Meta-Transcriptome Detector (MTD): a novel pipeline for metatranscriptome analysis of bulk and single-cell RNAseq data

RNA-seq data contains not only host transcriptomes but also non-host information that comprises transcripts from active microbiota in the host cells. Therefore, metatranscriptomics can reveal gene expression of the entire microbial community in a given sample. However, there is no single tool that can simultaneously analyze host-microbiota interactions and to quantify microbiome at the single-cell level, particularly for users with limited expertise of bioinformatics. Here, we developed a novel software program that can comprehensively and synergistically analyze gene expression of the host and microbiome as well as their association using bulk and single-cell RNA-seq data. Our pipeline, named Meta-Transcriptome Detector (MTD), can identify and quantify microbiome extensively, including viruses, bacteria, protozoa, fungi, plasmids, and vectors. MTD is easy to install and is user-friendly. This novel software program empowers researchers to study the interactions between microbiota and the host by analyzing gene expressions and pathways, which provides further insights into host responses to microorganisms.

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scAPAdb: a comprehensive database of alternative polyadenylation at single-cell resolution

Nucleic Acids Research ◽

10.1093/nar/gkab795 ◽

2021 ◽

Author(s):

Sheng Zhu ◽

Qiwei Lian ◽

Wenbin Ye ◽

Wei Qin ◽

Zhe Wu ◽

...

Keyword(s):

Single Cell ◽

Alternative Polyadenylation ◽

Cell Types ◽

Single Cell Level ◽

Cell Heterogeneity ◽

Rna Seq ◽

Cell Level ◽

Eukaryotic Gene ◽

User Friendly ◽

Different Cell Types

Abstract Alternative polyadenylation (APA) is a widespread regulatory mechanism of transcript diversification in eukaryotes, which is increasingly recognized as an important layer for eukaryotic gene expression. Recent studies based on single-cell RNA-seq (scRNA-seq) have revealed cell-to-cell heterogeneity in APA usage and APA dynamics across different cell types in various tissues, biological processes and diseases. However, currently available APA databases were all collected from bulk 3′-seq and/or RNA-seq data, and no existing database has provided APA information at single-cell resolution. Here, we present a user-friendly database called scAPAdb (http://www.bmibig.cn/scAPAdb), which provides a comprehensive and manually curated atlas of poly(A) sites, APA events and poly(A) signals at the single-cell level. Currently, scAPAdb collects APA information from > 360 scRNA-seq experiments, covering six species including human, mouse and several other plant species. scAPAdb also provides batch download of data, and users can query the database through a variety of keywords such as gene identifier, gene function and accession number. scAPAdb would be a valuable and extendable resource for the study of cell-to-cell heterogeneity in APA isoform usages and APA-mediated gene regulation at the single-cell level under diverse cell types, tissues and species.

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scCancer: a package for automated processing of single cell RNA-seq data in cancer

10.1101/800490 ◽

2019 ◽

Author(s):

Wenbo Guo ◽

Dongfang Wang ◽

Shicheng Wang ◽

Yiran Shan ◽

Jin Gu

Keyword(s):

Single Cell ◽

Learning Algorithm ◽

R Package ◽

Rna Seq ◽

Cell Level ◽

Quality Control Metrics ◽

Automated Processing ◽

Cellular Phenotypes ◽

User Friendly ◽

Processing Steps

AbstractSummaryMolecular heterogeneities bring great challenges for cancer diagnosis and treatment. Recent advance in single cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer transcriptomic heterogeneities at single cell level. Here, we develop an R package named scCancer which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness) and gene signatures. Finally, a user-friendly graphic report was generated for all the analyses.Availabilityhttp://lifeome.net/software/sccancer/[email protected]

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LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

Nucleic Acids Research ◽

10.1093/nar/gkz655 ◽

2019 ◽

Vol 47 (18) ◽

pp. e111-e111 ◽

Cited By ~ 12

Author(s):

Changlin Wan ◽

Wennan Chang ◽

Yu Zhang ◽

Fenil Shah ◽

Xiaoyu Lu ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

R Package ◽

Data Sets ◽

Rna Seq ◽

Mrna Metabolism ◽

Transcriptional Regulatory ◽

User Friendly

Abstract A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

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SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009118 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1009118

Author(s):

Jing Qi ◽

Yang Zhou ◽

Zicen Zhao ◽

Shuilin Jin

Keyword(s):

Gene Expression ◽

Single Cell ◽

Differential Expression Analysis ◽

Cell Types ◽

Rna Seq ◽

Cell Level ◽

Gene Level ◽

Level Information ◽

Downstream Analysis ◽

Gene Expression Levels

The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.

