Orchestrating Single-Cell Analysis with Bioconductor

Carey, Vincent J.; Carpp, Lindsay N.; Lun, Aaron T. L.; Marini, Fédérico; Rue-Albrecht, Kévin; Risso, Davide; Soneson, Charlotte; Pagès, Hervé; Smith, Kenneth G. C.; Huber, Wolfgang; Morgan, Martin; Gottardo, Raphaël; Hicks, Stephanie

doi:10.1101/590562

Cited by 54 publications

(64 citation statements)

References 143 publications

(208 reference statements)

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“…Nuclei were washed and centrifuged in this nuclei wash/resuspension buffer three times, before labeling with DAPI (10μg/mL We processed the sequencing data with the 10x Genomics' Cell Ranger pipeline, aligning to the human reference genome GRCh38, with a reconfigured GTF such that intronic alignments were additionally counted given the nuclear context, to generate UMI/feature-barcode matrices ( https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/advance d/references ). We started with raw feature-barcode matrices for analysis with the Bioconductor suite of R packages for single-cell RNA-seq analysis (Amezquita et al, 2020) . For quality control and cell calling, we first used a Monte Carlo simulation-based approach to assess and rule out empty droplets or those with random ambient transcriptional noise, such as from debris (Griffiths et al, 2018;Lun et al, 2019) .…”

Section: Dlpfc Snrna-seq Data Generationmentioning

confidence: 99%

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Maynard

Collado‐Torres

Weber³

et al. 2020

Preprint

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We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

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Section: Dlpfc Snrna-seq Data Generationmentioning

confidence: 99%

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Maynard

Collado‐Torres

Weber³

et al. 2020

Preprint

Self Cite

142

326

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“…Many computational applications have been developed that leverage the advantages of scRNA-seq experiments[10, 22,31,40]. Analysis has primarily focused on the interpretation of the cellular landscape; software suites incorporating customizable workflows have been developed to enable this analysis [1,31,41]. Denoising computational approaches to mitigating the sparsity of single-cell data (having few counts per cell) have corrected structural and sampling zeros [3], imputed missing values [9,15,24], or corrected measured expression values [11,12,39].…”

Section: Introductionmentioning

confidence: 99%

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

Tjaernberg¹,

Mahmood²,

Jackson

et al. 2020

Preprint

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The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN-and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN-and diffusion-based denoising methods and serve as a foundation for future research.

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“…Single cell data retrieved from the Single Cell Expression Atlas is processed using custom python code (see https://github.com/reactome/gsa-backend for details). This approach to create pseudo-bulk RNA-seq data resembles previously described methods to calculate differentially expressed genes 16 . Thereby, all pathway analysis methods supported by the ReactomeGSA analysis system are accessible to scRNA-seq data as well.…”

Section: Scrna-seq Pathway Analysismentioning

confidence: 99%

“…The ReactomeGSA R package has dedicated features to simplify pathway analyses of scRNA-seq data ( Figure 3). The "analyse_sc_clusters" function can directly process Seurat 15 and Bioconductor's SingleCellExperiment objects 16 . It automatically retrieves the average gene expression per cell cluster and performs an ssGSEA analysis on the cluster-level expression values.…”

Section: Reactomegsamentioning

confidence: 99%

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Griss

Viteri

Sidiropoulos

et al. 2020

Preprint

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Pathway analyses are key methods to analyse ‘omics experiments. Nevertheless, integrating data from different ‘omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome’s existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA thereby greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterise the role of B cells in anti-tumour immunity. We compared B cell rich and poor human cancer samples from five TCGA transcriptomics and two CPTAC proteomics studies. There, B cell-rich lung adenocarcinoma samples lack the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumour associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

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Orchestrating Single-Cell Analysis with Bioconductor

Cited by 54 publications

References 143 publications

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

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