Visualizing the Structure of RNA-seq Expression Data using Grade of Membership Models

Dey, Kushal Kumar; Hsiao, Chiaowen Joyce; Stephens, Matthew

doi:10.1101/051631

Cited by 20 publications

(26 citation statements)

References 52 publications

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“…using a non-parametric regression method such as trend filtering, to allow for the nonlinear trends that must occur in any cyclic phenomenon) before applying downstream analyses to the residuals. On the other hand, if the downstream methods rely on explicit models for count data (e.g., [37]) then controlling for cell cycle may be more complicated and require further methodological development. However, we note that these issues are not unique to our approach: controlling for cell cycle within count-based analyses poses additional methodological challenges for whatever method is used to estimate cell cycle.…”

Section: Discussionmentioning

confidence: 99%

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Hsiao¹,

Tung

Blischak³

et al. 2019

Preprint

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AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

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Section: Discussionmentioning

confidence: 99%

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Hsiao¹,

Tung

Blischak³

et al. 2019

Preprint

Self Cite

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“…The LDA model was originally introduced in the field of language processing to decompose large sets of text documents into topics (a problem known as “topic modelling”), based solely on their word frequency (Blei et al, ). It has been subsequently extended to the analysis of large and complex data sets in various fields, such as satellite image analysis (Vaduva, Gavat, & Datcu, ), fraud detection (Olszewski, ), bioinformatics (Dey, Hsiao, & Stephens, ; Liu, Tang, Dong, Yao, & Zhou, ) and temporal trends in popular music (Mauch, MacCallum, Levy, & Leroi, ). The popular software “Structure” of population genetics uses an almost identical model to characterize population structure based on the distribution of alleles across individuals (Pritchard, Stephens, & Donnelly, ).…”

Section: Introductionmentioning

confidence: 99%

Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA‐based census of soil biodiversity from a tropical forest

Sommeria‐Klein

Zinger

Coissac

et al. 2019

Molecular Ecology Resources

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High‐throughput sequencing of amplicons from environmental DNA samples permits rapid, standardized and comprehensive biodiversity assessments. However, retrieving and interpreting the structure of such data sets requires efficient methods for dimensionality reduction. Latent Dirichlet Allocation (LDA) can be used to decompose environmental DNA samples into overlapping assemblages of co‐occurring taxa. It is a flexible model‐based method adapted to uneven sample sizes and to large and sparse data sets. Here, we compare LDA performance on abundance and occurrence data, and we quantify the robustness of the LDA decomposition by measuring its stability with respect to the algorithm's initialization. We then apply LDA to a survey of 1,131 soil DNA samples that were collected in a 12‐ha plot of primary tropical forest and amplified using standard primers for bacteria, protists, fungi and metazoans. The analysis reveals that bacteria, protists and fungi exhibit a strong spatial structure, which matches the topographical features of the plot, while metazoans do not, confirming that microbial diversity is primarily controlled by environmental variation at the studied scale. We conclude that LDA is a sensitive, robust and computationally efficient method to detect and interpret the structure of large DNA‐based biodiversity data sets. We finally discuss the possible future applications of this approach for the study of biodiversity.

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“…These include standard k-means clustering, hierarchical clustering, and variants that are specifically designed for scRNA-seq data (i.e. RaceID/RaceID2 [6], CIDR [7]) as well as more advanced methods that utilise likelihood-based mixture modelling (countClust) [8], density-based spatial clustering [9] and kernel-based single-cell clustering (SIMLR) [10]. Several studies have compared and summarised various clustering algorithms used for scRNA-seq data analysis [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Geddes

Kim

Nan

et al. 2019

Preprint

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Background: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.Results: Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used. Conclusions:Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble

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Visualizing the Structure of RNA-seq Expression Data using Grade of Membership Models

Cited by 20 publications

References 52 publications

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Latent Dirichlet Allocation reveals spatial and taxonomic structure in a DNA‐based census of soil biodiversity from a tropical forest

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

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