Mercurial > repos > simon-gladman > phyloseq_filter

annotate phyloseq_filter.R @ 2:54897b7e0551 draft default tip

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Updated tool
author simon-gladman
date Thu, 22 Nov 2018 08:13:52 -0500
parents 9fbb104e16d9
children
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1 library('getopt')
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2 library('ape')
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3 library('ggplot2')
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4 suppressPackageStartupMessages(library('phyloseq'))
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5 library(plyr)
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6 Sys.setenv("DISPLAY"=":1")
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7 library(biomformat)
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8 library(jsonlite)
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9 suppressPackageStartupMessages(library(metagenomeSeq))
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10 suppressPackageStartupMessages(library("doParallel"))
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11 ncores = ceiling(detectCores() * 0.8)
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12 registerDoParallel(cores=ncores)
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13
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14 options(warn=-1)
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15
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16 theme_set(theme_bw())
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17 ## ggplot
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18 # http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
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19 # https://gist.github.com/Mikeyj/5429538
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20 # http://microbiome-tutorials.readthedocs.io/en/latest/_static/Composition.html
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21 # https://rstudio-pubs-static.s3.amazonaws.com/268156_d3ea37937f4f4469839ab6fa2c483842.html#otus_that_differ_by (stacked bar plot)
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22
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23 ## color
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24 ## http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
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25
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26 #http://saml.rilspace.com/creating-a-galaxy-tool-for-r-scripts-that-output-images-and-pdfs
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27 #http://joey711.github.io/phyloseq-demo/phyloseq-demo.html
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28 option_specification = matrix(c(
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29 'otu_table','o',2,'character',
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30 'tax_table','t',2,'character',
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31 'meta_table','m',2,'character',
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32 'biom','b',2,'character',
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33 'filter','f',2,'numeric',
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34 'kingdom','k',2,'character',
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35 'cutoff','c',2,'numeric',
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36 'keep','p',2,'numeric',
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37 'outbiom','h',2,'character',
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38 'outdir','d',2,'character',
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39 'htmlfile','w',2,'character'
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40 ),byrow=TRUE,ncol=4);
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41
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42
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43 options <- getopt(option_specification);
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44 options(bitmapType="cairo")
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45
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46 if (!is.null(options$outdir)) {
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47 # Create the directory
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48 dir.create(options$outdir,FALSE)
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49 }
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50
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51
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52
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53 cutoff_value<-options$cutoff
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54 ### select a kingdom for phyloseq plot (e.g., "phylum")
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55 #kingdom_str<-colnames(tax_table)[options$kingdom]
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56 kingdom_str<-options$kingdom
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57 keep<-options$keep
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58 filter<-options$filter
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59
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60 ### prepare the directory and file name
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61 pdffile <- gsub("[ ]+", "", paste(options$outdir,"/pdffile.pdf"))
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62 pngfile_before_filtering <- gsub("[ ]+", "", paste(options$outdir,"/barplot_before_filtering.png"))
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63 pngfile_after_filtering <- gsub("[ ]+", "", paste(options$outdir,"/barplot_after_filtering.png"))
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64 pngfile_pre_phyla_filtering <- gsub("[ ]+", "", paste(options$outdir,"/barplot_before_phyla_filtering.png"))
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65 pngfile_post_phyla_filtering<- gsub("[ ]+", "", paste(options$outdir,"/barplot_after_phyla_filtering.png"))
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66 htmlfile <- gsub("[ ]+", "", paste(options$htmlfile))
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67
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68 ### overwrite the write_biom function for proper BIOM format
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69 ### https://github.com/smdabdoub/biomformat/blob/master/R/IO-methods.R#L124
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70 write_biom <- function(x, biom_file){
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71 cat(toJSON(x, always_decimal=TRUE, auto_unbox=TRUE), file=biom_file)
