Mercurial > repos > iuc > dada2_dada

changeset 3:f79565b12646 draft

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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/dada2 commit 8533fe71d1d50f09348da2dc34941724407a1ffe"
author iuc
date Tue, 14 Jul 2020 07:40:03 -0400
parents 2222f08c8316
children 18a502a9d287
files macros.xml static/images/pairpipe.png static/images/pairpipe.svg test-data/gentest.R test-data/learnErrors.pdf test-data/learnErrors_R1.pdf
diffstat 6 files changed, 206 insertions(+), 193 deletions(-) [+]
line wrap: on
line diff
--- a/macros.xml	Mon Mar 16 07:34:56 2020 -0400
+++ b/macros.xml	Tue Jul 14 07:40:03 2020 -0400
@@ -7,7 +7,7 @@
         </requirements>
     </xml>
 
-    <token name="@DADA2_VERSION@">1.14</token>
+    <token name="@DADA2_VERSION@">1.16</token>
     <token name="@WRAPPER_VERSION@">0</token>
 
     <xml name="version_command">
@@ -124,6 +124,9 @@
 
 .. image:: pairpipe.png
 
+Note: In particular for the analysis of paired collections the collections should be sorted lexicographical
+before the analysis.
+
 For single end data you the steps "Unzip collection" and "mergePairs" are not necessary.
 
 More information may be found on the dada2 homepage:: https://benjjneb.github.io/dada2/index.html (in particular tutorials) or the documentation of dada2's R package https://bioconductor.org/packages/release/bioc/html/dada2.html (in particular the pdf which contains the full documentation of all parameters)
Binary file static/images/pairpipe.png has changed
--- a/static/images/pairpipe.svg	Mon Mar 16 07:34:56 2020 -0400
+++ b/static/images/pairpipe.svg	Tue Jul 14 07:40:03 2020 -0400
@@ -24,7 +24,7 @@
         <dc:format>image/svg+xml</dc:format>
         <dc:type
            rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
-        <dc:title></dc:title>
+        <dc:title />
       </cc:Work>
     </rdf:RDF>
   </metadata>
@@ -37,16 +37,16 @@
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--- a/test-data/gentest.R	Mon Mar 16 07:34:56 2020 -0400
+++ b/test-data/gentest.R	Tue Jul 14 07:40:03 2020 -0400
@@ -1,192 +1,186 @@
-library(dada2, quietly=T)
-library(ggplot2, quietly=T)
+library(dada2, quietly = T)
+library(ggplot2, quietly = T)
 
-sample.names <- c('F3D0_S188_L001', 'F3D141_S207_L001')
-fwd <- c('F3D0_S188_L001_R1_001.fastq.gz', 'F3D141_S207_L001_R1_001.fastq.gz')
-rev <- c('F3D0_S188_L001_R2_001.fastq.gz', 'F3D141_S207_L001_R2_001.fastq.gz')
+sample_names <- c("F3D0_S188_L001", "F3D141_S207_L001")
+fwd <- c("F3D0_S188_L001_R1_001.fastq.gz", "F3D141_S207_L001_R1_001.fastq.gz")
+rev <- c("F3D0_S188_L001_R2_001.fastq.gz", "F3D141_S207_L001_R2_001.fastq.gz")
 
-filt.fwd <- c('filterAndTrim_F3D0_R1.fq.gz', 'filterAndTrim_F3D141_R1.fq.gz')
-filt.rev <- c('filterAndTrim_F3D0_R2.fq.gz', 'filterAndTrim_F3D141_R2.fq.gz')
+filt_fwd <- c("filterAndTrim_F3D0_R1.fq.gz", "filterAndTrim_F3D141_R1.fq.gz")
+filt_rev <- c("filterAndTrim_F3D0_R2.fq.gz", "filterAndTrim_F3D141_R2.fq.gz")
 
 print("filterAndTrim")
 
-for(i in 1:length(fwd)){
-	ftout <- filterAndTrim(fwd[i], filt.fwd[i], rev[i], filt.rev[i])
-    b <- paste(strsplit(fwd[i], ".", fixed=T)[[1]][1], "tab", sep=".")
-    write.table(ftout, b, quote=F, sep="\t", col.names=NA)
+for (i in seq_len(fwd)) {
+    ftout <- dada2::filterAndTrim(fwd[i], filt_fwd[i], rev[i], filt_rev[i])
+    b <- paste(strsplit(fwd[i], ".", fixed = T)[[1]][1], "tab", sep = ".")
+    write.table(ftout, b, quote = F, sep = "\t", col.names = NA)
 }
 
