SCMarker: ab initio marker selection for single cell transcriptome profiling

AbstractSingle-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.Author SummarySingle cell RNA-sequencing technology simultaneously provides the mRNA transcript levels of thousands of genes in thousands of cells. A frequent requirement of single cell expression analysis is the identification of markers which may explain complex cellular states or tissue composition. We propose a new marker selection strategy (SCMarker) to accurately delineate cell types in single cell RNA-sequencing data by identifying genes that have bi/multi-modally distributed expression levels and are co- or mutually-exclusively expressed with some other genes. Our method can determine the cell-type-discriminative markers without referencing to any known transcriptomic profiles or cell ontologies, and consistently achieves accurate cell-type-discriminative marker identification in a variety of scRNA-seq datasets.

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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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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

BMC Bioinformatics ◽

10.1186/s12859-021-04028-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Bobby Ranjan ◽

Florian Schmidt ◽

Wenjie Sun ◽

Jinyu Park ◽

Mohammad Amin Honardoost ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Differentially Expressed Genes ◽

Cell Types ◽

Unsupervised Clustering ◽

Differentially Expressed ◽

Consensus Clustering ◽

Cell Type ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Background Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering approaches have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. Results We present scConsensus, an $${\mathbf {R}}$$ R framework for generating a consensus clustering by (1) integrating results from both unsupervised and supervised approaches and (2) refining the consensus clusters using differentially expressed genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. Conclusions scConsensus combines the merits of unsupervised and supervised approaches to partition cells with better cluster separation and homogeneity, thereby increasing our confidence in detecting distinct cell types. scConsensus is implemented in $${\mathbf {R}}$$ R and is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

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Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

10.1101/611517 ◽

2019 ◽

Cited By ~ 3

Author(s):

Ana Carolina Leote ◽

Xiaohui Wu ◽

Andreas Beyer

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Regulatory Network ◽

Missing Values ◽

Transcriptional Regulatory Network ◽

Imputation Method ◽

Sequencing Data ◽

Expression Levels ◽

Single Cell Rna Sequencing ◽

Baseline Approach

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, up to 48% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from https://github.com/anacarolinaleote/ADImpute) that automatically determines the best imputation method for each gene in a dataset.

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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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SciBet: a portable and fast single cell type identifier

10.1101/645358 ◽

2019 ◽

Cited By ~ 2

Author(s):

Chenwei Li ◽

Baolin Liu ◽

Boxi Kang ◽

Zedao Liu ◽

Yedan Liu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Type ◽

Sequencing Data ◽

Local Computation ◽

Local Data ◽

Single Cell Rna Sequencing ◽

Cross Platform ◽

Single Cell Type ◽

User Friendly

ABSTRACTFast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

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Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

10.1101/2020.11.16.385328 ◽

2020 ◽

Author(s):

Rui Hong ◽

Yusuke Koga ◽

Shruthi Bandyadka ◽

Anastasia Leshchyk ◽

Zhe Wang ◽

...

Keyword(s):

Quality Control ◽

Single Cell ◽

Rna Sequencing ◽

Sequencing Data ◽

Programming Environments ◽

Marker Selection ◽

Quality Control Metrics ◽

Single Cell Rna Sequencing ◽

Standard Quality ◽

Downstream Analysis

AbstractPerforming comprehensive quality control is necessary to remove technical or biological artifacts in single-cell RNA sequencing (scRNA-seq) data. Artifacts in the scRNA-seq data, such as doublets or ambient RNA, can also hinder downstream clustering and marker selection and need to be assessed. While several algorithms have been developed to perform various quality control tasks, they are only available in different packages across various programming environments. No standardized workflow has been developed to streamline the generation and reporting of all quality control metrics from these tools. We have built an easy-to-use pipeline, named SCTK-QC, in the singleCellTK package that generates a comprehensive set of quality control metrics from a plethora of packages for quality control. We are able to import data from several preprocessing tools including CellRanger, STARSolo, BUSTools, dropEST, Optimus, and SEQC. Standard quality control metrics for each cell are calculated including the total number of UMIs, total number of genes detected, and the percentage of counts mapping to predefined gene sets such as mitochondrial genes. Doublet detection algorithms employed include scrublet, scds, doubletCells, and doubletFinder. DecontX is used to identify contamination in each individual cell. To make the data accessible in downstream analysis workflows, the results can be exported to common data structures in R and Python or to text files for use in any generic workflow. Overall, this pipeline will streamline and standardize quality control analyses for single cell RNA-seq data across different platforms.

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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

10.1101/2020.04.22.056473 ◽

2020 ◽

Author(s):

Bobby Ranjan ◽

Florian Schmidt ◽

Wenjie Sun ◽

Jinyu Park ◽

Mohammad Amin Honardoost ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cell Types ◽

Unsupervised Clustering ◽

Differentially Expressed ◽

Consensus Clustering ◽

Cell Type ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Data Clusters

Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present scConsensus, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. scConsensus is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

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