Mds 3.0 Caa Script Examples
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
Mds 3.0 Caa Script Examples
One of the key applications in single-cell transcriptomics is to determine cell subpopulations based on cell clustering or classification [129, 130]. Due to the high level of noise in the scRNA-seq data, applying dimensionality reduction approaches to scRNA-seq matrix data may facilitate cell clustering. Whilst PCA is commonly used for bulk RNA-seq, the true biological variability of gene expression among cell subpopulations may not be readily distinguished by a small number of PCs. To better account for this variation, NMF was adapted to disentangle subpopulations in single-cell transcriptome data [118, 131], and has been shown to outperform PCA with greater accuracy and robustness (Fig. 1). Likewise, SinNLRR was developed to provide robust clustering of gene expression subspace by non-negative and low-rank representation .
Typical strategies and representative methods for annotating cell subpopulations identified by scRNA-seq. In addition to manual annotation, which is potentially time-consuming and subjective, automated cell type annotation can be mainly sorted into three categories: marker gene-based, reference transcriptome-based, and supervised machine learning-based approaches. The example approach names are listed in the plot. scRNA-seq single-cell RNA sequencing, scCATCH single-cell cluster-based automatic annotation toolkit for cellular heterogeneity, SCINA semi-supervised category identification and assignment, CHETAH characterization of cell types aided by hierarchical classification, SingleR single cell recognition, OnClass ontology-based single cell classification, ACTINN automated cell type identification using neural networks
To facilitate the interpretation and organization of marker genes identified in each cell type, functional enrichment analysis is commonly performed. Computational methods developed for bulk transcriptomics can be easily applied to this analysis, such as Database for Annotation, Visualization, and Integrated Discovery (DAVID) . This kind of analysis requires a hard cutoff on statistical significance to define the marker genes; in contrast, the widely-used gene set enrichment analysis (GSEA) is a cutoff-free approach [180, 181]. GSEA begins with ordering genes based on differential expression statistics between cell populations of interest, followed by statistically assessing if a functionally meaningful gene set or pathway is significantly overrepresented toward the top or bottom of the ranked list. To facilitate GSEA analysis, Molecular Signatures Database (MSigDB) provides a series of annotated gene sets, including pathways and hallmark gene signatures .
In addition to the cell-to-cell heterogeneity that can be captured by scRNA-seq, the dynamics of transcriptomes may also reflect the developmental trajectory or cell state transitions. Trajectory inference , pseudo-time estimation , and RNA velocity modeling  are all helpful to reveal molecular characteristics and regulatory mechanisms during cell differentiation or activation.
An alternative way to capture transcriptome dynamics is to use RNA velocity, which is based on the relationship between matured and unmatured transcripts (i.e., with unspliced introns) in the same cell. If there are relatively more unspliced transcripts in a cell, the gene is under upregulation, and vice versa. Jointly quantifying the ratio between matured and unmatured transcripts, and the gene expression changes during status changes, the direction of cell transition can be thus determined . This rationale has been realized in the first RNA velocity method Velocyto , and improved in the follow-up method scVelo, where a likelihood-based dynamical model was adopted . Furthermore, recently developed methods [212, 213] have combined RNA velocity with trajectory inference, resulting in directed trajectory inference independent of prior knowledge. For instance, CellRank takes advantage of both the robustness of trajectory inference and the directional information from RNA velocity, enabling the detection of previously unknown trajectories and cell states . CellPath is another method integrating single-cell gene expression dynamics and RNA velocity information for trajectory inference .
Different strategies and approaches developed for regulon inference and TF activity prediction with scRNA-seq. To achieve regulon and TF activity prediction, the TF databases and TF-target databases are important resources, and the computational strategies include co-expression gene module identification, dynamic and stochastic modeling of TF versus target expression changes, and application of machine learning approaches. TF transcription factor, scRNA-seq single-cell RNA sequencing, AnimalTFDB Animal Transcription Factor DataBase, Cistrome DB Cistrome Data Browser, WGCNA weighted gene co-expression network analysis, SCENIC single cell regulatory network information and clustering, TRRUST transcriptional regulatory relationships unravelled by sentence-based text-mining
Despite many methods developed for gene regulation analysis based on scRNA-seq, a rigorous judgment on the inferred results needs to be made, due to the complexity of transcriptional regulation and the insufficient information provided by scRNA-seq data. Performing validation experiments may make the inferred results more solid [253, 254].
Metabolism is at the core of all biological processes, and metabolic dysregulation is a hallmark of many diseases including cancer, diabetes, and cardiovascular disease . Although single-cell metabolomics technologies are under rapid development, they are now too premature for large-scale applications . Instead, metabolic analysis based on single-cell transcriptomics is a promising alternative approach. For example, researchers may use scRNA-seq to monitor the gene expression changes of key metabolic genes under different treatments  or during important physiological/pathological processes .
Focusing on single-cell transcriptomics, we have reviewed almost all respects of typical analysis of scRNA-seq data, ranging from QC, basic data processing, to high-level analysis including trajectory inference, CCC estimation and metabolic analysis. To facilitate researchers conducting the analysis on their data, we have constructed an online software/script repertoire for these analysis steps, and will keep it updated to cover more research scenarios. We also offer a step-by-step command line interface (CLI) for wrapping up the R and Python scripts for scRNA-seq analysis. The step-wise commands can be flexibly combined and tailored for specific applications due to the diversity on scientific questions and experimental design. Moreover, incorporating cutting-edge technologies, the analysis steps reviewed above may not cover every specifically required task. Indeed, additional analysis pipeline ( -NJMU/scPolylox) was necessary to process the scRNA-seq data for identifying Polylox transcript variants in lineage tracing .
Previous research has classified the downstream scRNA-seq data analysis methods into cell-level and gene-level analysis , which is intuitive and helpful for understanding. While cell-level analysis is typically concerned with the cell composition of given tissues or samples, gene-level analysis focuses on gene expression differences and heterogeneity. As a result, cell clustering for subpopulation identification, trajectory analysis, and CCC inference are examples of cell-level analysis, whereas differential expression, functional enrichment analysis, regulon inference, and metabolic flux analysis are primarily concerned with gene-level information. In contrast to bulk RNA-seq, single-cell RNA-seq allows for cell-level analysis with unprecedented accuracy and throughput, which in turn inspires a few types of gene-level analysis, such as marker gene identification and gene expression dynamics along inferred trajectories.
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