Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms

Author:

Tisch Adam1,Madapoosi Siddharth2ORCID,Blough Stephen1,Rosa Jan1,Eddy Sean3,Mariani Laura3,Naik Abhijit3,Limonte Christine4,Mccown Philip3,Menon Rajasree5,Rosas Sylvia6,Parikh Chirag7,Kretzler Matthias3,Mahfouz Ahmed8,Alakwaa Fadhl3ORCID

Affiliation:

1. University of Michigan

2. University of Michigan Medical School

3. Michigan Medicine: University of Michigan Michigan Medicine

4. University of Washington

5. University of Michigan Department of Computational Medicine and Bioinformatics

6. Joslin Diabetes Center

7. Johns Hopkins School of Medicine: The Johns Hopkins University School of Medicine

8. Leiden University Medical Center: Leids Universitair Medisch Centrum

Abstract

Abstract

Background 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 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.

Publisher

Research Square Platform LLC

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