 # In the test only the 1st data set is used
 t <- data.frame()
-t <- rbind(t, ftout[1,])
+t <- rbind(t, ftout[1, ])
 colnames(t) <- colnames(ftout)
 rownames(t) <- rownames(ftout)[1]
-write.table(t, "filterAndTrim.tab", quote=F, sep="\t", col.names=NA)
+write.table(t, "filterAndTrim.tab", quote = F, sep = "\t", col.names = NA)
 
-names(fwd) <- sample.names
-names(rev) <- sample.names
-names(filt.fwd) <- sample.names
-names(filt.rev) <- sample.names
+names(fwd) <- sample_names
+names(rev) <- sample_names
+names(filt_fwd) <- sample_names
+names(filt_rev) <- sample_names
 
 # Plot quality profile (just for one file, Galaxy compares with sim_size)
 print("plots")
-qp <- plotQualityProfile(fwd)
-ggsave('qualityProfile_fwd.pdf', qp, width = 20,height = 15,units = c("cm"))
-qp <- plotQualityProfile(rev)
-ggsave('qualityProfile_rev.pdf', qp, width = 20,height = 15,units = c("cm"))
-qp <- plotQualityProfile(fwd[1])
-ggsave('qualityProfile.pdf', qp, width = 20,height = 15,units = c("cm"))
+qp <- dada2::plotQualityProfile(fwd)
+ggsave("qualityProfile_fwd.pdf", qp, width = 20, height = 15, units = c("cm"))
+qp <- dada2::plotQualityProfile(rev)
+ggsave("qualityProfile_rev.pdf", qp, width = 20, height = 15, units = c("cm"))
+qp <- dada2::plotQualityProfile(fwd[1])
+ggsave("qualityProfile.pdf", qp, width = 20, height = 15, units = c("cm"))
 
 # Plot complexity (just for one file, Galaxy compares with sim_size)
 
-cp <- plotComplexity(fwd)
-ggsave('complexity_fwd.pdf', cp, width = 20,height = 15,units = c("cm"))
-cp <- plotComplexity(rev)
-ggsave('complexity_rev.pdf', cp, width = 20,height = 15,units = c("cm"))
-cp <- plotComplexity(fwd[1])
-ggsave('complexity.pdf', cp, width = 20,height = 15,units = c("cm"))
+cp <- dada2::plotComplexity(fwd)
+ggsave("complexity_fwd.pdf", cp, width = 20, height = 15, units = c("cm"))
+cp <- dada2::plotComplexity(rev)
+ggsave("complexity_rev.pdf", cp, width = 20, height = 15, units = c("cm"))
+cp <- dada2::plotComplexity(fwd[1])
+ggsave("complexity.pdf", cp, width = 20, height = 15, units = c("cm"))
 
 
 # learn Errors
 print("learnErrors")
-err.fwd <- learnErrors(filt.fwd) 
-saveRDS(err.fwd, file='learnErrors_R1.Rdata')
-plot <- plotErrors(err.fwd)
-ggsave('learnErrors_R1.pdf', plot, width = 20,height = 15,units = c("cm"))
+err_fwd <- dada2::learnErrors(filt_fwd)
+saveRDS(err_fwd, file = "learnErrors_R1.Rdata")
+plot <- dada2::plotErrors(err_fwd)
+ggsave("learnErrors_R1.pdf", plot, width = 20, height = 15, units = c("cm"))
 
-err.rev <- learnErrors(filt.rev) 
-saveRDS(err.rev, file='learnErrors_R2.Rdata')
-plot <- plotErrors(err.rev)
-ggsave('learnErrors.pdf', plot, width = 20,height = 15,units = c("cm"))
+err_rev <- dada2::learnErrors(filt_rev)
+saveRDS(err_rev, file = "learnErrors_R2.Rdata")
+plot <- dada2::plotErrors(err_rev)
+ggsave("learnErrors.pdf", plot, width = 20, height = 15, units = c("cm"))
 
 # dada
 print("dada")
-dada.fwd <- dada(filt.fwd, err.fwd)
-dada.rev <- dada(filt.rev, err.rev)
-for( id in sample.names ){
-	saveRDS(dada.fwd[[id]], file=paste("dada_", id,"_R1.Rdata", sep=""))
-	saveRDS(dada.rev[[id]], file=paste("dada_", id,"_R2.Rdata", sep=""))
+dada_fwd <- dada2::dada(filt_fwd, err_fwd)
+dada_rev <- dada2::dada(filt_rev, err_rev)
+for (id in sample_names) {
+    saveRDS(dada_fwd[[id]], file = paste("dada_", id, "_R1.Rdata", sep = ""))
+    saveRDS(dada_rev[[id]], file = paste("dada_", id, "_R2.Rdata", sep = ""))
 }
 
 # merge pairs
 print("mergePairs")
-merged <- mergePairs(dada.fwd, filt.fwd, dada.rev, filt.rev)
-for( id in sample.names ){
-	saveRDS(merged[[id]], file=paste("mergePairs_", id,".Rdata", sep=""))
+merged <- dada2::mergePairs(dada_fwd, filt_fwd, dada_rev, filt_rev)
+for (id in sample_names) {
+    saveRDS(merged[[id]], file = paste("mergePairs_", id, ".Rdata", sep = ""))
 }
 
 
 # make sequence table
 print("makeSequenceTable")
 seqtab <- makeSequenceTable(merged)
-write.table(t(seqtab), file="makeSequenceTable.tab", quote=F, sep="\t", row.names = T, col.names = NA)
+write.table(t(seqtab), file = "makeSequenceTable.tab", quote = F, sep = "\t", row.names = T, col.names = NA)
 
-reads.per.seqlen <- tapply(colSums(seqtab), factor(nchar(getSequences(seqtab))), sum)
-df <- data.frame(length=as.numeric(names(reads.per.seqlen)), count=reads.per.seqlen)
-pdf( 'makeSequenceTable.pdf' )
-ggplot(data=df, aes(x=length, y=count)) +
+reads_per_seqlen <- tapply(colSums(seqtab), factor(nchar(getSequences(seqtab))), sum)
+df <- data.frame(length = as.numeric(names(reads_per_seqlen)), count = reads_per_seqlen)
+pdf("makeSequenceTable.pdf")
+ggplot(data = df, aes(x = length, y = count)) +
     geom_col() +
     theme_bw()
 bequiet <- dev.off()
 
 # remove bimera
 print("removeBimera")
-seqtab.nochim <- removeBimeraDenovo(seqtab)
-write.table(t(seqtab), file="removeBimeraDenovo.tab", quote=F, sep="\t", row.names = T, col.names = NA)
+seqtab_nochim <- dada2::removeBimeraDenovo(seqtab)
+write.table(t(seqtab), file = "removeBimeraDenovo.tab", quote = F, sep = "\t", row.names = T, col.names = NA)
 
 # assign taxonomy/species
-tl <- 'Level1,Level2,Level3,Level4,Level5'
+tl <- "Level1,Level2,Level3,Level4,Level5"
 tl <- strsplit(tl, ",")[[1]]
 
 set.seed(42)
 print("assignTaxonomyAndSpecies")
-taxa <- assignTaxonomy(seqtab.nochim, 'reference.fa.gz', outputBootstraps = T, taxLevels=tl, multithread = 1)
+taxa <- dada2::assignTaxonomy(seqtab_nochim, "reference.fa.gz", outputBootstraps = T, taxLevels = tl, multithread = 1)
 
-taxa$tax <- addSpecies(taxa$tax, 'reference_species.fa.gz')
-write.table(taxa$tax, file = 'assignTaxonomyAddspecies.tab', quote = F, sep = "\t", row.names = T, col.names = NA)
+taxa$tax <- dada2::addSpecies(taxa$tax, "reference_species.fa.gz")
+write.table(taxa$tax, file = "assignTaxonomyAddspecies.tab", quote = F, sep = "\t", row.names = T, col.names = NA)
 
-write.table(taxa$boot, file = 'assignTaxonomyAddspecies_boot.tab', quote = F, sep = "\t", row.names = T, col.names = NA)
+write.table(taxa$boot, file = "assignTaxonomyAddspecies_boot.tab", quote = F, sep = "\t", row.names = T, col.names = NA)
 
 
-## Generate extra test data for parameter testing 
+## Generate extra test data for parameter testing
 print("alternatives")
-filterAndTrim(fwd, c('filterAndTrim_single_F3D0_R1.fq.gz', 'filterAndTrim_single_F3D141_R1.fq.gz'), rm.phix = T, orient.fwd = 'TACGG')
+dada2::filterAndTrim(fwd, c("filterAndTrim_single_F3D0_R1.fq.gz", "filterAndTrim_single_F3D141_R1.fq.gz"), rm.phix = T, orient.fwd = "TACGG")
 
-filterAndTrim(fwd, c('filterAndTrim_single_trimmers_F3D0_R1.fq.gz', 'filterAndTrim_single_trimmers_F3D141_R1.fq.gz'), truncQ = 30, truncLen = 2, trimLeft = 150, trimRight = 2)
+dada2::filterAndTrim(fwd, c("filterAndTrim_single_trimmers_F3D0_R1.fq.gz", "filterAndTrim_single_trimmers_F3D141_R1.fq.gz"), truncQ = 30, truncLen = 2, trimLeft = 150, trimRight = 2)
 
-filterAndTrim(fwd, c('filterAndTrim_single_filters_F3D0_R1.fq.gz', 'filterAndTrim_single_filters_F3D141_R1.fq.gz'), maxLen = 255, minLen = 60, maxN = 100, minQ = 13, maxEE = 1)
+dada2::filterAndTrim(fwd, c("filterAndTrim_single_filters_F3D0_R1.fq.gz", "filterAndTrim_single_filters_F3D141_R1.fq.gz"), maxLen = 255, minLen = 60, maxN = 100, minQ = 13, maxEE = 1)
 
 
-merged_nondef <- mergePairs(dada.fwd, filt.fwd, dada.rev, filt.rev, minOverlap = 8, maxMismatch = 1, justConcatenate = TRUE, trimOverhang = TRUE)
-for( id in sample.names ){
-	saveRDS(merged_nondef[[id]], file=paste("mergePairs_", id,"_nondefault.Rdata", sep=""))
+merged_nondef <- dada2::mergePairs(dada_fwd, filt_fwd, dada_rev, filt_rev, minOverlap = 8, maxMismatch = 1, justConcatenate = TRUE, trimOverhang = TRUE)
+for (id in sample_names) {
+    saveRDS(merged_nondef[[id]], file = paste("mergePairs_", id, "_nondefault.Rdata", sep = ""))
 }
-rb.dada.fwd <- removeBimeraDenovo(dada.fwd[["F3D0_S188_L001"]])
-write.table(rb.dada.fwd, file = 'removeBimeraDenovo_F3D0_dada_uniques.tab', quote = F, sep = "\t", row.names = T, col.names = F)
+rb_dada_fwd <- dada2::removeBimeraDenovo(dada_fwd[["F3D0_S188_L001"]])
+write.table(rb_dada_fwd, file = "removeBimeraDenovo_F3D0_dada_uniques.tab", quote = F, sep = "\t", row.names = T, col.names = F)
 
-rb.merged <- removeBimeraDenovo(merged, method="pooled")
-saveRDS(rb.merged, file='removeBimeraDenovo_F3D0_mergepairs.Rdata')
- 
+rb_merged <- dada2::removeBimeraDenovo(merged, method = "pooled")
+saveRDS(rb_merged, file = "removeBimeraDenovo_F3D0_mergepairs.Rdata")
+
 # SeqCounts
-getN <- function(x){ sum(getUniques(x)) }
-
-read.uniques <- function ( fname ) {
-    p <- read.table(fname, header=F, sep="\t")
-    n <-x[,2]
-    names(n)<-x[,1]
+get_n <- function(x) {
+    sum(dada2::getUniques(x))
 }
 
-
 print("seqCounts ft")
-samples = list()
-samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header=T, sep="\t", row.names=1)
+samples <- list()
+samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header = T, sep = "\t", row.names = 1)
 dname <- "filter"
 tdf <- samples[["F3D0_S188_L001_R1_001.tab"]]
-names(tdf) <- paste( dname, names(tdf) )
-tdf <- cbind( data.frame(samples=names( samples )), tdf)
-write.table(tdf, "seqCounts_filter.tab", quote=F, sep="\t", row.names = F, col.names = T)
+names(tdf) <- paste(dname, names(tdf))
+tdf <- cbind(data.frame(samples = names(samples)), tdf)
+write.table(tdf, "seqCounts_filter.tab", quote = F, sep = "\t", row.names = F, col.names = T)
 
-samples = list()
-samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header=T, sep="\t", row.names=1)
-samples[["F3D141_S207_L001_R1_001.tab"]] <- read.table("F3D141_S207_L001_R1_001.tab", header=T, sep="\t", row.names=1)
+samples <- list()
+samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header = T, sep = "\t", row.names = 1)
+samples[["F3D141_S207_L001_R1_001.tab"]] <- read.table("F3D141_S207_L001_R1_001.tab", header = T, sep = "\t", row.names = 1)
 dname <- "filter"
 tdf <- samples[["F3D0_S188_L001_R1_001.tab"]]
 tdf <- rbind(tdf, samples[["F3D141_S207_L001_R1_001.tab"]])
-names(tdf) <- paste( dname, names(tdf) )
-tdf <- cbind( data.frame(samples=names( samples )), tdf)
-write.table(tdf, "seqCounts_filter_both.tab", quote=F, sep="\t", row.names = F, col.names = T)
+names(tdf) <- paste(dname, names(tdf))
+tdf <- cbind(data.frame(samples = names(samples)), tdf)
+write.table(tdf, "seqCounts_filter_both.tab", quote = F, sep = "\t", row.names = F, col.names = T)
 
 print("seqCounts dada")
-samples = list()
-samples[["dada_F3D0_S188_L001_R1.Rdata"]] <- readRDS('dada_F3D0_S188_L001_R1.Rdata')
-samples[["dada_F3D141_S207_L001_R1.Rdata"]] <- readRDS('dada_F3D141_S207_L001_R1.Rdata')
+samples <- list()
+samples[["dada_F3D0_S188_L001_R1.Rdata"]] <- readRDS("dada_F3D0_S188_L001_R1.Rdata")
+samples[["dada_F3D141_S207_L001_R1.Rdata"]] <- readRDS("dada_F3D141_S207_L001_R1.Rdata")
 dname <- "dadaF"
-tdf <- data.frame( samples = names(samples) )
-tdf[[ dname ]] <- sapply(samples, getN)
-write.table(tdf, "seqCounts_dadaF.tab", quote=F, sep="\t", row.names = F, col.names = T)
+tdf <- data.frame(samples = names(samples))
+tdf[[dname]] <- sapply(samples, get_n)
+write.table(tdf, "seqCounts_dadaF.tab", quote = F, sep = "\t", row.names = F, col.names = T)
 
 print("seqCounts mp")
-samples = list()
-samples[["mergePairs_F3D0_S188_L001.Rdata"]] <- readRDS('mergePairs_F3D0_S188_L001.Rdata')
-samples[["mergePairs_F3D141_S207_L001.Rdata"]] <- readRDS('mergePairs_F3D141_S207_L001.Rdata')
+samples <- list()
+samples[["mergePairs_F3D0_S188_L001.Rdata"]] <- readRDS("mergePairs_F3D0_S188_L001.Rdata")
+samples[["mergePairs_F3D141_S207_L001.Rdata"]] <- readRDS("mergePairs_F3D141_S207_L001.Rdata")
 dname <- "merge"
-tdf <- data.frame( samples = names(samples) )
-tdf[[ dname ]] <- sapply(samples, getN)
-write.table(tdf, "seqCounts_merge.tab", quote=F, sep="\t", row.names = F, col.names = T)
+tdf <- data.frame(samples = names(samples))
+tdf[[dname]] <- sapply(samples, get_n)
+write.table(tdf, "seqCounts_merge.tab", quote = F, sep = "\t", row.names = F, col.names = T)
 
 print("seqCounts st")
-samples = list()
-samples <- t(as.matrix( read.table("makeSequenceTable.tab", header=T, sep="\t", row.names=1) ))
+samples <- list()
+samples <- t(as.matrix(read.table("makeSequenceTable.tab", header = T, sep = "\t", row.names = 1)))
 dname <- "seqtab"
-tdf <- data.frame( samples = row.names(samples) )
-tdf[[ dname ]] <- rowSums(samples)
-write.table(tdf, "seqCounts_seqtab.tab", quote=F, sep="\t", row.names = F, col.names = T)
+tdf <- data.frame(samples = row.names(samples))
+tdf[[dname]] <- rowSums(samples)
+write.table(tdf, "seqCounts_seqtab.tab", quote = F, sep = "\t", row.names = F, col.names = T)
 
 print("seqCounts rb")
-samples = list()
-samples <- t(as.matrix( read.table("removeBimeraDenovo.tab", header=T, sep="\t", row.names=1) ))
+samples <- list()
+samples <- t(as.matrix(read.table("removeBimeraDenovo.tab", header = T, sep = "\t", row.names = 1)))
 dname <- "nochim"
-tdf <- data.frame( samples = row.names(samples) )
-tdf[[ dname ]] <- rowSums(samples)
-write.table(tdf, "seqCounts_nochim.tab", quote=F, sep="\t", row.names = F, col.names = T)
-
+tdf <- data.frame(samples = row.names(samples))
+tdf[[dname]] <- rowSums(samples)
+write.table(tdf, "seqCounts_nochim.tab", quote = F, sep = "\t", row.names = F, col.names = T)
Binary file test-data/learnErrors.pdf has changed
Binary file test-data/learnErrors_R1.pdf has changed