Comprehensive analysis of multi-omics single-cell data using the single-cell analyst

Abstract

The rapid advancement of multi-omics single-cell technologies has significantly enhanced our ability to investigate complex biological systems at unprecedented resolution. However, many existing analysis tools are complex, requiring substantial coding expertize, which can be a barrier for computationally less competent researchers. To address this challenge, we present single-cell analyst, a user-friendly, web-based platform to facilitate comprehensive multi-omics analysis. Single-cell analyst supports a wide range of data types, including six single-cell omics: single-cell RNA sequencing (scRNA-sequencing), single-cell assay for transposase accessible chromatin sequencing (scATAC-seq sequencing), single-cell immune profiling (scImmune profiling), single-cell copy number variation, cytometry by time-of-flight, and flow cytometry and spatial transcriptomics, and enables researchers to perform integrated analyses without requiring programming skills. The platform offers both online and offline modes, providing flexibility for various use cases. It automates critical analysis steps, such as quality control, data processing, and phenotype-specific analyses, while also offering interactive, publication-ready visualizations. With over 20 interactive tools for intermediate analysis, single cell analyst simplifies workflows and significantly reduces the learning curve typically associated with similar platforms. This robust tool accommodates datasets of varying sizes, completing analyses within minutes to hours depending on the data volume, and ensures efficient use of computational resources. By democratizing the complex process of multi-omics analysis, single-cell analyst serves as an accessible, all-encompassing solution for researchers of diverse technical backgrounds. The platform is freely accessible at www.singlecellanalyst.org.

Keywords: multi‐omics; single‐cell sequencing; web server.

Figure 1

An overview of the analysis frameworks on the single cell analyst web server. Seven frameworks are available on the platform for users to carry out single‐cell multimodal analyses. Analyses for the omics types on the first row are mostly catered for 10× Genomics data formats. The analysis also supports data from other technologies as long as the stated data formats are provided. The first set of the framework included quality control (QC) steps, with omics‐specific QC checks such as automated gating for flow cytometry data. This will be followed by a general analysis framework, including post‐QC dimensionality reduction, clustering, DE analysis, and the prediction of cell types in each cluster. Next, omics‐specific analyses will be performed. Owing to the different natures of the data types from each omics data set, additional analyses will be carried out to meet omics‐specific analysis requirements. For example, pseudotime analyses will be carried out during an analysis of single‐cell RNA sequencing (scRNA‐seq) data. Clonal homeostasis will be carried out for single‐cell immune (scImmune Profiling) data to assess the spatial occupancies of clones across samples. BACH2, BTB and CNC Homology 2; CD56, cluster of differentiation 56; CT, cell type; CyTOF, cytometry by time‐of‐flight; DE analysis, differential expression analysis; FSC‐A, forward scatter area; MDS, multidimensional scaling; QC check, quality control check; scRNA‐seq, single‐cell RNA sequencing; scATAC‐seq, single‐cell assay for transposase accessible chromatin sequencing; scImmune profiling, single cell immune profiling; scCNV, single‐cell copy number variation; TSS, transcription start site; TF, transcription factor; TF footprinting, transcription factor footprinting; UMAP, uniform manifold approximation and projection.

Figure 2

Overview of the single cell analyst database. Key features of the single cell analyst database, covering multi‐omics phenotypic features of over 100 adult and fetal tissues. RNA‐seq (GTEx), RNA sequencing from genotype‐tissue expression project; tSNE, t‐distributed stochastic neighbor embedding; CDR3, complementarity‐determining region 3.

Figure 3

Analysis workflow for the scRNA‐seq framework. Key steps in the workflow for scRNA‐seq analysis on the multi‐omics server. DC, dendritic cells; DEGs, differentially Expressed Genes; DisGeNET, disease gene network; GSEA, gene set enrichment analysis; PCA, principal component analysis.

Figure 4

Analysis workflow for the scATAC‐seq framework. Key steps in the workflow for single‐cell assay for transposase‐accessible chromatin using sequencing (scATAC‐seq) analysis on the multi‐omics server. Chr, chromosome; DE peaks, differential accessible peaks.

Figure 5

Analysis workflow for the scImmune Profiling framework. Key steps in the workflow for scImmune Profiling analysis on the multi‐omics server. NK, natural killer cells; VDJC, variable, diversity, joining, and constant gene regions.

Figure 6

Analysis workflow for the single‐cell copy number variations (scCNV) framework. Key steps in the workflow for scCNV analysis on the multi‐omics server. DAPC, discriminant analysis of principal components.