Mercurial > repos > iuc > dada2_learnerrors

annotate test-data/gentest.R @ 8:d751c4726e0f draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/dada2 commit 6248c33a4eacc8785cac321cd4603acf06fae0fd
author iuc
date Thu, 23 May 2024 08:52:42 +0000
parents 75403243703a
children
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1 library(dada2, quietly = TRUE)
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2 library(ggplot2, quietly = TRUE)
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4 sample_names <- c("F3D0_S188_L001", "F3D141_S207_L001")
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5 fwd <- c("F3D0_S188_L001_R1_001.fastq.gz", "F3D141_S207_L001_R1_001.fastq.gz")
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6 rev <- c("F3D0_S188_L001_R2_001.fastq.gz", "F3D141_S207_L001_R2_001.fastq.gz")
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8 filt_fwd <- c("filterAndTrim_F3D0_R1.fq.gz", "filterAndTrim_F3D141_R1.fq.gz")
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9 filt_rev <- c("filterAndTrim_F3D0_R2.fq.gz", "filterAndTrim_F3D141_R2.fq.gz")
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11 print("filterAndTrim")
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13 for (i in seq_len(fwd)) {
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14 ftout <- dada2::filterAndTrim(fwd[i], filt_fwd[i], rev[i], filt_rev[i])
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15 b <- paste(strsplit(fwd[i], ".", fixed = TRUE)[[1]][1], "tab", sep = ".")
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16 write.table(ftout, b, quote = FALSE, sep = "\t", col.names = NA)
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17 }
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19 # In the test only the 1st data set is used
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20 t <- data.frame()
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21 t <- rbind(t, ftout[1, ])
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22 colnames(t) <- colnames(ftout)
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23 rownames(t) <- rownames(ftout)[1]
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24 write.table(t, "filterAndTrim.tab", quote = FALSE, sep = "\t", col.names = NA)
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26 names(fwd) <- sample_names
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27 names(rev) <- sample_names
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28 names(filt_fwd) <- sample_names
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29 names(filt_rev) <- sample_names
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31 # Plot quality profile (just for one file, Galaxy compares with sim_size)
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32 print("plots")
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33 qp <- dada2::plotQualityProfile(fwd)
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34 ggsave("qualityProfile_fwd.pdf", qp, width = 20, height = 15, units = c("cm"))
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35 qp <- dada2::plotQualityProfile(rev)
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36 ggsave("qualityProfile_rev.pdf", qp, width = 20, height = 15, units = c("cm"))
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37 qp <- dada2::plotQualityProfile(fwd[1])
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38 ggsave("qualityProfile.pdf", qp, width = 20, height = 15, units = c("cm"))
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40 # Plot complexity (just for one file, Galaxy compares with sim_size)
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42 cp <- dada2::plotComplexity(fwd)
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43 ggsave("complexity_fwd.pdf", cp, width = 20, height = 15, units = c("cm"))
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44 cp <- dada2::plotComplexity(rev)
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45 ggsave("complexity_rev.pdf", cp, width = 20, height = 15, units = c("cm"))
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46 cp <- dada2::plotComplexity(fwd[1])
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47 ggsave("complexity.pdf", cp, width = 20, height = 15, units = c("cm"))
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50 # learn Errors
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51 print("learnErrors")
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52 err_fwd <- dada2::learnErrors(filt_fwd)
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53 saveRDS(err_fwd, file = "learnErrors_R1.Rdata")
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54 plot <- dada2::plotErrors(err_fwd)
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55 ggsave("learnErrors_R1.pdf", plot, width = 20, height = 15, units = c("cm"))
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57 err_rev <- dada2::learnErrors(filt_rev)
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58 saveRDS(err_rev, file = "learnErrors_R2.Rdata")
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59 plot <- dada2::plotErrors(err_rev)
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60 ggsave("learnErrors.pdf", plot, width = 20, height = 15, units = c("cm"))
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61
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62 # dada
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63 print("dada")
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64 dada_fwd <- dada2::dada(filt_fwd, err_fwd)
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65 dada_rev <- dada2::dada(filt_rev, err_rev)
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66 for (id in sample_names) {
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67 saveRDS(dada_fwd[[id]], file = paste("dada_", id, "_R1.Rdata", sep = ""))
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68 saveRDS(dada_rev[[id]], file = paste("dada_", id, "_R2.Rdata", sep = ""))
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69 }
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71 # merge pairs
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72 print("mergePairs")
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73 merged <- dada2::mergePairs(dada_fwd, filt_fwd, dada_rev, filt_rev)
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74 for (id in sample_names) {
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75 saveRDS(merged[[id]], file = paste("mergePairs_", id, ".Rdata", sep = ""))
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76 }
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77
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79 # make sequence table
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80 print("makeSequenceTable")
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81 seqtab <- makeSequenceTable(merged)
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82 write.table(t(seqtab), file = "makeSequenceTable.tab", quote = FALSE, sep = "\t", row.names = TRUE, col.names = NA)
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83
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84 reads_per_seqlen <- tapply(colSums(seqtab), factor(nchar(getSequences(seqtab))), sum)
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85 df <- data.frame(length = as.numeric(names(reads_per_seqlen)), count = reads_per_seqlen)
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86 pdf("makeSequenceTable.pdf")
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87 ggplot(data = df, aes(x = length, y = count)) +
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88 geom_col() +
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89 theme_bw()
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90 bequiet <- dev.off()
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91
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92 # remove bimera
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93 print("removeBimera")
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94 seqtab_nochim <- dada2::removeBimeraDenovo(seqtab)
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95 write.table(t(seqtab), file = "removeBimeraDenovo.tab", quote = FALSE, sep = "\t", row.names = TRUE, col.names = NA)
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96
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97 # assign taxonomy/species
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98 tl <- "Level1,Level2,Level3,Level4,Level5"
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99 tl <- strsplit(tl, ",")[[1]]
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100
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101 set.seed(42)
