)))
+    print(do.call(plotmap, c(bra$scl, final = TRUE, fr = TRUE)))
     print(do.call(mtext, c("Final Clusters (F-R)", test)))
}

-do.trajectoryinspection.fateID <- function(ltr){
+do.trajectoryinspection.fateID <- function(ltr) { # nolint
     n <- do.call(cellsfromtree, c(ltr, trjfid.cellsfrom))
     x <- getfdata(ltr@sc)

-    trjfid.filterset$x = x
-    trjfid.filterset$n = n$f
+    trjfid.filterset$x <- x
+    trjfid.filterset$n <- n$f
     fs <- do.call(filterset, c(trjfid.filterset))
-    trjfid.getsom$x = fs
+    trjfid.getsom$x <- fs
     s1d <- do.call(getsom, c(trjfid.getsom))
-    trjfid.procsom$s1d = s1d
+    trjfid.procsom$s1d <- s1d
     ps <- do.call(procsom, c(trjfid.procsom))

     y    <- ltr@sc@cpart[n$f]
     fcol <- ltr@sc@fcol

-    trjfid.plotheat$xpart = y
-    trjfid.plotheat$xcol = fcol
+    trjfid.plotheat$xpart <- y
+    trjfid.plotheat$xcol <- fcol
+
+    test$side <- 3
+    test$line <- 3

     ##Plot average z-score for all modules derived from the SOM:
-    trjfid.plotheat$x = ps$nodes.z
-    trjfid.plotheat$ypart = unique(ps$nodes)
+    trjfid.plotheat$x <- ps$nodes.z
+    trjfid.plotheat$ypart <- unique(ps$nodes)
     print(do.call(plotheatmap, c(trjfid.plotheat)))
-    print(do.call(mtext, c("Average z-score for all modules derived from SOM", test)))
+    print(do.call(mtext, c("Average z-score for all modules derived from SOM",
+                           test)))
     ##Plot z-score profile of each gene ordered by SOM modules:
-    trjfid.plotheat$x = ps$all.z
-    trjfid.plotheat$ypart = ps$nodes
+    trjfid.plotheat$x <- ps$all.z
+    trjfid.plotheat$ypart <- ps$nodes
     print(do.call(plotheatmap, c(trjfid.plotheat)))
-    print(do.call(mtext, c("z-score profile of each gene ordered by SOM modules", test)))
+    print(do.call(mtext, c(paste0("z-score profile of each gene",
+                                  "ordered by SOM modules"), test)))
     ##Plot normalized expression profile of each gene ordered by SOM modules:
-    trjfid.plotheat$x = ps$all.e
-    trjfid.plotheat$ypart = ps$nodes
+    trjfid.plotheat$x <- ps$all.e
+    trjfid.plotheat$ypart <- ps$nodes
     print(do.call(plotheatmap, c(trjfid.plotheat)))
-    print(do.call(mtext, c("Normalized expression profile of each gene ordered by SOM modules", test)))
-    ##Plot binarized expression profile of each gene (z-score < -1, -1 < z-score < 1, z-score > 1):
-    trjfid.plotheat$x = ps$all.b
-    trjfid.plotheat$ypart = ps$nodes
+    print(do.call(mtext, c(paste0("Normalized expression profile of each",
+                                  "gene ordered by SOM modules"), test)))
+    ##Plot binarized expression profile of each gene
+    ##(z-score < -1, -1 < z-score < 1, z-score > 1)
+    trjfid.plotheat$x <- ps$all.b
+    trjfid.plotheat$ypart <- ps$nodes
     print(do.call(plotheatmap, c(trjfid.plotheat)))
     print(do.call(mtext, c("Binarized expression profile of each gene", test)))
     ## This should be written out, and passed back into the tool
     ## to perform sominspect
-    return(list(fs=fs,ps=ps,y=y,fcol=fcol,nf=n$f))
+    return(list(fs = fs, ps = ps, y = y, fcol = fcol, nf = n$f))
}

-do.trajectoryinspection.fateID.sominspect <- function(domo){
+do.trajectoryinspection.fateID.sominspect <- function(domo) { # nolint
     g <- trjfidsomi.use.genes
-    if (class(g) == "numeric"){
+    if (class(g) == "numeric") {
         g <- names(ps$nodes)[ps$nodes %in% g]
     }

-    typ = NULL
-    if (!is.null(trjfidsomi.use.types)){
-        typ = sub(trjfidsomi.use.types,"", domo$nf)
+    typ <- NULL
+    if (!is.null(trjfidsomi.use.types)) {
+        typ <- sub(trjfidsomi.use.types, "", domo$nf)
     }

-    trjfidsomi$x = domo$fs
-    trjfidsomi$y = domo$y
-    trjfidsomi$g = g
-    trjfidsomi$n = domo$nf
-    trjfidsomi$col = domo$fcol
-    trjfidsomi$types = typ
+    trjfidsomi$x <- domo$fs
+    trjfidsomi$y <- domo$y
+    trjfidsomi$g <- g
+    trjfidsomi$n <- domo$nf
+    trjfidsomi$col <- domo$fcol
+    trjfidsomi$types <- typ

     ## The average pseudo-temporal expression profile of this group
     ## can be plotted by the function plotexpression:
-    par(mfrow = c(1,1))
-    test$cex = 1
-    second$line = 1.5
-    if (trjfidsomi$name == "Title") trjfidsomi$name = ""
+    par(mfrow = c(1, 1))
+    test$cex <- 1
+    second$line <- 1.5
+    if (trjfidsomi$name == "Title") trjfidsomi$name <- ""
     print(do.call(plotexpression, c(trjfidsomi)))
-    mess2 <- paste(c(trjfidsomi.use.genes), collapse=", ")
+    mess2 <- paste(c(trjfidsomi.use.genes), collapse = ", ")
     mess1 <- "Average pseudo-temporal expression profile"
     print(do.call(mtext, c(mess1, test)))
     print(do.call(mtext, c(mess2, second)))

diff -r 7608d5faee41 -r 43c146e25a43 test-data/intestinal.genelist
--- a/test-data/intestinal.genelist Wed Jan 29 17:16:02 2020 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000

[

b'@@ -1,201 +0,0 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diff -r 7608d5faee41 -r 43c146e25a43 test-data/intestinal.pdf

Binary file test-data/intestinal.pdf has changed

diff -r 7608d5faee41 -r 43c146e25a43 test-data/intestinal_advanced.filter.pdf

Binary file test-data/intestinal_advanced.filter.pdf has changed

diff -r 7608d5faee41 -r 43c146e25a43 test-data/intestinal_advanced.genelist
--- a/test-data/intestinal_advanced.genelist Wed Jan 29 17:16:02 2020 -0500
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000

[

b'@@ -1,251 +0,0 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[

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