LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection.

ABSTRACT:

Motivation

Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type.

Results

We developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models

SUBMITTER: Johnson TS 

PROVIDER: S-EPMC6853662 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature