scISR | R Documentation |
Perform single-cell Imputation using Subspace Regression
scISR( data, ncores = 1, force_impute = FALSE, do_fast = TRUE, preprocessing = TRUE, batch_impute = FALSE, seed = 1 )
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 |
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.
scISR
returns an imputed single-cell expression matrix where rows represent genes while columns represent samples.
{ # 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))) } }
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