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Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles

10.1101/027219 ◽

2015 ◽

Cited By ~ 18

Author(s):

Kieran Campbell ◽

Chris P Ponting ◽

Caleb Webber

Keyword(s):

Gene Expression ◽

Single Cell ◽

Biological Processes ◽

Rna Seq ◽

Principal Curves ◽

Cell Level ◽

Laplacian Eigenmaps ◽

Low Dimensional ◽

Cell Data ◽

Insight Into

Advances in RNA-seq technologies provide unprecedented insight into the variability and heterogeneity of gene expression at the single-cell level. However, such data offers only a snapshot of the transcriptome, whereas it is often the progression of cells through dynamic biological processes that is of interest. As a result, one outstanding challenge is to infer such progressions by ordering gene expression from single cell data alone, known as the cell ordering problem. Here, we introduce a new method that constructs a low-dimensional non-linear embedding of the data using laplacian eigenmaps before assigning each cell a pseudotime using principal curves. We characterise why on a theoretical level our method is more robust to the high levels of noise typical of single-cell RNA-seq data before demonstrating its utility on two existing datasets of differentiating cells.

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Individual Level Differential Expression Analysis for Single Cell RNA-seq data

10.1101/2021.05.10.443350 ◽

2021 ◽

Author(s):

Mengqi Zhang ◽

Si Liu ◽

Zhen Miao ◽

Fang Han ◽

Raphael Gottardo ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Bulk Solution ◽

Rna Seq ◽

Cell Level ◽

Individual Level ◽

Level Data

Bulk RNA-seq data quantify the expression of a gene in an individual by one number (e.g., fragment count). In contrast, single cell RNA-seq (scRNA-seq) data provide much richer information: the distribution of gene expression across many cells. To assess differential expression across individuals using scRNA-seq data, a straightforward solution is to create ''pseudo'' bulk RNA-seq data by adding up the fragment counts of a gene across cells for each individual, and then apply methods designed for differential expression using bulk RNA-seq data. This pseudo-bulk solution reduces the distribution of gene expression across cells to a single number and thus loses a good amount of information. We propose to assess differential expression using the gene expression distribution measured by cell level data. We find denoising cell level data can substantially improve the power of this approach. We apply our method, named IDEAS (Individual level Differential Expression Analysis for scRNA-seq), to study the gene expression difference between autism subjects and controls. We find neurogranin-expressing neurons harbor a high proportion of differentially expressed genes, and ERBB signals in microglia are associated with autism.

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OEFinder: A user interface to identify and visualize ordering effects in single-cell RNA-seq data

10.1101/025437 ◽

2015 ◽

Cited By ~ 1

Author(s):

Ning Leng ◽

Jeea Choi ◽

Li-Fang Chu ◽

James Thomson ◽

Christina Kendziorski ◽

...

Keyword(s):

Gene Expression ◽

User Interface ◽

Single Cell ◽

Statistical Method ◽

R Package ◽

Data Sets ◽

Graphical Interface ◽

Rna Seq ◽

Ordering Effects ◽

User Friendly

A recent paper identified an artifact in multiple single-cell RNA-seq (scRNA-seq) data sets generated by the Fluidigm C1 platform. Specifically, Leng* et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations that allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.

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SUGAR-seq enables simultaneous detection of glycans, epitopes, and the transcriptome in single cells

Science Advances ◽

10.1126/sciadv.abe3610 ◽

2021 ◽

Vol 7 (8) ◽

pp. eabe3610

Author(s):

Conor J. Kearney ◽

Stephin J. Vervoort ◽

Kelly M. Ramsbottom ◽

Izabela Todorovski ◽

Emily J. Lelliott ◽

...

Keyword(s):

Single Cell ◽

Posttranslational Modification ◽

Cellular Differentiation ◽

Single Cells ◽

Simultaneous Detection ◽

Surface Protein ◽

Rna Seq ◽

Cell Level ◽

Simultaneous Integration ◽

Unique Surface

Multimodal single-cell RNA sequencing enables the precise mapping of transcriptional and phenotypic features of cellular differentiation states but does not allow for simultaneous integration of critical posttranslational modification data. Here, we describe SUrface-protein Glycan And RNA-seq (SUGAR-seq), a method that enables detection and analysis of N-linked glycosylation, extracellular epitopes, and the transcriptome at the single-cell level. Integrated SUGAR-seq and glycoproteome analysis identified tumor-infiltrating T cells with unique surface glycan properties that report their epigenetic and functional state.

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