An accurate and robust imputation method scImpute for single-cell RNA-seq data

By Wei Vivian Li, Jingyi Jessica Li. Nature Communications, 2018

646 citations recorded by the source corpus.

The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics. Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/48caa94dd92179434053e2fb4387cada86d797ea

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Citations in the corpus

Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “Biological process labels are stored in databases such as MSigDB (Liberzon et al , 2011), the Gene Ontology (Ashburner et al , 2000; The Gene Ontology Consortium, 2017), or the pathway databases KEGG (Kanehisa et al , 2017) and Reactome (Fabregat et al , 2018).”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Several imputation methods have been recently developed for scRNA-seq, including SAVER (Huang et al., 2018), MAGIC (van Dijk et al., 2018), ScImpute (Li and Li, 2018), DrImpute (Gong et al., 2018), and AutoImpute (Talwar et al., 2018).”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, neutral): “We treated the total count as one cell’s read depth and defined two total counts indicators: T (‘target’) and S (‘source’), for the total counts of the raw and the input samples, Article https://doi.org/10.1038/s41592-024-02305-7 We then compared scFoundation with imputation methods including MAGIC…”
Single-cell RNA-seq denoising using a deep count autoencoder (2018, methodology): “A number of scRNA-seq speci ﬁ c imputation or denoising methods exist 18 – 22 .”
Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, methodology): “…tools have been developed recently in this regard, including statistical models CIDR (clustering through imputation and dimensionality reduction) 18 , scImpute 19 , MAGIC 20 and SAVER 21 , to name a few, and deep learning approaches DeepImpute 22 , DCA (deep count autoencoder) 23 and scScope 24 .”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, methodology): “…the drop-out events via DrImpute will improve the accuracy of these clustering methods and (2) the performance of the existing tools combined with DrImpute will perform better than existing scRNA-seq imputation tools such as CIDR [30], scImpute [23] and MAGIC [24] in addressing dropout events.”
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions (2019, methodology): “Smoothing sparse data [25 – 27] or imputation of transcriptional states [25, 28 – 30] was proposed as a possible pre-process for modeling similarity in the data.”
SAVER: Gene expression recovery for single-cell RNA sequencing (2018, neutral): “6, 7a).”
Challenges in unsupervised clustering of single-cell RNA-seq data (2019, methodology): “There are several statistical methods available for imputing zeros 25,47,48 , but they all rely on pre existing cell–cell or gene–gene correlations in the data to infer the appropriate imputed value.”