Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifier

Abstract

Background The advent of single-cell RNA sequencing (scRNA-seq) has provided a high-resolution overview of the cellular heterogeneity of different tissue types. Manual cell type annotation of gene expression datasets remains a useful but time-intensive task. Ensemble machine learning methods leverage the predictive power of multiple classifiers and can be applied to classify high-dimensional gene expression data. Here, we present a novel application of the Subsemble supervised ensemble machine learning classifier used to classify novel cells with known cell type labels using gene expression data. Methods First, we tested the classification performance of different pre-processing steps used to normalize and upsample the training dataset for the Subsemble using a colorectal cancer dataset. Second, we conducted a cross-validated performance benchmark of the Subsemble classifier compared to nine other cell type classification methods across five metrics tested, using an acute myeloid leukemia dataset. Third, we conducted a comparative performance benchmark of the Subsemble classifier using a patient-based leave-one-out cross-validation scheme. Rank normalized scores were calculated for each classifier to aggregate performance across multiple metrics. Results The Subsemble classifier performed best when trained on a dataset that was log-transformed then upsampled to generate balanced class distributions. The Subsemble classifier was consistently the top-ranked classifier across five classification performance metrics compared to the nine other baseline classifiers and showed an improvement in performance as the training dataset increased. When tested using the patient-based leave-one-out cross-validation scheme, the Subsemble was the top-ranked classifier based on rank normalized scores. Conclusions Our proof-of-concept study showed that the Subsemble classifier can be used to accurately predict known cell type labels from single-cell gene expression data. The top-ranked classification performance of the Subsemble across two validation datasets, two cross-validation schemes, and five performance metrics motivates future development of accurate ensemble classifiers of scRNA-seq datasets.