Chapter 2 Convert inputdata into phyloseq object
r Biocpkg(“phyloseq”) (McMurdie and Holmes 2013) is the most popular Biocondcutor package used by the microbiome research field, and phyloseq-class objects are a great data-standard for microbiota data in R. Therefore, the core functions in XMAS take phyloseq-class object as input. In the phyloseq object, information on OTU abundances, taxonomy of OTUs, the phylogenetic tree and metadata is stored.
This tutorial will introduce you the basic steps to convert results from the in-house pipeline into phyloseq-class object. More importantly on how to look at your data and filter appropriately. We will use the inputs from /home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/.
Loading packages
2.1 Metaphlan2/3
The result of the in-house Metaphlan2/3 pipeline.
2.1.1 Merging output of metaphlan
- go to the directory of metaphlan created by in-house metaphlan pipeline
- extract the absolute path of output
ls /share/projects/Engineering/pipeline_output/PipelineJob_141157_20220823/*/humann2/*.tsv | grep -E "metaphlan" > metaphlan_BJ_RoundG_filepath.tsv- Saving the following python codes into
merge_metaphlan.py
import argparse as ap
import os
import re
import pandas as pd
import numpy as np
parser = ap.ArgumentParser(description='combining results from metaphlan')
parser.add_argument('-f', '--file', type=str,
help='file contains metaphlan results', required=True)
parser.add_argument('-p', '--prefix', type=str,
help='prefix of output', required=True)
parser.add_argument('-t', '--taxa', type=str, default="species",
help='specific taxa level', required=False)
parser.add_argument('-k', '--kind', type=str, default="metaphlan2",
help='version of metaphlan', required=False)
parser.add_argument('-o', '--out', type=str, default="./",
help='output path', required=False)
args = parser.parse_args()
def Filename_to_filepath(filelist):
file2path = {}
with open(filelist, 'r') as f:
files = f.readlines()
for File in files:
File = File.strip()
if os.path.exists(File):
# seqid is either numeric(18933) or character (CJR0607)
FileName = str(re.match(r'\S+[humann2|humann3]\/'
r'(\d+|\d+\_\d+|\d+\_\d+\_\d+|\w+\d+)'
r'\_metaphlan_\S+_list.tsv',
File)[1])
file2path[FileName] = File
return(file2path)
def Read_File(filenames, samplenames, taxalevels, filetype):
if filetype == 'metaphlan2':
# skip row with '#' and the 1st row
dat = pd.read_table(filenames,
sep='\t',
skip_blank_lines=True,
comment='#',
skiprows=1,
header=None)
dat1 = dat.iloc[:, 0:2]
else:
# skip row with '#'
dat = pd.read_table(filenames,
sep='\t',
skip_blank_lines=True,
comment='#',
#skiprows=1,
header=None)
dat1 = dat.iloc[:, [0, 2]]
if taxalevels[0] == taxalevels[1]:
df_temp = dat1[dat1.iloc[:, 0].str.contains(taxalevels[0])]
else:
# filtering the rows where taxa doesn't contains the specific taxa level
df = dat1[dat1.iloc[:, 0].str.contains(taxalevels[0])]
df_temp = df[df.iloc[:, 0].str.contains(taxalevels[1]) == False]
# adding column name to the respective columns
df_temp.columns = ['taxaid', samplenames]
res = df_temp.copy()
# sorting by taxaid
res.sort_values('taxaid', inplace=True)
# dropping ALL duplicate values
res.drop_duplicates(
subset='taxaid',
keep=False,
inplace=True)
return(res)
def Merge_Metaphlan(filedict, taxa, filetype):
if taxa == "species":
taxalevels = 's__|t__'
elif taxa == "genus":
taxalevels = 'g__|s__'
elif taxa == "family":
taxalevels = 'f__|g__'
elif taxa == "order":
taxalevels = 'o__|f__'
elif taxa == "class":
taxalevels = 'c__|o__'
elif taxa == "phylum":
taxalevels = 'p__|c__'
taxa_levels = taxalevels.split('|')
if len(taxa_levels) != 2:
print("please check your taxa")
exit()
df = pd.DataFrame()
for key in filedict:
temp_df = Read_File(filedict[key], key, taxa_levels, filetype)
if df.empty:
df = temp_df
else:
df = pd.merge(df,
temp_df,
on="taxaid",
how='outer')
res = df.replace(np.nan, 0)
return(res)
def Make_dir(dirpath):
if not os.path.exists(dirpath):
os.mkdir(dirpath)
def main():
file = args.file
out = args.out
taxa = args.taxa
kind = args.kind
prefix = args.prefix
file2dic = Filename_to_filepath(file)
df_res = Merge_Metaphlan(file2dic, taxa, kind)
Make_dir(out)
outfile_name = out + "/" + prefix + ".csv"
df_res.to_csv(outfile_name, sep='\t', encoding='utf-8', index=False)
print('Congratulations, Program Ended Without Problem')
main()- merging output: for example: species levels
python merge_metaphlan.py \
-f metaphlan_BJ_RoundG_filepath.tsv \
-p metaphlan2_BJ_RoundG_merge_species \
-t species \
-o ./profile/Finally, you would obtain the file integrated all the output in this project folder /share/projects/Engineering/pipeline_output/PipelineJob_141157_20220823/, and then use it to do data analysis.
