Identification of new marker genes from plant single‐cell RNA‐seq data using interpretable machine learning methods

Abstract

Summary An essential step in the analysis of single‐cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single‐cell predictive marker (SPmarker) to identify novel cell‐type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single‐cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non‐Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell‐type marker genes from scRNA‐seq data and pave the way for cross‐species mapping of scRNA‐seq data in plants.

Reference genes for quantitative Arabidopsis single molecule RNA fluorescencein situhybridization

“…To overcome this, we recommend use of multiplexed probe sets to simultaneously label cell-type specific RNA. Machine learning algorithms applied to single cell transcriptome datasets have recently expanded the pool of suitable gene candidates ( Yan et al , 2022 ).…”

Gene Regulatory Network topology mediates the role of diffusible components in cellular patterns: the root epidermis of Arabidopsis thaliana as a study system

“…On the first point, there are several recent approaches that have described the expression profiles of root cells and have tried to establish the developmental trajectories of the different cell types at a single-cell resolution not only for WT but also for some mutants and ambiental conditions [17,26,34,74,90,101,132] and some of these approaches even take advantage of machine-learning methods to generate better quality data to identify cell-type marker genes from scRNA-seq data [130]. This approach would allow us to extend our GRN with key regulators that were not incorporated in our model by reconstructing regulatory networks based on massive data [129,131] and to compare previously proposed conserved loops and incorporate new ones that are able to describe the dynamic behavior of epidermal differentiation in different developmental contexts.…”

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning

“…The analysis of genomics data might be powered by artificial intelligence (AI) techniques, which hold the mathematical power to untangle the intricate relationship between data points in higher-dimensional settings [ 9 ]. Examples of AI-assisted medicine can be found in the identification of marker genes [ 10 , 11 ], biomarkers [ 12 , 13 ], vaccine candidates [ 14 , 15 ], and potential targets for therapeutics [ 16 ]. Moreover, AI has been used in conjunction with scRNA-seq data analysis for the identification of tumor cells [ 17 ].…”