Description
Single-Cell Imputation using Subspace Regression.
Description
Provides an imputation pipeline for single-cell RNA sequencing data. The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).
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.
How to install
- The package can be installed from this repository.
- 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:
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:
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)