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This is the development version of ccImpute; for the stable release version, see ccImpute.

ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (

Bioconductor version: Development (3.20)

Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.

Author: Marcin Malec [cre, aut] , Parichit Sharma [aut], Hasan Kurban [aut], Mehmet Dalklic [aut]

Maintainer: Marcin Malec <mamalec at>

Citation (from within R, enter citation("ccImpute")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

ccImpute package manual HTML R Script
Reference Manual PDF


biocViews Clustering, DimensionReduction, PrincipalComponent, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version 1.7.1
In Bioconductor since BioC 3.16 (R-4.2) (1.5 years)
License GPL-3
Imports Rcpp, sparseMatrixStats, stats, BiocParallel, irlba, SingleCellExperiment, Matrix, SummarizedExperiment
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Suggests knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, mclust, testthat (>= 3.0.0), splatter
Linking To Rcpp, RcppEigen
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Source Package ccImpute_1.7.1.tar.gz
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macOS Binary (arm64) ccImpute_1.7.0.tgz
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