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

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]

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

2020 ◽  
Author(s):  
Wenbo Guo ◽  
Dongfang Wang ◽  
Shicheng Wang ◽  
Yiran Shan ◽  
Changyi Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Learning Algorithm ◽  
R Package ◽  
Quality Control Metrics ◽  
Sample Data ◽  
Automated Processing ◽  
Transcriptomic Level ◽  
Cellular Phenotypes ◽  
User Friendly ◽  
Integration Analysis

Abstract Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic 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), gene signatures and cell–cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.

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

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.

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

2021 ◽  
Author(s):  
Fei Wu ◽  
Yaozhong Liu ◽  
Binhua Ling
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Host Cells ◽  
Host Responses ◽  
Rna Seq ◽  
Cell Level ◽  
Gene Expressions ◽  
Rnaseq Data ◽  
Software Program ◽  
User Friendly

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.

scholarly journals BingleSeq: A user-friendly R package for Bulk and Single-cell RNA-Seq Data Analysis

2020 ◽  
Author(s):  
Daniel Dimitrov ◽  
Quan Gu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Gene Annotation ◽  
Differential Expression Analysis ◽  
Transcriptome Profiling ◽  
R Package ◽  
Rna Seq ◽  
The Individual ◽  
User Friendly

AbstractRNA sequencing is a high-throughput sequencing technique considered as an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is Differential Expression analysis and it is used to determine genetic loci with distinct expression across different conditions. On the other hand, an emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both these types of analyses include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that both require programming expertise.BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface and incorporating three state-of-the-art software packages for each type of the aforementioned analyses, alongside additional features such as key visualisation techniques, functional gene annotation analysis and rank-based consensus for differential gene analysis results, among others. As a result, BingleSeq puts the best and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programming experience.

scholarly journals BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10469
Author(s):  
Daniel Dimitrov ◽  
Quan Gu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Differential Expression Analysis ◽  
Transcriptome Profiling ◽  
R Package ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
The Individual ◽  
Visualization Techniques ◽  
User Friendly

Background RNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing. Implementation BingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience. Availability BingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/.

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

2019 ◽  
Vol 47 (18) ◽  
pp. e111-e111 ◽  
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.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

2020 ◽  
Author(s):  
Dustin J. Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
User Friendly

AbstractRNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.HighlightsscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs).scMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration.scMappR is deployed as a user-friendly R package available at CRAN.

scholarly journals ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

2020 ◽  
Author(s):  
Johannes Smolander ◽  
Sini Junttila ◽  
Mikko S Venäläinen ◽  
Laura L Elo
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Single Cell ◽  
Cell Population ◽  
Learning Algorithm ◽  
R Package ◽  
Supplementary Information ◽  
Cell Populations ◽  
Rna Seq ◽  
Pancreatic Cell

Abstract Motivation Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. Results We introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods. Availability and implementation ILoReg is available as an R package at https://bioconductor.org/packages/ILoReg. Supplementary information Supplementary data are available at Bioinformatics online.

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

2015 ◽  
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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