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scisr

https://github.com/duct317/scisr

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: nature.com
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: duct317
  • Language: R
  • Default Branch: master
  • Size: 3.22 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 2
  • Open Issues: 1
  • Releases: 0
Created over 5 years ago · Last pushed about 4 years ago
Metadata Files
Readme

README.md

scISR: Single-cell Imputation using Subspace Regression

scISR performs imputation for single-cell sequencing data. scISR identifies the true dropout values in the scRNA-seq dataset using hyper-geomtric testing approach. Based on the result obtained from hyper-geometric testing, the original dataset is segregated into two including training data and imputable data. Next, training data is used for constructing a generalize linear regression model that is used for imputation on the imputable data. The package is now available on CRAN.

How to install

  • The package can be installed from CRAN or this repository.
  • Using CRAN: install.packages("scISR")
  • Using devtools:
    • Install devtools: utils::install.packages('devtools')
    • Install the package using: devtools::install_github('duct317/scISR')
      # Example
      ## Load the Goolam dataset and perform imputation
  • Load the package: library(scISR)
  • Load Goolam dataset: data('Goolam'); raw <- Goolam$data; label <- Goolam$label
  • Perform the imputation: imputed <- scISR(data = raw)
    ## Result assessment
  • Perform PCA and k-means clustering on raw data: R library(irlba) library(mclust) set.seed(1) # Filter genes that have only zeros from raw data raw_filer <- raw[rowSums(raw != 0) > 0, ] pca_raw <- irlba::prcomp_irlba(t(raw_filer), n = 50)$x cluster_raw <- kmeans(pca_raw, length(unique(label)), nstart = 2000, iter.max = 2000)$cluster print(paste('ARI of clusters using raw data:', round(adjustedRandIndex(cluster_raw, label),3)))
  • Perform PCA and k-means clustering on imputed data: R set.seed(1) pca_imputed <- irlba::prcomp_irlba(t(imputed), n = 50)$x cluster_imputed <- kmeans(pca_imputed, length(unique(label)), nstart = 2000, iter.max = 2000)$cluster print(paste('ARI of clusters using imputed data:', round(adjustedRandIndex(cluster_imputed, label),3))) # Citation: Duc Tran, Bang Tran, Hung Nguyen, Tin Nguyen (2022). A novel method for single-cell data imputation using subspace regression. Scientific Reports, 12, 2697. doi: 10.1038/s41598-022-06500-4 (link)

Owner

  • Name: Duc Tran
  • Login: duct317
  • Kind: user

GitHub Events

Total
Last Year

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 14
  • Total Committers: 2
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.071
Top Committers
Name Email Commits
Duc d****t@n****u 13
Duc Tran d****7@g****m 1
Committer Domains (Top 20 + Academic)
nevada.unr.edu: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 3
  • Total pull requests: 0
  • Average time to close issues: 4 days
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 2.0
  • 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
View more stats
Top Authors
Issue Authors
  • Rohit-Satyam (2)
  • inoue0426 (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 208 last-month
  • Total docker downloads: 48
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: scISR

Single-Cell Imputation using Subspace Regression

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 208 Last month
  • Docker Downloads: 48
Rankings
Forks count: 21.9%
Stargazers count: 28.5%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 41.0%
Downloads: 89.1%
Maintainers (1)
duct@nevada.unr.edu
Last synced: 11 months ago

Dependencies

DESCRIPTION cran
  • R >= 3.4 depends
  • PINSPlus * imports
  • cluster * imports
  • entropy * imports
  • irlba * imports
  • markdown * imports
  • matrixStats * imports
  • parallel * imports
  • stats * imports
  • utils * imports
  • knitr * suggests
  • mclust * suggests
  • testthat * suggests