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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/decontam commit 8eca6c026e7bd7ab2e471485cb93b8dee1cd2c07
author iuc
date Sun, 09 Mar 2025 19:27:13 +0000
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86da5c894956 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/decontam commit 9e0ce6d02ee71d5b974ef615b1c5286bc45d8e6b
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1 ---
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2 title: "decontam_docs"
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3 output: html_document
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4 date: "2024-09-10"
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5 ---
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7 # This R markdown generates the test data for the wrapper and can be used to test the functions used in the configfile
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9 ```{r setup, include=FALSE}
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10 knitr::opts_chunk$set(echo = TRUE)
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11 ```
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13 ## Install test env and run studio in this env
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15 ```{bash install}
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16 mamba create --name decontam bioconductor-decontam bioconductor-phyloseq r-tidyverse
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17 mamba activate decontam
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18 rstudio
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19 ```
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21 ### Check correct R home
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23 ```{r home}
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24 R.home()
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25 ```
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27 ## Get test data
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29 Create test data for wrapper, it should be able to use a matrix and vector as well as phyloseq object as input.
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31 ```{r store test data}
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32 R.home()
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33 library(phyloseq)
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34 packageVersion("phyloseq")
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35 library(ggplot2)
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36 packageVersion("ggplot2")
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37 library(decontam)
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38 packageVersion("decontam")
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40 ps <- readRDS(system.file("extdata", "MUClite.rds", package = "decontam"))
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42 # Sample from a physeq object with a sampling function.
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43 # ps: physeq object to be sampled
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44 # fun: function to use for sampling (default `sample`)
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45 # ...: parameters to be passed to fun,
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46 # see `help(sample)` for default parameters
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47 sample_ps <- function(ps, fun = sample, ...) {
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48 ids <- sample_names(ps)
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49 sampled_ids <- fun(ids, ...)
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50 ps <- prune_samples(sampled_ids, ps)
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51 return(ps)
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52 }
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53
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54 # the initial object is to big for the test case so we subsample
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55 ps <- sample_ps(ps, size = 200)
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56
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57 ## ps
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58 # get otu table
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59 otu <- as(otu_table(ps), "matrix")
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60 head(otu[, 1:10], 10)
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61
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62 # add control column to sample data
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63 sample_data(ps)$control <- sample_data(ps)$Sample_or_Control == "Control Sample"
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64 # store as 0 and 1
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65 sample_data(ps)$control <- as.integer(sample_data(ps)$control)
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66 head(sample_data(ps), 1000)
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67
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68 metadata <- as(sample_data(ps), "matrix")
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69 head(metadata, 1000)
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70
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71 # store test data
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72 # stores the row names as column,
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73 # see https://stackoverflow.com/questions/2478352
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74 # /write-table-writes-unwanted-leading-empty-column-to-header-when-has-rownames
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75 write.table(data.frame("SampleID" = rownames(otu), otu),
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76 file = file.path(getwd(), "otu_input.tsv"),
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77 sep = "\t",
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78 row.names = FALSE,
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79 quote = FALSE
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80 )
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81 write.table(data.frame("SampleID" = rownames(metadata), metadata),
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82 file = file.path(getwd(), "metadata_input.tsv"),
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83 sep = "\t",
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84 row.names = FALSE,
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85 quote = FALSE
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86 )
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87
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88 saveRDS(ps, file.path(getwd(), "phyloseq_input.rds"))
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89 ```
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90
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91 ## Load test data
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92
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93 ```{r load test data}
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94 library(tidyverse)
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95
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96 # get OTU table (first column is the OTU/ASV ID)
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97 otu <- read_tsv(file.path(getwd(), "otu_input.tsv"))
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98 # use first column as colname
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99 otu2 <- otu %>% tibble::column_to_rownames(colnames(otu)[1])
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100 otu <- otu_table(otu2, taxa_are_rows = FALSE)
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101
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102 # get metadata table must have matching OTU/ASV ID in first column
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103 meta <- read_tsv(file.path(getwd(), "metadata_input.tsv"))
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104 # use first column as colname
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105
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106 meta2 <- meta %>% tibble::column_to_rownames(colnames(meta)[1])
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107 control_column <- "control"
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108
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109 # convert 0/1 to bool for the control column and store in control column
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110 meta2$control <- as.logical(meta2[[control_column]])
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111 sampledata <- sample_data(meta2)
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112
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113 # create dummy tax table (actually not needed,
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114 # but nice to learn how to load phyloseq objects)
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115 taxmat <- as.data.frame(matrix(sample(letters, 10, replace = TRUE),
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116 nrow = ncol(otu2), ncol = 7
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117 ))
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118 rownames(taxmat) <- colnames(otu2)
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119 tax <- tax_table(as.matrix(taxmat))
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120
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121 ps <- phyloseq(otu, tax, sampledata)
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122 ```
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123
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124 # plot 1
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125
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126 ```{r plot library size vs control}
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127 # Put sample_data into a ggplot-friendly data.frame
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128 df <- as.data.frame(sample_data(ps))
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129 df$LibrarySize <- sample_sums(ps)
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130 df <- df[order(df$LibrarySize), ]
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131 df$Index <- seq_len(nrow(df))
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132 ggplot(data = df, aes(x = Index, y = LibrarySize, color = control)) +
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133 geom_point()
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134 ```
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135
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136 # plot 2
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137
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138 ```{r plot prevalence}
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139 contamdf_prev <- isContaminant(ps,
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140 method = "prevalence",
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141 neg = "control",
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142 threshold = 0.5
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143 )
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144 table(contamdf_prev$contaminant)
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145
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146 ps_pa <- transform_sample_counts(ps, function(abund) 1 * (abund > 0))
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147 ps_pa_neg <- prune_samples(sample_data(ps_pa)$control == TRUE, ps_pa)
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148 ps_pa_pos <- prune_samples(sample_data(ps_pa)$control == FALSE, ps_pa)
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149 # Make data_frame of prevalence in positive and negative samples
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150 df_pa <- data.frame(
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151 pa_pos = taxa_sums(ps_pa_pos), pa_neg = taxa_sums(ps_pa_neg),
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152 contaminant = contamdf_prev$contaminant
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153 )
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154 ggplot(data = df_pa, aes(x = pa_neg, y = pa_pos, color = contaminant)) +
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155 geom_point() +
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156 xlab("Prevalence (Negative Controls)") +
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157 ylab("Prevalence (True Samples)")
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158 ```
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159
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160 # generate output
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161
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162 ```{r remove contams}
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163 id_name <- colnames(otu)[1]
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164
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165 ps_noncontam <- prune_taxa(!contamdf_prev$contaminant, ps)
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166
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167 otu_table(ps_noncontam) %>%
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168 as.data.frame() %>%
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169 rownames_to_column(id_name) <- otu
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170
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171 write.table(otu,
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172 file = file.path(getwd(), "otu_output.tsv"),
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173 sep = "\t",
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174 row.names = FALSE,
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175 )
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176 ```