SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

Abstract A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called ‘dropout’), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.2 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 1

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Peripheral Blood ◽

Characteristic Curve ◽

Cell Type ◽

Sequencing Data ◽

Cell Clusters ◽

Reference Cell ◽

Mouse Tissues ◽

Single Cell Rna Sequencing

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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Automated identification of Cell Types in Single Cell RNA Sequencing

10.1101/532093 ◽

2019 ◽

Cited By ~ 3

Author(s):

Feiyang Ma ◽

Matteo Pellegrini

Keyword(s):

Neural Network ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Marker Genes ◽

Complex Data ◽

Cell Type ◽

Human T Cell ◽

Single Cell Rna Sequencing

AbstractCell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines.Author SummarySingle cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.1 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 7

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Expression Signature ◽

Cell Type ◽

Cell Clusters ◽

Reference Cell ◽

Single Cell Rna Sequencing ◽

Gene Expression Signatures ◽

Type Gene

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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deCS: A tool for systematic cell type annotations of single-cell RNA sequencing data among human tissues

10.1101/2021.09.19.460993 ◽

2021 ◽

Author(s):

Guangsheng Pei ◽

Fangfang Yan ◽

Lukas M Simon ◽

Yulin Dai ◽

Peilin Jia ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Cell ◽

Expression Profiles ◽

Marker Genes ◽

Data Sets ◽

Cell Type ◽

Cellular Mechanisms ◽

Single Cell Rna Sequencing ◽

Annotation Accuracy

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method based on a comprehensive collection of human cell type expression profiles or a list of marker genes. We applied deCS to single-cell data sets from various tissue types, and systematically evaluated the annotation accuracy under various conditions, including reference panels, sequencing depth and feature selection. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to the existing state-of-the-art annotation tools, deCS significantly reduced computation time while increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insight into the cellular mechanisms of disease pathogenesis.

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Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data

F1000Research ◽

10.12688/f1000research.18490.3 ◽

2019 ◽

Vol 8 ◽

pp. 296 ◽

Cited By ~ 2

Author(s):

J. Javier Diaz-Mejia ◽

Elaine C. Meng ◽

Alexander R. Pico ◽

Sonya A. MacParland ◽

Troy Ketela ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Human Peripheral Blood ◽

Characteristic Curve ◽

Cell Type ◽

Sequencing Data ◽

Cell Clusters ◽

Reference Cell ◽

Mouse Tissues ◽

Single Cell Rna Sequencing

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

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Comprehensive network modeling from single cell RNA sequencing of human and mouse reveals well conserved transcription regulation of hematopoiesis

BMC Genomics ◽

10.1186/s12864-020-07241-2 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Shouguo Gao ◽

Zhijie Wu ◽

Xingmin Feng ◽

Sachiko Kajigaya ◽

Xujing Wang ◽

...

Keyword(s):

Bone Marrow ◽

Transcription Factors ◽

Single Cell ◽

Rna Sequencing ◽

Transcription Regulation ◽

Regulatory Networks ◽

Stem Cell Differentiation ◽

Hematopoietic Stem ◽

Single Cell Rna Sequencing ◽

Human And Mouse

Abstract Background Presently, there is no comprehensive analysis of the transcription regulation network in hematopoiesis. Comparison of networks arising from gene co-expression across species can facilitate an understanding of the conservation of functional gene modules in hematopoiesis. Results We used single-cell RNA sequencing to profile bone marrow from human and mouse, and inferred transcription regulatory networks in each species in order to characterize transcriptional programs governing hematopoietic stem cell differentiation. We designed an algorithm for network reconstruction to conduct comparative transcriptomic analysis of hematopoietic gene co-expression and transcription regulation in human and mouse bone marrow cells. Co-expression network connectivity of hematopoiesis-related genes was found to be well conserved between mouse and human. The co-expression network showed “small-world” and “scale-free” architecture. The gene regulatory network formed a hierarchical structure, and hematopoiesis transcription factors localized to the hierarchy’s middle level. Conclusions Transcriptional regulatory networks are well conserved between human and mouse. The hierarchical organization of transcription factors may provide insights into hematopoietic cell lineage commitment, and to signal processing, cell survival and disease initiation.

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Critical downstream analysis steps for single-cell RNA sequencing data

Briefings in Bioinformatics ◽

10.1093/bib/bbab105 ◽

2021 ◽

Author(s):

Zilong Zhang ◽

Feifei Cui ◽

Chen Lin ◽

Lingling Zhao ◽

Chunyu Wang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Noisy Data ◽

Single Cell Level ◽

Cell Type ◽

Sequencing Data ◽

Cell Level ◽

Bioinformatics Tool ◽

Single Cell Rna Sequencing ◽

Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

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