Journal
GENOME BIOLOGY
Volume 18, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-017-1188-0
Keywords
Single-cell; scRNA-seq; Dropout; Imputation; Dimensionality reduction; Clustering; Cell type
Funding
- New South Wales Ministry of Health
- Human Frontier Science Program [RGY0084/2014]
- National Health and Medical Research Council of Australia [1105271]
- National Heart Foundation of Australia
- Amazon Web Services (AWS) Cloud Credits for Research
- National Health and Medical Research Council of Australia [1105271] Funding Source: NHMRC
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Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, andRaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR.
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