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scTenifoldKnk

R/MATLAB package to perform virtual knockout experiments on single-cell gene regulatory networks.

https://github.com/cailab-tamu/sctenifoldknk

Science Score: 10.0%

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    CITATION.cff file
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    1 of 5 committers (20.0%) from academic institutions
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    Low similarity (10.1%) to scientific vocabulary

Keywords

functional-genomics gene-function gene-knockout gene-regulatory-network virtual-knockout-experiments
Last synced: 11 months ago · JSON representation

Repository

R/MATLAB package to perform virtual knockout experiments on single-cell gene regulatory networks.

Basic Info
  • Host: GitHub
  • Owner: cailab-tamu
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 2.11 GB
Statistics
  • Stars: 38
  • Watchers: 8
  • Forks: 6
  • Open Issues: 17
  • Releases: 0
Topics
functional-genomics gene-function gene-knockout gene-regulatory-network virtual-knockout-experiments
Created over 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

scTenifoldKnk

A R/MATLAB/Python package to perform virtual knockout experiments on single-cell gene regulatory networks. scTenifoldKnk is a machine learning workflow that performs virtual knockout experiments using single-cell RNA sequencing (scRNAseq) data from wild-type (WT) control samples as input. Constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the genes outdegree edges to zero. scTenifoldKnk then compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the genes function in the analyzed cells.

Python version of scTenifoldKnk is available at: https://github.com/qwerty239qwe/scTenifoldpy

MATLAB version is available at: https://github.com/jamesjcai/scGEAToolbox

Install:

You can install scTenifoldKnk/R using the following command:

{R} library(remotes) install_github('cailab-tamu/scTenifoldKnk') library(scTenifoldKnk)

Available functions:

|Code| Function | |:-|:-| |scTenifoldKnk|Perform virtual knockout experiments on single-cell gene regulatory networks|

Input:

The required input for scTenifoldKnk is an expression matrix with genes in the rows and cells (barcodes) in the columns. Data is expected to be previously normalized or not normalized if QC = TRUE.

Running time:

The running time of scTenifoldKnk is largely dependent on how long it takes to construct scGRNs from subsampled expression matrices. Time increases proportional to the number of cells and genes in the dataset used as input. Below is a table of running times under different scenarios:

| Number of Cells | Number of Genes | Running Time | |-----------------|-----------------|--------------| | 300 | 1000 | 3.45 min | | 1000 | 1000 | 4.25 min | | 1000 | 5000 | 171.88 min (2 h 51.6 min) | | 2500 | 5000 | 175.29 min (2 h 55.3 min) | | 5000 | 5000 | 188.88 min (3 h 8.9 min) | | 5000 | 7500 | 189.51 min (3 h 9.5 min) | | 7500 | 5000 | 615.45 min (10 h 15.5 min) | | 7500 | 7500 | 616.12 min (10 h 16.1 min) |

Output:

The output of scTenifoldKnk is a list with 3 slots as follows: * tensorNetworks: The computed weight-averaged denoised gene regulatory networks after CANDECOMP/PARAFAC (CP) tensor decomposition. It includes two slots with: * X: The constructed network for the X sample. * Y: The constructed network for the Y sample. * manifoldAlignment: The generated low-dimensional features result of the non-linear manifold alignment. It is a data frame with 2 times the number of genes in the rows and d (default= 2) dimensions in the columns * diffRegulation: The results of the differential regulation analysis. It is a data frame with 6 columns as follows: * gene: A character vector with the gene id identified from the manifoldAlignment output. * distance: A numeric vector of the Euclidean distance computed between the coordinates of the same gene in both conditions. * Z: A numeric vector of the Z-scores computed after Box-Cox power transformation. * FC: A numeric vector of the FC computed with respect to the expectation. * p.value: A numeric vector of the p-values associated to the fold-changes, probabilities are asigned as P[X > x] using the Chi-square distribution with one degree of freedom. * p.adj: A numeric vector of adjusted p-values using Benjamini & Hochberg (1995) FDR correction.

The Texas A & M University System. All rights reserved.

Owner

  • Name: Cai Lab at Texas A&M (cailab-tamu)
  • Login: cailab-tamu
  • Kind: organization
  • Location: College Station, Texas, USA

Single-Cell Biology / Machine Learning / Quantum Computing

GitHub Events

Total
  • Issues event: 4
  • Watch event: 20
  • Issue comment event: 5
  • Push event: 2
  • Pull request event: 1
Last Year
  • Issues event: 4
  • Watch event: 20
  • Issue comment event: 5
  • Push event: 2
  • Pull request event: 1

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 270
  • Total Committers: 5
  • Avg Commits per committer: 54.0
  • Development Distribution Score (DDS): 0.115
Top Committers
Name Email Commits
Daniel Osorio d****h@t****u 239
James Cai j****i@g****t 18
Daniel Osorio d****o@u****m 10
Yan Zhong 4****7@u****m 2
Qxu 5****9@u****m 1
Committer Domains (Top 20 + Academic)
genomezoo.net: 1 tamu.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 27
  • Total pull requests: 2
  • Average time to close issues: 4 days
  • Average time to close pull requests: 2 minutes
  • Total issue authors: 21
  • Total pull request authors: 2
  • Average comments per issue: 1.48
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 1
  • Average time to close issues: 3 days
  • Average time to close pull requests: N/A
  • Issue authors: 3
  • Pull request authors: 1
  • Average comments per issue: 0.67
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Leonrunning (4)
  • Rohit-Satyam (2)
  • jamesjcai (2)
  • seekning (2)
  • abedkurdi (2)
  • gdk43 (1)
  • vfriver (1)
  • mgr98 (1)
  • charlesgwellem (1)
  • dmsalsgh97 (1)
  • Prakrithi-P (1)
  • lagom2728 (1)
  • 2124sa (1)
  • qotjrud3 (1)
  • VikArz02 (1)
Pull Request Authors
  • qianxu05172019 (1)
  • Richard0901 (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 235 last-month
  • Total docker downloads: 22,110
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: scTenifoldKnk

In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 235 Last month
  • Docker Downloads: 22,110
Rankings
Stargazers count: 11.0%
Forks count: 11.3%
Dependent packages count: 29.8%
Average: 34.0%
Dependent repos count: 35.5%
Downloads: 82.4%
Maintainers (1)
dcosorioh@tamu.edu
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • MASS * imports
  • Matrix * imports
  • methods * imports
  • pbapply * imports
  • scTenifoldNet * imports
  • stats * imports
  • utils * imports
  • testthat >= 2.1.0 suggests