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. 2024 Jul 16;15:5983. doi: 10.1038/s41467-024-50194-3

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — a A graphical overview highlights crucial analysis modules: cell type annotation (pySCSA), cellular interactions (CellPhoneDB), trajectory inference (pyVIA), pathway analysis (AUCell), and drug response prediction (scDrug). b An example code snippet illustrates the process for loading data and conducting analyses using pySCSA, CellPhoneDB, pyVIA, AUCell, and SEACells, with the inclusion of continuous covariates. c UMAP plot visualizes single-cell RNA sequencing (scRNA-seq) data from colorectal cancer (CRC) patients. The plot contrasts manual cell type annotations, shown in the left panel, with automatic annotations depicted in the right panel. d The concordance between manual and pySCSA-generated annotations is presented in a row-normalized confusion matrix. e Pathway enrichment within CRC cells is elucidated in a UMAP visualization, with the left side indicating B cell receptor signaling and the right side detailing T cell receptor signaling, as analyzed by AUCell. f Metacell composition within the CRC dataset is revealed in a UMAP plot. g Epithelial cell subpopulations in CRC are displayed in a UMAP plot; automated annotations by pySCSA are demonstrated on the left, complemented by a cell state transition directed graph derived from a Partition-based Graph Abstraction (PAGA) trajectory on the right. h CellPhoneDB computes an interaction network between CRC cell types, offering insights into intercellular communication. i Scaled mean expression levels of genes that code for interacting ligand-receptor proteins, identified by CellPhoneDB, are shown in dot plots to underscore the supporting interactions between immune and epithelial cells.