ClustAssess: tools for assessing the robustness of single-cell clustering

Shahsavari, Arash; Munteanu, Andi; Mohorianu, Irina

doi:10.1101/2022.01.31.478592

Cited by 13 publications

(13 citation statements)

References 74 publications

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“…However, Cytocipher was able to identify the transcriptionally distinct populations. Importantly, several methods have been developed concerned with varying cluster hyper-parameters to identify an optimal parameter set [32][33][34][35][36][37][38][39] . While the criteria to define 'optimal' clustering is tool dependent 2-39 , the tool Clustree 39 for example, defines this as the highest resolution clustering where further increasing the clustering resolution results in arbitrary rearrangements of cells to increase the number of clusters.…”

Section: Discussionmentioning

confidence: 99%

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

Balderson

Thor

Bodén

2022

Preprint

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Identification of novel and known cell types with single cell RNA-seq (scRNA-seq) is revolutionising the study of multicellular organisms. However, typical scRNA-seq analysis involves many preprocessing steps and represents an abstraction of the original measurements, often resulting in clusters of single cells that may not display distinct gene expression. To mitigate this, cell clusters are typically validated as cell types by re-examination of the original expression measurements, often resulting in post-hoc manual alteration of clusters to ensure distinct gene expression. However, distinct cell populations may exist that are not clearly demarcated by a single marker gene, but instead co-express a unique combination of genes; a phenomenon which is difficult to detect by manual examination. Furthermore, manual examination of genes and post-hoc cluster editing is time-consuming, error-prone, and irreproducible. Here, we present Cytocipher, an scverse compatible bioinformatics method and software that scores cells for unique combinatorial gene co-expression and statistically tests whether clusters are significantly different. Application to both simulated and real data demonstrates that the combinatorial gene expression scoring outperforms existing per-cell gene enrichment methods, such as Giotto Parametric Analysis of Gene Set Enrichment and Scanpy-score. Furthermore, Cytocipher cluster-merging identified distinct CD8+ T cell subtypes in human peripheral blood mononuclear cells that were not identified in the original annotations. Cytocipher cluster-merging was also able to identify distinct intermediate states corresponding to cell lineage decisions and branch points in mouse pancreas development, not previously identified in the original annotations. Identification of significantly different clusters of cells is an important new methodological improvement to the existing analysis pipeline of scRNA-seq data. Utilisation of Cytocipher will thus ensure that single cell Atlas mapping efforts provide distinctly different and programmatically reproducible cell clusters. Cytocipher is available at https://github.com/BradBalderson/Cytocipher.

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Section: Discussionmentioning

confidence: 99%

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

Balderson

Thor

Bodén

2022

Preprint

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“…A 20-nearest neighbour graph was computed on the first 30 PCs of the data; the cells were clustered using SLM community detection [ 60 ] on the NN graph. To assess the clustering similarity, element-centric clustering comparison [ 61 ] was employed on the set of common barcodes between GT and S using the ClustAssess R package [ 62 ]. Cluster markers were identified using the ROC test in Seurat v3.1.4 [ 56 ]; only genes with

were considered.…”

Section: Materials and Methodsmentioning

confidence: 99%

The Sum of Two Halves May Be Different from the Whole—Effects of Splitting Sequencing Samples Across Lanes

Williams

Chazarra-Gil

Shahsavari

et al. 2022

Genes

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The advances in high-throughput sequencing (HTS) have enabled the characterisation of biological processes at an unprecedented level of detail; most hypotheses in molecular biology rely on analyses of HTS data. However, achieving increased robustness and reproducibility of results remains a main challenge. Although variability in results may be introduced at various stages, e.g., alignment, summarisation or detection of differential expression, one source of variability was systematically omitted: the sequencing design, which propagates through analyses and may introduce an additional layer of technical variation. We illustrate qualitative and quantitative differences arising from splitting samples across lanes on bulk and single-cell sequencing. For bulk mRNAseq data, we focus on differential expression and enrichment analyses; for bulk ChIPseq data, we investigate the effect on peak calling and the peaks’ properties. At the single-cell level, we concentrate on identifying cell subpopulations. We rely on markers used for assigning cell identities; both smartSeq and 10× data are presented. The observed reduction in the number of unique sequenced fragments limits the level of detail on which the different prediction approaches depend. Furthermore, the sequencing stochasticity adds in a weighting bias corroborated with variable sequencing depths and (yet unexplained) sequencing bias. Subsequently, we observe an overall reduction in sequencing complexity and a distortion in the biological signal across technologies, experimental contexts, organisms and tissues.

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“…Dimensionality reductions [PCA followed by UMAP (83)], as well as clustering [Louvain algorithm (84)], were conducted in Seurat; the optimal number of clusters was selected on the basis of a stability analysis using the ClustAssess package (v0.3.0) (85). Following an assessment of the stability of clustering results, for subsequent steps, we focused on the 3000 most variable genes, across all cells in the dataset.…”

Section: Single-cell Rna Sequencing Analysismentioning

confidence: 99%

Human surface ectoderm and amniotic ectoderm are sequentially specified according to cellular density

Nakanoh,

Sham,

Ghimire

et al. 2024

Sci. Adv.

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Mechanisms specifying amniotic ectoderm and surface ectoderm are unresolved in humans due to their close similarities in expression patterns and signal requirements. This lack of knowledge hinders the development of protocols to accurately model human embryogenesis. Here, we developed a human pluripotent stem cell model to investigate the divergence between amniotic and surface ectoderms. In the established culture system, cells differentiated into functional amnioblast-like cells. Single-cell RNA sequencing analyses of amnioblast differentiation revealed an intermediate cell state with enhanced surface ectoderm gene expression. Furthermore, when the differentiation started at the confluent condition, cells retained the expression profile of surface ectoderm. Collectively, we propose that human amniotic ectoderm and surface ectoderm are specified along a common nonneural ectoderm trajectory based on cell density. Our culture system also generated extraembryonic mesoderm–like cells from the primed pluripotent state. Together, this study provides an integrative understanding of the human nonneural ectoderm development and a model for embryonic and extraembryonic human development around gastrulation.

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ClustAssess: tools for assessing the robustness of single-cell clustering

Cited by 13 publications

References 74 publications

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

The Sum of Two Halves May Be Different from the Whole—Effects of Splitting Sequencing Samples Across Lanes

Human surface ectoderm and amniotic ectoderm are sequentially specified according to cellular density

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