A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

By Aaron T. L. Lun, Davis J. McCarthy, John C. Marioni. F1000Research, 2016

1,565 citations recorded by the source corpus.

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d2ace21f80a8ac89a07159247762623a1738cd55

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Citations in the corpus

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches (2022, positive): “The workflow to identify the outliers, based on 3 metrics (library size, number of expressed genes and mitochondrial proportion) described by the Marioni lab (Lun et al., 2016) was followed.”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, neutral): “In each of the 25 simulation iterations, we draw an equal sample size of 300 cells per group and test for di erential expression using limma-trend [41] on log2 CPM values with scran [42] library size correction.”
A benchmark of batch-effect correction methods for single-cell RNA sequencing data (2020, methodology): “Based on the examples provided by fastMNN authors, 5000 HVGs were identified and used as input for projection into the cosine space, followed by multi-sample PCA dimension reduction using the multiBatchPCA function from the Scran package [28] to obtain 50 principal components.”
Differential abundance testing on single-cell data using K-nearest neighbour graphs (2021, methodology): “Namely, dimensionality reduction with PCA was performed on the 3,000 top HVGs, calculated using modelGeneVar and getTopHVGs from the R package scran 48 , and the top 11 PCs were retained for KNN graph building and UMAP embedding.”
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (2021, background): “ArchR additionally supports scran 51 for single-cell clustering.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “…revolve around the above platforms (R and bioconductor tools: https://github.com/drisso/bioc2016singlecell and https://hemberg-lab.github.io/scRNA.seq.course/; Lun et al , 2016b; Seurat: https://satijalab.org/seurat/get_started.html; Scanpy: https://scanpy.readthedocs.io/en/stable/tutorials.html).”
MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data (2020, methodology): “Gene expression counts were normalized using scran [61], and they were modelled in MOFA with a Gaussian likelihood.”
Using clusterProfiler to characterize multiomics data (2024, neutral): “For these upstream analyses, users can also consider using scater, scuttle and scran from Bioconductor 57,58 or scanpy in Python 59 .”
SCANPY: large-scale single-cell gene expression data analysis (2018, methodology): “Simple integrated analysis work flows for single-cell transcriptomic data [1] have been enabled by frameworks such as S EURAT [2], M ONOCLE [3], SCDE/P AGODA [4], MAST [5], C ELL R ANGER [6], S CATER [7], and S CRAN [8].”