RT Journal Article
SR Electronic
T1 DECODE-ing sparsity patterns in single-cell RNA-seq
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 241646
DO 10.1101/241646
A1 Shahin Mohammadi
A1 Jose Davila-Velderrain
A1 Manolis Kellis
A1 Ananth Grama
YR 2018
UL http://biorxiv.org/content/early/2018/03/09/241646.abstract
AB An inherent challenge in interpreting single-cell transcriptomic data is the high frequency of zero values. This phenomenon has been attributed to both biological and technical sources, although the extent of the contribution of each remains unclear. Here, we show that the underlying gene presence/absence sparsity patterns are by themselves highly informative. We develop an algorithm, called DECODE, to assess the extent of joint presence/absence of genes across different cells, and to infer a gene dependency network. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. technical zeros, by exploiting each gene’s local network neighborhood. For inferred non-biological zeros, we build a predictive model that imputes the missing value of each gene based on activity patterns of its most informative neighbors. We show that our framework accurately infers gene-gene functional dependencies, pinpoints technical zeros, and predicts biologically-meaningful missing values in three diverse datasets.