Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms

Abstract

Introduction: Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation.

Results: We analyzed publicly available sc/snRNA-seq datasets from five expert-annotated studies, comprising 62,120 cells from 79 kidney biopsy samples. Datasets were integrated by harmonizing cell type annotations across studies. Five different supervised machine learning algorithms (support vector machines, random forests, multilayer perceptrons, k-nearest neighbors, and extreme gradient boosting) were applied to automatically annotate cell types using four training datasets and one testing dataset. Performance metrics, including accuracy (F1 score) and rejection rates, were evaluated. All five machine learning algorithms demonstrated high accuracies, with a median F1 score of 0.94 and a median rejection rate of 1.8 %. The algorithms performed equally well across different datasets and successfully rejected cell types that were not present in the training data. However, F1 scores were lower when models trained primarily on scRNA-seq data were tested on snRNA-seq data.

Conclusions: Despite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.

Keywords: Annotation; Cell identity; Classification; Kidney; Machine learning; RNA-seq.

Fig. 1

Heatmap of correlations between cell types across all 5 cohorts per the authors' original annotations. Axes are a symmetrical layout of annotations and annotations are grouped according to dendrogram and boxed by harmonized cell types, which are defined as groups of annotations with a high degree of correlation.

Fig. 2

Heatmaps demonstrating for each testing dataset, the performances of each classification algorithm as defined by (a) median F1 score or (b) median percent of cells classified as unknowns. (c) Heatmap of each classification algorithm's rejection rate on when Young et al. was used as the testing dataset.

Fig. 3

Heatmap of each classifier's F1 score on (a) Menon et al., (b) Lake et al., (c) Wu et al., and (d) Young et al. with respect to each harmonized cell type.