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102 print("assignTaxonomyAndSpecies")
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103 taxa <- dada2::assignTaxonomy(seqtab_nochim, "reference.fa.gz", outputBootstraps = TRUE, taxLevels = tl, multithread = 1)
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104
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105 taxa$tax <- dada2::addSpecies(taxa$tax, "reference_species.fa.gz")
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106 write.table(taxa$tax, file = "assignTaxonomyAddspecies.tab", quote = FALSE, sep = "\t", row.names = TRUE, col.names = NA)
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107
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108 write.table(taxa$boot, file = "assignTaxonomyAddspecies_boot.tab", quote = FALSE, sep = "\t", row.names = TRUE, col.names = NA)
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109
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110
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111 ## Generate extra test data for parameter testing
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112 print("alternatives")
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113 dada2::filterAndTrim(fwd, c("filterAndTrim_single_F3D0_R1.fq.gz", "filterAndTrim_single_F3D141_R1.fq.gz"), rm.phix = TRUE, orient.fwd = "TACGG")
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114
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115 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)
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116
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117 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)
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118
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119
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120 merged_nondef <- dada2::mergePairs(dada_fwd, filt_fwd, dada_rev, filt_rev, minOverlap = 8, maxMismatch = 1, justConcatenate = TRUE, trimOverhang = TRUE)
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121 for (id in sample_names) {
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122 saveRDS(merged_nondef[[id]], file = paste("mergePairs_", id, "_nondefault.Rdata", sep = ""))
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123 }
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124 rb_dada_fwd <- dada2::removeBimeraDenovo(dada_fwd[["F3D0_S188_L001"]])
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125 write.table(rb_dada_fwd, file = "removeBimeraDenovo_F3D0_dada_uniques.tab", quote = FALSE, sep = "\t", row.names = TRUE, col.names = FALSE)
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126
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127 rb_merged <- dada2::removeBimeraDenovo(merged, method = "pooled")
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128 saveRDS(rb_merged, file = "removeBimeraDenovo_F3D0_mergepairs.Rdata")
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129
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130 # SeqCounts
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131 get_n <- function(x) {
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132 sum(dada2::getUniques(x))
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133 }
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134
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135 print("seqCounts ft")
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136 samples <- list()
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137 samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header = TRUE, sep = "\t", row.names = 1)
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138 dname <- "filter"
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139 tdf <- samples[["F3D0_S188_L001_R1_001.tab"]]
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140 names(tdf) <- paste(dname, names(tdf))
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141 tdf <- cbind(data.frame(samples = names(samples)), tdf)
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142 write.table(tdf, "seqCounts_filter.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
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143
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144 samples <- list()
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145 samples[["F3D0_S188_L001_R1_001.tab"]] <- read.table("F3D0_S188_L001_R1_001.tab", header = TRUE, sep = "\t", row.names = 1)
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146 samples[["F3D141_S207_L001_R1_001.tab"]] <- read.table("F3D141_S207_L001_R1_001.tab", header = TRUE, sep = "\t", row.names = 1)
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147 dname <- "filter"
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148 tdf <- samples[["F3D0_S188_L001_R1_001.tab"]]
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149 tdf <- rbind(tdf, samples[["F3D141_S207_L001_R1_001.tab"]])
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150 names(tdf) <- paste(dname, names(tdf))
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151 tdf <- cbind(data.frame(samples = names(samples)), tdf)
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152 write.table(tdf, "seqCounts_filter_both.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
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153
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154 print("seqCounts dada")
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155 samples <- list()
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156 samples[["dada_F3D0_S188_L001_R1.Rdata"]] <- readRDS("dada_F3D0_S188_L001_R1.Rdata")
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157 samples[["dada_F3D141_S207_L001_R1.Rdata"]] <- readRDS("dada_F3D141_S207_L001_R1.Rdata")
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158 dname <- "dadaF"
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159 tdf <- data.frame(samples = names(samples))
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160 tdf[[dname]] <- sapply(samples, get_n)
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161 write.table(tdf, "seqCounts_dadaF.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
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162
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163 print("seqCounts mp")
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164 samples <- list()
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165 samples[["mergePairs_F3D0_S188_L001.Rdata"]] <- readRDS("mergePairs_F3D0_S188_L001.Rdata")
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166 samples[["mergePairs_F3D141_S207_L001.Rdata"]] <- readRDS("mergePairs_F3D141_S207_L001.Rdata")
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167 dname <- "merge"
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168 tdf <- data.frame(samples = names(samples))
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169 tdf[[dname]] <- sapply(samples, get_n)
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170 write.table(tdf, "seqCounts_merge.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
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171
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172 print("seqCounts st")
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173 samples <- list()
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174 samples <- t(as.matrix(read.table("makeSequenceTable.tab", header = TRUE, sep = "\t", row.names = 1)))
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175 dname <- "seqtab"
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176 tdf <- data.frame(samples = row.names(samples))
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177 tdf[[dname]] <- rowSums(samples)
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178 write.table(tdf, "seqCounts_seqtab.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)
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179
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180 print("seqCounts rb")
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181 samples <- list()
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182 samples <- t(as.matrix(read.table("removeBimeraDenovo.tab", header = TRUE, sep = "\t", row.names = 1)))
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183 dname <- "nochim"
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184 tdf <- data.frame(samples = row.names(samples))
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185 tdf[[dname]] <- rowSums(samples)
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186 write.table(tdf, "seqCounts_nochim.tab", quote = FALSE, sep = "\t", row.names = FALSE, col.names = TRUE)