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btm

Blood Transcription Modules for interpreting blood transcriptomics data

https://github.com/shuzhao-li/btm

Science Score: 23.0%

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    Low similarity (9.6%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Blood Transcription Modules for interpreting blood transcriptomics data

Basic Info
  • Host: GitHub
  • Owner: shuzhao-li
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 2.95 MB
Statistics
  • Stars: 10
  • Watchers: 0
  • Forks: 6
  • Open Issues: 0
  • Releases: 1
Created over 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

BTM (Blood Transcription Modules)

Gene modules to interpret blood transcriptomics data.

Used as alternative to conventional pathways, offering granular immunology and often better sensitivity. The modules can also be used as gene sets for GSEA analysis. This is the BTM modules described in

  • Li, S., Rouphael, N., Duraisingham, S., Romero-Steiner, S., Presnell, S., Davis, C., ..., Pulendran, B. (2014). Molecular signatures of antibody responses derived from a systems biological study of 5 human vaccines. Nature immunology, 15(2), 195. https://www.nature.com/articles/ni.2789

Installation

pip install BTM

BTM_Plus at 2021

An update with the help of Amnah Siddiqa.

This set of modules excludes all TBA modules, and replaces the cell specific markers by a new set of markers compiled from these three papers:

  • Aran, D., Hu, Z. and Butte, A.J., 2017. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome biology, 18(1), pp.1-14.
  • Zhang, X., Lan, Y., Xu, J., Quan, F., Zhao, E., Deng, C., Luo, T., Xu, L., Liao, G., Yan, M. and Ping, Y., 2019. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic acids research, 47(D1), pp.D721-D728.
  • Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y.Y., Carre, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M. and Zippelius, A., 2019. RNA-Seq signatures normalized by mRNA abundance allow absolute deconvolution of human immune cell types. Cell reports, 26(6), pp.1627-1640.

The cell specific markers were merged by keeping genes in at least half of the source sets. I.e., if 5 genesets exist for the same cell population, a gene is kept if it appears in at least 3 of the genesets. All genesets/modules larger than 100 genes are excluded from BTM_Plus.

```

from BTM.BTMPlus import BTMPlus as B2 len(B2) 276 B2[88] {'id': 'P089', 'name': 'mismatch repair (I)', 'src': ['Li-Pulendran, LiM22.0mismatch repair (I)'], 'genes': ['SMC1A', 'POLA1', 'NCAPG2', 'RFC5', 'RFC4', 'MSH2', 'TMPO', 'MSH6', 'RFC2', 'GMNN', 'BUB1', 'RMI1', 'RACGAP1', 'EXO1', 'POLD3', 'PRIM1', 'ZWINT', 'CHEK1', 'CENPK', 'FIGNL1', 'MCM6', 'RFC3', 'SSBP1', 'TOPBP1', 'RPA3', 'SMC2']} ```

To generate permutations of BTM_Plus

In same directory, change Number_Permutation to your need - python permutate.py

This writes two files, 'resampledBTMPlus.txt', 'random_genesets.txt', each line of a geneset of size 50 (default).

Example use of the 2013 version (Companion to original paper)

To convert gene level data to BTM module activity scores: from btm.btm_tool import genetable_to_activityscores genetable_to_activityscores(infile, outfile)

Download tutorial package at https://github.com/shuzhao-li/BTM/releases/download/v1/ni.2789-S5.zip (same as original paper supplement ni.2789-S5.zip, but Nature link was broken).

This "BTMtutorialpackage" download package should contain - btmtool.py, btmexampledata.py, MCV4D3v0probesets.txt, geneabcorrelation.rnk, BTMforGSEA20131008.gmt, monocytesvsbcells.txt.

The btm_tool.py is to illustrate

  1. Converting gene level data to BTM activity table. (Can also convert Affy probeset level data to gene level data.)
  2. Enrichment test of an input gene list.
  3. Testing antibody correlation to gene expression at module level.

Owner

  • Name: Shuzhao
  • Login: shuzhao-li
  • Kind: user

GitHub Events

Total
  • Create event: 1
  • Issues event: 1
  • Release event: 1
  • Watch event: 2
  • Issue comment event: 1
  • Push event: 1
  • Fork event: 1
Last Year
  • Create event: 1
  • Issues event: 1
  • Release event: 1
  • Watch event: 2
  • Issue comment event: 1
  • Push event: 1
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 9
  • Total Committers: 1
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 4
  • Committers: 1
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Shuzhao Li s****i@g****m 9

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 2
  • Total pull requests: 0
  • Average time to close issues: 12 months
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • ggruenhagen3 (1)
  • shuzhao-li (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 183 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
pypi.org: btm

Blood Transcription Modules for transcriptomics analysis

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 183 Last month
Rankings
Dependent packages count: 10.0%
Forks count: 15.3%
Stargazers count: 25.0%
Downloads: 26.2%
Average: 28.8%
Dependent repos count: 67.6%
Maintainers (1)
shuzhao
Last synced: 11 months ago

Dependencies

setup.py pypi
  • numpy *
  • scipy *