Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning.

ABSTRACT:

Motivation

To develop and assess the accuracy of deep learning models that identify different retinal cell types, as well as different retinal ganglion cell (RGC) subtypes, based on patterns of single-cell RNA sequencing (scRNA-seq) in multiple datasets.

Results

Deep domain adaptation models were developed and tested using three different datasets. The first dataset included 44 808 single retinal cells from mice (39 cell types) with 24 658 genes, the second dataset included 6225 single RGCs from mice (41 subtypes) with 13 616 genes and the third dataset included 35 699 single RGCs from mice (45 subtypes) with 18 222 genes. We used four loss functions in the learning process to align the source and target distributions, reduce misclassification errors and maximize robustness.

SUBMITTER: Madadi Y 

PROVIDER: S-EPMC9991888 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature