  Cell type identification from single-cell transcriptomic data is a common
goal of single-cell RNA sequencing (scRNAseq) data analysis. Neural networks
have been employed to identify cell types from scRNAseq data with high
performance. However, it requires a large mount of individual cells with
accurate and unbiased annotated types to build the identification models.
Unfortunately, labeling the scRNAseq data is cumbersome and time-consuming as
it involves manual inspection of marker genes. To overcome this challenge, we
propose a semi-supervised learning model to use unlabeled scRNAseq cells and
limited amount of labeled scRNAseq cells to implement cell identification.
Firstly, we transform the scRNAseq cells to "gene sentences", which is inspired
by similarities between natural language system and gene system. Then genes in
these sentences are represented as gene embeddings to reduce data sparsity.
With these embeddings, we implement a semi-supervised learning model based on
recurrent convolutional neural networks (RCNN), which includes a shared
network, a supervised network and an unsupervised network. The proposed model
is evaluated on macosko2015, a large scale single-cell transcriptomic dataset
with ground truth of individual cell types. It is observed that the proposed
model is able to achieve encouraging performance by learning on very limited
amount of labeled scRNAseq cells together with a large number of unlabeled
scRNAseq cells.
