ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Liu, Baolin; Li, Chenwei; Li, Ziyi; Ren, Xianwen; Zhang, Zemin

doi:10.1101/819581

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…We recommend users to choose a state-of-the-art cell clustering method such as Seurat and SC3. For the resulting clusters, we recommend users to visualize them by t-SNE or UMAP and use a goodness-of-fit measure (e.g., Pearson’s chi-square statistic and ROGUE score [107]) to check whether each gene approximately follows a NB or ZINB distribution in a cell cluster. This check will guide users to decide on an appropriate number of cell clusters in a data-driven way.…”

Section: Methodsmentioning

confidence: 99%

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Sun

Song

2020

Preprint

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In the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, an interpretable simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.

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Section: Methodsmentioning

confidence: 99%

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Sun

Song

2020

Preprint

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“…Quantification of genetic entropy was performed using ROGUE (Ratio of Global Unshifted Entropy) (50). As input, raw counts of thyrocytes that passed quality control were used.…”

Section: Methodsmentioning

confidence: 99%

Single-cell transcriptome analysis reveals cell-cell communication and thyrocyte diversity in the zebrafish thyroid gland

Gillotay

Shankar

Eski

et al. 2020

Preprint

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Single-cell transcriptome analysis reveals cell-cell communication and thyrocyte 1diversity in the zebrafish thyroid gland. 2 Abstract 16The thyroid gland regulates growth and metabolism via production of thyroid hormone in 17 follicles composed of thyrocytes. So far, thyrocytes have been assumed to be a 18 homogenous population. To uncover genetic heterogeneity in the thyrocyte population, 19 and molecularly characterize the non-thyrocyte cells surrounding the follicle, we 20 developed a single-cell transcriptome atlas of the zebrafish thyroid gland. The 6249-cell 21 atlas includes profiles of thyrocytes, blood vessels, lymphatic vessels, immune cells and 22 fibroblasts. Further, the thyrocytes could be split into two sub-populations with unique 23 transcriptional signature, including differential expression of the transcription factor 24 pax2a. To validate thyrocyte heterogeneity, we generated a CRISPR/Cas9-based 25 pax2a knock-in line, which demonstrated specific pax2a expression in the thyrocytes. 26However, a population of pax2a-low mature thyrocytes interspersed within individual 27 follicles could be distinguished, corroborating heterogeneity within the thyrocyte 28 population. Our results identify and validate transcriptional differences within the 29 nominally homogenous thyrocyte population. 30

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Evaluating single-cell cluster stability using the Jaccard similarity index

Tang

Kaymaz

Logeman

et al. 2020

Preprint

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Motivation: One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others. Results: Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat, and estimation of cluster stability using the Jaccard similarity index. The Snakemake workflow takes advantage of high-performance computing clusters and dispatches jobs in parallel to available CPUs to speed up the analysis. The scclusteval package provides functions to facilitate the analysis of the output, including a series of rich visualizations. Availability: R package scclusteval:

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ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Cited by 3 publications

References 49 publications

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Single-cell transcriptome analysis reveals cell-cell communication and thyrocyte diversity in the zebrafish thyroid gland

Evaluating single-cell cluster stability using the Jaccard similarity index

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