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72 }
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73
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74 ### This function accepts different two different type of BIOM file format
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75 readBIOM<-function(inBiom){
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76 tryCatch({
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77 phyloseq_obj<-import_biom(inBiom,parallel=TRUE)
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78 return(phyloseq_obj)
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79 },
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80 error=function(e){
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81 biom_obj<-read_biom(inBiom)
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82
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83 otu_matrix = as(biom_data(biom_obj), "matrix")
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84 OTU_TABLE = otu_table(otu_matrix, taxa_are_rows=TRUE)
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85
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86 taxonomy_matrix = as.matrix(observation_metadata(biom_obj), rownames.force=TRUE)
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87 TAXONOMY_TABLE = tax_table(taxonomy_matrix)
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88
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89 metadata.temp<-sample_metadata(biom_obj)
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90 METADATA_TABLE<-plyr::ldply(metadata.temp, rbind)
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91 rownames(METADATA_TABLE)<-as.character(METADATA_TABLE$.id)
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92
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93 phyloseq_obj = phyloseq(OTU_TABLE, TAXONOMY_TABLE,sample_data(METADATA_TABLE))
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94 return(phyloseq_obj)
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95 }
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96 )
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97 }
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98
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99
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100 create_PDF<-function(pdf_file,OTU_DATAFRAME_BEFORE_FILTERING,OTU_DATAFRAME_AFTER_FILTERING,physeq_pre_phyla_filtering,physeq_post_phyla_filtering,kingdom_str,htmlfile,pngfile_before_filtering,pngfile_after_filtering,pngfile_pre_phyla_filtering,pngfile_post_phyla_filtering){
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101 pdf(pdf_file);
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102 barplot_before_filtering<-ggplot(OTU_DATAFRAME_BEFORE_FILTERING,aes(rownames(OTU_DATAFRAME_BEFORE_FILTERING))) +
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103 geom_bar(aes(weight=Abundance),fill="tomato3") +
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104 labs(title="Sample Depth Bar Chart",subtitle="Sample Vs Abundance (Before Filtering)",caption="source: Input Biom") +
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105 xlab("Sample") +
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106 ylab("Abundance") +
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107 theme(axis.text.x=element_text(angle=65,vjust=0.6))
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108 print(barplot_before_filtering)
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109
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110 barplot_after_filtering<-ggplot(OTU_DATAFRAME_AFTER_FILTERING,aes(rownames(OTU_DATAFRAME_AFTER_FILTERING))) +
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111 geom_bar(aes(weight=Abundance),fill="blue") +
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112 labs(title="Sample Depth Bar Chart",subtitle="Sample Vs Abundance (After Filtering)", caption="source: Input Biom") +
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113 xlab("Sample") +
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114 ylab("Abundance") +
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115 theme(axis.text.x=element_text(angle=65,vjust=0.6))
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116 print(barplot_after_filtering)
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117
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118 barplot_pre_phyla_filtering<-plot_bar(physeq_pre_phyla_filtering, x=colnames(sample_data(physeq_pre_phyla_filtering))[1], fill=kingdom_str) +
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119 geom_bar(stat="identity", position="stack") +
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120 labs(title=paste("Sample Depth Bar Chart",kingdom_str,sep=":"),subtitle="Sample Vs Abundance (Pre phyla Filtering)",caption="source: Input Biom") +
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121 xlab("Sample") +
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122 ylab("Abundance") +
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123 theme(axis.text.x=element_text(angle=90,vjust=0.6)) +
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124 scale_fill_hue()
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125 print(barplot_pre_phyla_filtering)
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126
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127 barplot_post_phyla_filtering<-plot_bar(physeq_post_phyla_filtering, x=colnames(sample_data(physeq_post_phyla_filtering))[1], fill=kingdom_str) +
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128 geom_bar(stat="identity", position="stack") +
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129 labs(title=paste("Sample Depth Bar Chart",kingdom_str,sep=":"),subtitle="Sample Vs Abundance (Post phyla Filtering)",caption="source: Input Biom") +
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130 xlab("Sample") +
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131 ylab("Abundance") +
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132 theme(axis.text.x=element_text(angle=90,vjust=0.6)) +
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133 scale_fill_hue()
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134 print(barplot_post_phyla_filtering)
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135 garbage<-dev.off();
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136
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137 #png('barplot_before_filtering.png')
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138 bitmap(pngfile_before_filtering,"png16m")
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139 barplot_before_filtering_png<-ggplot(OTU_DATAFRAME_BEFORE_FILTERING,aes(rownames(OTU_DATAFRAME_BEFORE_FILTERING))) +
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140 geom_bar(aes(weight=Abundance),fill="tomato3") +
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141 labs(title="Sample Depth Bar Chart",subtitle="Sample Vs Abundance (Before Filtering)",caption="source: Input Biom") +
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142 xlab("Sample") +
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143 ylab("Abundance") +
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144 theme(axis.text.x=element_text(angle=65,vjust=0.6))
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145 print(barplot_before_filtering_png)
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146 garbage<-dev.off()
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147
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148 #png('barplot_after_filtering.png')
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149 bitmap(pngfile_after_filtering,"png16m")
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150 barplot_after_filtering_png<-ggplot(OTU_DATAFRAME_AFTER_FILTERING,aes(rownames(OTU_DATAFRAME_AFTER_FILTERING))) +
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151 geom_bar(aes(weight=Abundance),fill="blue") +
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152 labs(title="Sample Depth Bar Chart",subtitle="Sample Vs Abundance (After Filtering)", caption="source: Input Biom") +
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153 xlab("Sample") +
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154 ylab("Abundance") +
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155 theme(axis.text.x=element_text(angle=65,vjust=0.6))
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156 print(barplot_after_filtering_png)
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157 garbage<-dev.off()
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158
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159 #png('barplot_pre_phyla_filtering.png')
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160 bitmap(pngfile_pre_phyla_filtering,"png16m")
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161 #print(sample_data(physeq_pre_phyla_filtering))
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162 barplot_pre_phyla_filtering<-plot_bar(physeq_pre_phyla_filtering, x=colnames(sample_data(physeq_pre_phyla_filtering))[1], fill=kingdom_str) +
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163 geom_bar(stat="identity", position="stack") +
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164 labs(title=paste("Sample Depth Bar Chart",kingdom_str,sep=":"),subtitle="Sample Vs Abundance (Pre Phyla Filtering)",caption="source: Input Biom") +
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165 xlab("Sample") +
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166 ylab("Abundance") +
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167 theme(axis.text.x=element_text(angle=90,vjust=0.6)) +
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168 scale_fill_hue()
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169 print(barplot_pre_phyla_filtering)
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170 garbage<-dev.off()
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171
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172 #png('barplot_post_phyla_filtering.png')
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173 bitmap(pngfile_post_phyla_filtering,"png16m")
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174 barplot_post_phyla_filtering<-plot_bar(physeq_post_phyla_filtering, x=colnames(sample_data(physeq_post_phyla_filtering))[1], fill=kingdom_str) +
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175 geom_bar(stat="identity", position="stack") +
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176 labs(title=paste("Sample Depth Bar Chart",kingdom_str,sep=":"),subtitle="Sample Vs Abundance (Post Phyla Filtering)",caption="source: Input Biom") +
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177 xlab("Sample") +
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178 ylab("Abundance") +
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179 theme(axis.text.x=element_text(angle=90,vjust=0.6)) +
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180 scale_fill_hue()
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181 print(barplot_post_phyla_filtering)
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182 garbage<-dev.off()
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183
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184 create_HTML(htmlfile)
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185 }
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186
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187 create_HTML<-function(htmlfile){
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188 htmlfile_handle <- file(htmlfile)
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189 html_output = c('<html><body>',
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190 '<table align="center">',
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191 '<tr>',
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192 '<td valign="middle" style="vertical-align:middle;">',
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193 '<a href="pdffile.pdf"><img src="barplot_before_filtering.png"/></a>',
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194 '</td>',
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195 '</tr>',
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196 '<tr>',
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197 '<td valign="middle" style="vertical-align:middle;">',
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198 '<a href="pdffile.pdf"><img src="barplot_after_filtering.png"/></a>',
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199 '</td>',
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200 '</tr>',
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201 '<tr>',
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202 '<td valign="middle" style="vertical-align:middle;">',
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203 '<a href="pdffile.pdf"><img src="barplot_before_phyla_filtering.png"/></a>',
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204 '</td>',
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205 '</tr>',
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206 '<tr>',
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207 '<td valign="middle" style="vertical-align:middle;">',
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208 '<a href="pdffile.pdf"><img src="barplot_after_phyla_filtering.png"/></a>',
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209 '</td>',
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210 '</tr>',
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211 '</table>',
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212 '</body></html>');
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213 writeLines(html_output, htmlfile_handle);
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214 close(htmlfile_handle);
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215 }
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216
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217 convert_phyloseq_otutable_to_dataframe<-function(physeq_obj){
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218 temp.df<-data.frame(otu_table(physeq_obj))
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219 temp.df.counts<-as.data.frame(colSums(temp.df))
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220 colnames(temp.df.counts)<-"Abundance"
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221 #print(temp.df.counts)
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222 return(temp.df.counts)
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223 }
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224
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225 if(!is.null(options$biom)){
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226
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227 #physeq<-import_biom(options$biom)
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228 physeq<-readBIOM(options$biom)
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229
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230 if(length(rank_names(physeq)) == 8){
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231 tax_table(physeq) <- tax_table(physeq)[,-1]
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232 colnames(tax_table(physeq)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
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233 } else {
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234 colnames(tax_table(physeq)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
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235 }
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236
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237 ### select column name from sample table for nmds plot
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238 ## which(colnames(sample_data(biom)) == "vegetation_type_id")
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239 #category_type<-colnames(sample_data(physeq))[options$subset]
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240 #category_type <- options$subset
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241
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242 ### obtain the unique value in the selected column from sample table
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243 #category_option<-unique(sample_data(physeq))[,options$subset]
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244
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245 }else{
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246
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247 ### read the data into correct data type to create phyloseq object
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248 otu_table<-as.matrix(read.table(options$otu_table,header=T,sep="\t"))
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249 tax_table<-as.matrix(read.table(options$tax_table,header=T,sep="\t"))
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250 sample_table<-read.table(options$meta_table,header=T,sep="\t",stringsAsFactors=F)
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251
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252
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253 ### select column name from sample table for nmds plot
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254 #category_type<-colnames(sample_table)[options$category]
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255
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256 ### obtain the unique value in the selected column from sample table
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257 #category_option<-unique(sample_table[,options$category])
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258
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259
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260 ### create a sample object for phyloseq
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261 sample_object<-sample_data(sample_table)
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262
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263 ### create otu object for phyloseq
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264 OTU<-otu_table(otu_table, taxa_are_rows = TRUE)
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265
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266 ### create tax object for phyloseq
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267 TAX<-tax_table(tax_table)
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268
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269 ### create a phyloseq object
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270 physeq = phyloseq(OTU,TAX,sample_object)
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271 }
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272 ### make the first column to be the sample ID in the phyloseq object
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273
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274 firstColumn = sample_data(physeq)[,1]
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275 row_names = rownames(sample_data(physeq))
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276 check = all( firstColumn == row_names)
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277 if(!check){
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278 sample_data(physeq) <- cbind(SampleID= rownames(sample_data(physeq)),sample_data(physeq))
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279 }
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280
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281
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282 ### extract otu table from phyloseq object
