Sagane Joye-Dind - Building a reference dataset of single-cell RNA-Seq data
Channel: Biocuration Conference 2023
Date: 2023-06-21

*Title:* Building a reference dataset of single-cell RNA-Seq data for training machine learning algorithms

*Session:* April 25 - Ai And Text Mining 1 - 04

*Abstract:* Single-cell RNA-Seq (scRNA-Seq) data are being massively produced in many conditions and species. They allow the study of hundreds of cell types, in widely different contexts regarding, e.g., anatomical localization, developmental stage, or disease state. The characterization of the cell type of each cell is highly labor intensive and error prone, especially when annotating results from bead-based or nanowell technologies, where the a priori cell type is unknown. This characterization usually involves a clustering of the cells based on their gene expression, the identification of marker genes for each cluster, and manual identification by an expert of the cell type corresponding to these marker genes. Additionally, data accessibility for reanalyzing scRNA-Seq data is often sparse, with e.g. missing barcode information, missing explicit relation between each cell and their annotated cluster, or free text format for cell type annotation. Machine-Learning (ML) methods are being used to annotate single-cell data, to facilitate the characterization of cell types. However, because of the lack of standardization of these data, it is challenging to train and evaluate algorithms for a variety of tissue and species contextes. This project aims at providing a reference dataset in D. melanogaster, and to train and benchmark several ML algorithms thanks to it. It is a collaboration between Bgee (https://bgee.org/), specialized in transcriptomics data annotation, ASAP (https://asap.epfl.ch/), specialized in scRNA-Seq analysis pipeline standardization, and the Robinson Statistical Bioinformatics Group (https://robinsonlabuzh.github.io/), specialized in genomics statistical methods. Several experiments have been re-annotated and standardized: the Fly Cell Atlas, plus all publicly available experiments using the “10x Genomics” technology. Cluster annotations are all standardized using reference ontologies (e.g., Cell Ontology, Uberon), using ontology post-composition methods, as well as the a priori information known before the clustering step. Cell barcode information, and link to their cluster allowing cell type assignment, are checked and integrated. Information for all integrated experiments is released in a common format, as H5AD files. Several systematic challenges have been already identified in metadata: uncertainties about cell type assignment, incorrect cell type assignments, or differences in annotations depending on the clustering method used. We report and correct these errors and uncertainties, in order to provide a gold standard reference dataset of FAIR and annotated scRNA-Seq data. We will present this reference dataset and the lessons learned, to address open questions about, e.g., the validity of using a same ML classifier in different tissues and conditions, or even in different species; or about how to handle cell type assignment uncertainties. This dataset will allow researchers to evaluate and improve their own ML classification methods, and will provide a foundation for defining a common standard for scRNA-Seq FAIR data exchange.