2.1.2 Importing results
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/MGS/metaphlan2_merged.tsv
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/MGS/metadata.txt
2.1.3 taxa table
metaphlan2_res_list <- import_metaphlan_taxa(data_metaphlan2 = metaphlan2_res,
taxa_level = "Species")
tax_tab <- metaphlan2_res_list$tax_tab
head(tax_tab)## Kingdom Phylum Class Order Family Genus
## s__Actinomyces_graevenitzii k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_johnsonii k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_massiliensis k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_odontolyticus k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_oris k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_viscosus k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## Species
## s__Actinomyces_graevenitzii s__Actinomyces_graevenitzii
## s__Actinomyces_johnsonii s__Actinomyces_johnsonii
## s__Actinomyces_massiliensis s__Actinomyces_massiliensis
## s__Actinomyces_odontolyticus s__Actinomyces_odontolyticus
## s__Actinomyces_oris s__Actinomyces_oris
## s__Actinomyces_viscosus s__Actinomyces_viscosus2.1.4 otu table
metaphlan2_res_list <- import_metaphlan_taxa(data_metaphlan2 = metaphlan2_res,
taxa_level = "Species")
otu_tab <- metaphlan2_res_list$abu_tab
head(otu_tab)## s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18
## s__Actinomyces_graevenitzii 3.58e-05 0 0.0e+00 0 0 0 7.20e-06 0.0e+00 0.0000000 0 1.40e-05 0e+00 0.00e+00 0.0e+00 0 0 0 6.1e-06
## s__Actinomyces_johnsonii 0.00e+00 0 0.0e+00 0 0 0 0.00e+00 0.0e+00 0.0000000 0 0.00e+00 0e+00 1.15e-05 0.0e+00 0 0 0 0.0e+00
## s__Actinomyces_massiliensis 0.00e+00 0 0.0e+00 0 0 0 2.95e-05 0.0e+00 0.0000000 0 0.00e+00 0e+00 0.00e+00 0.0e+00 0 0 0 0.0e+00
## s__Actinomyces_odontolyticus 7.70e-05 0 1.7e-06 0 0 0 2.47e-05 5.8e-06 0.0001175 0 6.16e-05 0e+00 1.66e-05 3.9e-06 0 0 0 0.0e+00
## s__Actinomyces_oris 0.00e+00 0 0.0e+00 0 0 0 2.81e-05 0.0e+00 0.0000135 0 0.00e+00 0e+00 0.00e+00 0.0e+00 0 0 0 0.0e+00
## s__Actinomyces_viscosus 4.60e-06 0 0.0e+00 0 0 0 0.00e+00 0.0e+00 0.0000039 0 6.20e-06 7e-06 1.10e-06 0.0e+00 0 0 0 0.0e+00
## s19 s20 s21 s22 refE
## s__Actinomyces_graevenitzii 1.60e-06 0 0 0.00e+00 0
## s__Actinomyces_johnsonii 0.00e+00 0 0 0.00e+00 0
## s__Actinomyces_massiliensis 0.00e+00 0 0 2.52e-05 0
## s__Actinomyces_odontolyticus 4.19e-05 0 0 0.00e+00 0
## s__Actinomyces_oris 0.00e+00 0 0 7.40e-06 0
## s__Actinomyces_viscosus 0.00e+00 0 0 7.28e-05 02.1.5 metadata table
## Group phynotype
## s1 BB 0.00
## s2 AA 2.50
## s3 BB 0.00
## s4 AA 1.25
## s5 AA 30.00
## s6 AA 15.002.1.6 phyloseq object
metaphlan2_ps <- get_metaphlan_phyloseq(
otu_tab = otu_tab,
sam_tab = sam_tab,
tax_tab = tax_tab)
metaphlan2_ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 322 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 322 taxa by 7 taxonomic ranks ]we obtain the phyloseq-class object and then use it to perform data analysis.
Here, the phyloseq object comprises five components (OTU Table, Sample Data, and Taxonomy Table).
2.2 Transform the abundance of taxa whose relative abundance under Limit Of Detection (LOD) into Zeros
metaphlan2_ps_LOD_species <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Species",
cutoff = 1e-04)
metaphlan2_ps_LOD_species## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 192 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 192 taxa by 7 taxonomic ranks ]2.3 Systematic Information
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-10-27
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
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##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────References
McMurdie, Paul J, and Susan Holmes. 2013. “Phyloseq: An r Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PloS One 8 (4): e61217.