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283 before_filtering_dataframe_sampleCounts<-convert_phyloseq_otutable_to_dataframe(physeq)
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284
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285 ### filtering OTUs based on cutoff value (e.g., 5)
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286 #physeq_temp =genefilter_sample(physeq, filterfun_sample(function(x) x > cutoff_value), A=0.1*nsamples(physeq))
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287 physeq_temp =genefilter_sample(physeq, filterfun_sample(function(x) x > cutoff_value), A=filter*nsamples(physeq))
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288
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289 ### phyloseq object after filtered
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290 physeq_filter = prune_taxa(physeq_temp, physeq)
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291
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292
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293
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294 ## Transform to even sampling depth
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295 #physeq_filter = transform_sample_counts(physeq_filter, function(x) 1E6 * x/sum(x))
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296
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297 #after_filtering.dataframe<-data.frame(otu_table(physeq_filter))
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298 #after_filtering_dataframe_sampleCounts<-as.data.frame(colSums(after_filtering.dataframe))
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299 #colnames(after_filtering_dataframe_sampleCounts)<-"Abundance"
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300 after_filtering_dataframe_sampleCounts<-convert_phyloseq_otutable_to_dataframe(physeq_filter)
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301
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302 # create_PDF(pdffile,before_filtering_dataframe_sampleCounts,after_filtering_dataframe_sampleCounts,htmlfile,pngfile_before_filtering,pngfile_after_filtering)
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303
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304 # kingdom_str <- as.numeric(kingdom_str)
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305 ## Keep only the most abundant five phyla
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306
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307 ### Phyla - Pre transformation (Transform to even sampling depth)
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308
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309 #physeq_filter_pre_transform = tapply(taxa_sums(physeq_filter), tax_table(physeq_filter)[, kingdom_str], sum,na.rm=TRUE)
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310
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311 phylum.sum_pre_transform= tapply(taxa_sums(physeq_filter), tax_table(physeq_filter)[, kingdom_str], sum,na.rm=TRUE)
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312
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313 topphyla_pre_transform = names(sort(phylum.sum_pre_transform, TRUE))[1:keep]
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314
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315 physeq_filter_pre_transform = prune_taxa((tax_table(physeq_filter)[, kingdom_str] %in% topphyla_pre_transform), physeq_filter)
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316
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317 ### Phyla - Post Transformation (Transform to even sampling depth)
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318 physeq_filter_post_transform = transform_sample_counts(physeq_filter, function(x) 1E6 * x/sum(x))
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319
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320 phylum.sum_post_transform = tapply(taxa_sums(physeq_filter_post_transform), tax_table(physeq_filter_post_transform)[, kingdom_str], sum,na.rm=TRUE)
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321 ### number of most abundance phyla to keep
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322 topphyla_post_transform = names(sort(phylum.sum_post_transform, TRUE))[1:keep]
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323
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324 physeq_filter_post_transform = prune_taxa((tax_table(physeq_filter_post_transform)[, kingdom_str] %in% topphyla_post_transform), physeq_filter_post_transform)
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325
1
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326 ### remove samples with zero value
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327 otu_table(physeq_filter_post_transform)<-otu_table(physeq_filter_post_transform)[,colSums(otu_table(physeq_filter_post_transform)) > 0]
0
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328
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329 create_PDF(pdffile,before_filtering_dataframe_sampleCounts,after_filtering_dataframe_sampleCounts,physeq_filter_pre_transform,physeq_filter_post_transform,kingdom_str,htmlfile,pngfile_before_filtering,pngfile_after_filtering,pngfile_pre_phyla_filtering,pngfile_post_phyla_filtering)
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330
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331 ### convert phyloseq object to metagenomeSeq - preparing for BIOM output
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332 #metagenomeSeq_obj <- phyloseq_to_metagenomeSeq(physeq_filter_post_transform)
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333 #metagenomeSeq_biom <- MRexperiment2biom(metagenomeSeq_obj)
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334
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335 biom_obj=make_biom(otu_table(physeq_filter_post_transform),sample_metadata=sample_data(physeq_filter_post_transform),observation_metadata=tax_table(physeq_filter_post_transform),matrix_element_type="float")
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336 biom_obj_2_metagenomeSeq_obj<-biom2MRexperiment(biom_obj)
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337 metagenomeSeq_biom <- MRexperiment2biom(biom_obj_2_metagenomeSeq_obj)
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338
0
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339
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340 ## write biom file
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341 write_biom(metagenomeSeq_biom, biom_file=options$outbiom)