PT  - JOURNAL ARTICLE
AU  - Aanchal Mongia
AU  - Debarka Sengupta
AU  - Angshul Majumdar
TI  - deepMc: deep Matrix Completion for imputation of single cell RNA-seq data
AID  - 10.1101/387621
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 387621
4099  - http://biorxiv.org/content/early/2018/08/09/387621.short
4100  - http://biorxiv.org/content/early/2018/08/09/387621.full
AB  - Single cell RNA-seq has fueled discovery and innovation in medicine over the past few years and is useful for studying cellular responses at individual cell resolution. But, due to paucity of starting RNA, the data acquired is highly sparse. To address this, We propose a deep matrix factorization based method, deepMc, to impute missing values in gene-expression data. For the deep architecture of our approach, We draw our motivation from great success of deep learning in solving various Machine learning problems. In this work, We support our method with positive results on several evaluation metrics like clustering of cell populations, differential expression analysis and cell type separability.