Get the App
SLTechnology News&Howtos  ›  Development  › 

What is the standardization method of OTUtable

Shulou Source: shulou.com Published: 2022-06-01 04:39:05 10月02日 Update

This article mainly introduces "what is the OTUtable standardization method". In daily operation, I believe many people have doubts about what the OTUtable standardization method is. I have consulted all kinds of materials and sorted out simple and easy operation methods. I hope to help you answer the question of "what is the OTUtable standardization method"! Next, please follow the small series to learn together!

OTUtable standard method

1. Squaring:

single_rarefaction.py -i pick_de_novo_otus/otu_table_clean.biom -o pick_de_novo_otus/otu_table_clean_rare.biom -d 2032

2. css method or deseq 2 method

normalize_table.py -i otu_table_clean.biom -a CSS -o otu_table_clean_css.biomnormalize_table.py -i otu_table_clean.biom -a DESeq2 -z -o otu_table_clean_deseq2_norm_no_negatives.biom

Data processing after standardization:

#################################################################################//www.example.com www.example.com normalized_otu_css/normalize_table. py-i $workdir/5.pick_otu/pick_de_novo_otus/otu_table_clean. bio-a CSS-o normalized_otu_css/CSS_normalized_otu_table. organism convert-i normalized_otu_css/CSS_normalized_otu_table. organism-o normalized_otu_css/CSS_normalized_otu_table. organism convert-i normalized_otu_css/CSS_normalized_otu_table. organism convert-o normalized_otu_css/CSS_normalized_otu_table. u_table1.txt--to-tsv--header-key taxonomy #Remove OTUgrep-v' k;_;_; B; a; c; t; e; r; www.ncbi.nlm.nih.gov/pmc/articles/PMC4010126/ a 'normalized_otu_css/CSS_normalized_otu_table1.txt> normalized_otu_css/CSS_normalized_otu_table. txt #Note bio format conversion json to hdf 5: https://www.. com/article/1314biom convert-i normalized_otu_css/CSS_normalized_otu_table.txt-o normalized_otu_css/CSS_normalized_otu_table_hdf5.biom--table-type="OTU table"--to-hdf5--process-obs-metadata taxonomybiom summarize-table-i normalized_otu_css/CSS_normalized_otu_table_hdf5.biom-o normalized_otu_css/CSS_normalized_otu_table_summary.txt#normalize OTU abundance relative abundance echo Summarize taxaecho summarize_taxa: level 2,3,4,5,6,7> taxa_summary_parm. txtecho plot_taxa_summary: x_width 8> taxa_summary_parm. txtecho plot_taxa_summary: bar_width 0.5> taxa_summary_parm. txtecho plot_taxa_summary: chart_type bar>>taxa_summary_parm.txtsummarize_taxa.py-i normalized_otu_css/CSS_normalized_otu_table_hdf5.biom-o normalized_otu_css/absolute_abundance-a--level 2,3,4,5,6,7summarize_taxa.py-i normalized_otu_css/CSS_normalized_otu_table_hdf5.biom-o normalized_otu_css/relative_abundance--level 2,3,4,5,6, 7mkdir normalized_otu_css/group_otus_cssfor i in city loc country;do collapse_samples.py-b normalized_otu_css/CSS_normalized_otu_table_hdf5.biom-m $fastmap--collapse_fields $i--output_mapping_fp normalized_otu_css/group_otus_css/group.$ i.txt --output_biom_fp normalized_otu_css/group_otus_css/otu_table_$i.biombiom convert -i normalized_otu_css/group_otus_css/otu_table_$i.biom -o normalized_otu_css/group_otus_css/otu_table_${i}.txt --to-tsv --header-key taxonomysummarize_taxa.py -i normalized_otu_css/group_otus_css/otu_table_$i.biom -o normalized_otu_css/group_otus_css/taxa_summary_relative.$ {i} -L 2,3,4,5,6,7summarize_taxa.py -i normalized_otu_css/group_otus_css/otu_table_$i.biom -o normalized_otu_css/group_otus_css/taxa_summary_absolute.$ {i}- a-L 2, 3, 4, 5, 6, 7 done At this point, the study of "What is the OTUtable standardization method" is over, hoping to solve everyone's doubts. Theory and practice can better match to help you learn, go and try it! If you want to continue learning more relevant knowledge, please continue to pay attention to the website, Xiaobian will continue to strive to bring more practical articles for everyone!

Tags: Methods standards standardization learning abundance more help practical next only data data processing articles formats comments theories knowledge articles websites materials Apple Docker Huawei Linux macOS MariaDB Microsoft MySQL NVidia OPPO Reno Docker MySQL Xiaomi MariaDB macOS