Package 'scISR'

Title: 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>).
Authors: Duc Tran [aut, cre], Bang Tran [aut], Hung Nguyen [aut], Tin Nguyen [fnd]
Maintainer: Duc Tran <[email protected]>
License: LGPL
Version: 0.1.1
Built: 2024-11-17 03:54:52 UTC
Source: https://github.com/duct317/scisr

Help Index

Goolam

Description

Goolam dataset with data and cell types information.The number of genes is reduced to 10,000.

Usage

Goolam

Format

An object of class list of length 2.

scISR: Single-cell Imputation using Subspace Regression

Description

Perform single-cell Imputation using Subspace Regression

Usage

scISR(
  data,
  ncores = 1,
  force_impute = FALSE,
  do_fast = TRUE,
  preprocessing = TRUE,
  batch_impute = FALSE,
  seed = 1
)

Arguments

data

Input matrix or data frame. Rows represent genes while columns represent samples
ncores

Number of cores that the algorithm should use. Default value is 1.
force_impute

Always perform imputation.
do_fast

Use fast imputation implementation.
preprocessing

Perform preprocessing on original data to filter out low quality features.
batch_impute

Perform imputation in batches to reduce memory consumption.
seed

Seed for reproducibility. Default value is 1.

Details

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 subsets 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.

Value

scISR returns an imputed single-cell expression matrix where rows represent genes while columns represent samples.

Examples

{
# Load the package
library(scISR)
# Load Goolam dataset
data('Goolam');
# Use only 500 random genes for example
set.seed(1)
raw <- Goolam$data[sample(seq_len(nrow(Goolam$data)), 500), ]
label <- Goolam$label

# Perform the imputation
imputed <- scISR(data = raw)

if(requireNamespace('mclust'))
{
  library(mclust)
  # Perform PCA and k-means clustering on raw data
  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)))
}
}