  Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure
the genome-wide transcriptome of many individual cells in parallel, but results
in noisy data with many dropout events. Existing methods to learn molecular
signatures from bulk transcriptomic data may therefore not be adapted to
scRNA-seq data, in order to automatically classify individual cells into
predefined classes. We propose a new method called DropLasso to learn a
molecular signature from scRNA-seq data. DropLasso extends the dropout
regularisation technique, popular in neural network training, to esti- mate
sparse linear models. It is well adapted to data corrupted by dropout noise,
such as scRNA-seq data, and we clarify how it relates to elastic net
regularisation. We provide promising results on simulated and real scRNA-seq
data, suggesting that DropLasso may be better adapted than standard regularisa-
tions to infer molecular signatures from scRNA-seq data.
