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 the characterization of transcriptional heterogeneity in normal and diseased tissues under various conditions. It also sheds light on the 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 the separation of true zeros from dropouts, cell-clustering, differential expression analysis, cell type separability, the performance of dimensionality reduction techniques for cell visualization, and gene distribution.Availability and Implementation: https://github.com/aanchalMongia/McImpute_scRNAseq

Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture

“…CRIP [ 38 ] predicts the RBP binding sites on circRNA by combining a convolution neural network (CNN) and a recurrent neural network (RNN). Different from CRIP, circSLNN [ 40 ] converts the prediction of binding sites on RNAs to a sequence labelling problem and classifies using a conditional random field (CRF) layer instead of a fully connected layer (FC). In this study, we compared our model with CRIP and circSLNN on seven RBP datasets with 5-fold cross validation.…”

“…CRIP [ 38 ] and CSCRSites [ 39 ] employed different deep learning frameworks to identify the binding sites within circRNAs. CircSLNN [ 40 ] treats the prediction task of RBP binding sites as a sequence labelling problem to identify RBP binding sites on circRNAs. CRIP and CSCRsites accept a fixed-length circRNA segment, and noisy nucleotides may be generated that affect the outcome of the prediction.…”

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

“…• Basic frameworks (MF: Matrix factorization [41] and MC: Matrix completion or Nuclear norm minimization [41])…”

“…The above problem is solved in an alternating manner, by first decoupling the mask using a majorization-minimization technique [51,14] and then using alternating least squares method [25] to obtain U and V . The complete algorithm is described in [41].…”

“…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].…”