McImpute: Matrix completion based imputation for single cell RNA-seq data

Abstract

Motivation: Single cell RNA sequencing has been proved to be revolutionary for its potential of zooming into complex biological systems. Genome wide expression analysis at single cell resolution, provides a window into dynamics of cellular phenotypes. This facilitates characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on development or emergence of specific cell populations and phenotypes. However, owing to the paucity of input RNA, a typical single cell RNA sequencing data features a high number of dropout events where transcripts fail to get amplified. Results:We introduce mcImpute, a low-rank matrix completion based technique to impute dropouts in single cell expression data. On a number of real datasets, application of mcImpute yields significant improvements in separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability and performance of dimensionality reduction techniques for cell visualization. Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq 7 cell type and understanding the dynamics of transcriptional changes during 8 development [1, 33, 40].9Despite all the goodness, scRNA-seq technologies suffer from a number of sources of 10 technical noise. Most important of these is insufficient input RNA. Due to small 11 quantities transcripts are frequently missed during the reverse transcription step. As a 12 direct consequence, these transcripts are not detected during the sequencing step [12]. 13Often times the lowly expressed genes are the worst hit. Excluding these genes from 14 analysis may not be the best solution as many of the transcription factors and cell 15 surface markers are sacrificed in this process [38]. Added to that, variability in dropout 16 rate across individual cells or cell types, works as a confounding factor for a number of 17 July 5, 2018 1/15 downstream analyses [18, 30]. Hicks and colleagues [8] showed, on a number of 18 scRNA-seq datasets, that the first principal components highly correlate with 19 proportion of dropouts across individual transcriptomes. In summary, there is a 20 standing need for efficient methods to impute scRNA-seq datasets. 21 Very recently, efforts have been made to devise imputation techniques for scRNA-seq 22 data. Most notable of among these are MAGIC [38], scImpute [19] and drImpute [15]. 23 MAGIC uses a neighborhood based heuristic to infer the missing values based on the 24 idea of heat diffusion, altering all gene expression levels including the ones not affected 25 by dropouts. On the other hand, scImpute first estimates which values are affected by 26dropouts based on Gamma-Normal mixture model and then fills the dropout values in a 27 cell by borrowing information of the same gene in other similar cells, which are selected 28 based on the genes unlikely affected by dropout events. Overall performance of 29 scImpute has been shown to to be superior to MAGIC. Parametric modeling of single 30...

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“…The above problem can be solved alternatively, by invoking majorization-minimization arguments [51] to deal with the mask operator M and by applying thresholding operations on the singular values to process the nuclear norm term [41].…”