| Type: | Package |
| Title: | Single-Cell Imputation using Subspace Regression |
| Version: | 0.1.1 |
| Maintainer: | Duc Tran <duct@nevada.unr.edu> |
| 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>). |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| Depends: | R (≥ 3.4) |
| Imports: | cluster, entropy, stats, utils, parallel, irlba, PINSPlus, matrixStats, markdown |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Suggests: | testthat, knitr, mclust |
| VignetteBuilder: | knitr |
| URL: | https://github.com/duct317/scISR |
| BugReports: | https://github.com/duct317/scISR/issues |
| Packaged: | 2022-06-28 19:32:08 UTC; dtran |
| Author: | Duc Tran [aut, cre], Bang Tran [aut], Hung Nguyen [aut], Tin Nguyen [fnd] |
| Repository: | CRAN |
| Date/Publication: | 2022-06-30 06:20:08 UTC |
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 |
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 |
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)))
}
}