Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology

Abstract Motivation The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. Results We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. Availability and implementation A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). Supplementary information Supplementary data are available at Bioinformatics online.

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Comprehensive evaluation of computational cell-type quantification methods for immuno-oncology

10.1101/463828 ◽

2018 ◽

Cited By ~ 4

Author(s):

Gregor Sturm ◽

Francesca Finotello ◽

Florent Petitprez ◽

Jitao David Zhang ◽

Jan Baumbach ◽

...

Keyword(s):

Tumor Microenvironment ◽

Single Cell ◽

Computational Methods ◽

Immune Cell ◽

Comprehensive Evaluation ◽

Supplementary Information ◽

Rna Seq ◽

Cell Type ◽

Link Type ◽

Real World Datasets

AbstractMotivationThe composition and density of immune cells in the tumor microenvironment profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining, or single-cell sequencing is often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing.ResultsWe developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11,000 cells from the tumor microenvironment to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1,800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures.AvailabilityA snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv)[email protected] informationSupplementary data are available at Bioinformatics online.

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rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

10.1101/2021.05.28.446161 ◽

2021 ◽

Author(s):

Daniel Osorio ◽

Marieke Lydia Kuijjer ◽

James J. Cai

Keyword(s):

Single Cell ◽

Cell Types ◽

R Package ◽

Rna Seq ◽

Cell Type ◽

Sequencing Data ◽

Single Experiment ◽

Tissue Samples ◽

Molecular Phenotypes ◽

Public Datasets

Motivation: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and Implementation: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

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Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

10.7287/peerj.preprints.26858 ◽

2018 ◽

Author(s):

Santiago J Carmona ◽

David Gfeller

Keyword(s):

Single Cell ◽

Immune Cell ◽

Adaptive Immune System ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Recent Developments ◽

Species Specific ◽

Development And Validation ◽

Whole Transcriptome

Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.

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Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq

10.7287/peerj.preprints.26858v1 ◽

2018 ◽

Author(s):

Santiago J Carmona ◽

David Gfeller

Keyword(s):

Single Cell ◽

Immune Cell ◽

Adaptive Immune System ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Recent Developments ◽

Species Specific ◽

Development And Validation ◽

Whole Transcriptome

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ACTINN: automated identification of cell types in single cell RNA sequencing

Bioinformatics ◽

10.1093/bioinformatics/btz592 ◽

2019 ◽

Cited By ~ 7

Author(s):

Feiyang Ma ◽

Matteo Pellegrini

Keyword(s):

Neural Network ◽

Single Cell ◽

Rna Sequencing ◽

Immune Cell ◽

Cell Types ◽

Mouse Cell ◽

Supplementary Information ◽

Cell Type ◽

Human T Cell ◽

Single Cell Rna Sequencing

Abstract Motivation Cell 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 three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results 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. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

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Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle

Bioinformatics ◽

10.1093/bioinformatics/btab629 ◽

2021 ◽

Author(s):

Tobias Tekath ◽

Martin Dugas

Keyword(s):

Single Cell ◽

Transcript Level ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Rna Seq ◽

Cell Type ◽

Gene Level ◽

Analysis Workflow ◽

Usage Analysis

Abstract Motivation Each year, the number of published bulk and single-cell RNA-seq data sets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell type identification. Results We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq data sets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. Additionally, we present novel potential DTU applications like the identification of cell type specific transcript isoforms as biomarkers. Availability The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Supplementary information Supplementary data are available at Bioinformatics online.

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scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization

Bioinformatics ◽

10.1093/bioinformatics/btz936 ◽

2019 ◽

Vol 36 (8) ◽

pp. 2474-2485 ◽

Cited By ~ 2

Author(s):

Zhanying Feng ◽

Xianwen Ren ◽

Yuan Fang ◽

Yining Yin ◽

Chutian Huang ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Cell Types ◽

Mouse Cell ◽

Supplementary Information ◽

Rna Seq ◽

Cell Type ◽

Robust Solution ◽

Development Trajectory ◽

Consensus Optimization

Abstract Motivation Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene’s ability to reconstruct cell–cell relationship and minimize gene redundancy by considering gene–gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM’s advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as ‘mouse cell marker atlas’, which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. Availability and implementation scTIM is freely available at https://github.com/Frank-Orwell/scTIM. Supplementary information Supplementary data are available at Bioinformatics online.

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CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

10.1101/2021.01.30.428954 ◽

2021 ◽

Author(s):

Kai Kang ◽

Caizhi David Huang ◽

Yuanyuan Li ◽

David M. Umbach ◽

Leping Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

In Silico ◽

Cell Types ◽

R Package ◽

Biological Tissues ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Cell Type Specific

AbstractBackgroundBiological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and with a new function to aid interpretation of deconvolution outcomes. The R package would be of interest for the broader R community.ResultWe developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating CDSeq-estimated cell types using publicly available single-cell RNA sequencing (scRNA-seq) data (single-cell data from 20 major organs are included in the R package). This function allows users to readily interpret and visualize the CDSeq-estimated cell types. We carried out additional validations of the CDSeqR software with in silico and in vitro mixtures and with real experimental data including RNA-seq data from the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) project.ConclusionsThe existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. However, bulk level analyses neglect tissue heterogeneity and hinder investigation in a cell-type-specific fashion. The CDSeqR package can be viewed as providing in silico single-cell dissection of bulk measurements. It enables researchers to gain cell-type-specific information from bulk RNA-seq data.

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

10.1101/2020.08.24.265298 ◽

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.

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Tree-based Correlation Screen and Visualization for Exploring Phenotype-Cell Type Association in Multiple Sample Single-Cell RNA-Sequencing Experiments

10.1101/2021.10.27.466024 ◽

2021 ◽

Author(s):

Boyang Zhang ◽

Zhicheng Ji ◽

Hongkai Ji

Keyword(s):

Single Cell ◽

Cell Types ◽

R Package ◽

Phenotypic Traits ◽

Rna Seq ◽

Cell Type ◽

Molecular Features ◽

Multiple Sample ◽

Multiple Cell ◽

Multiple Samples

Single-cell RNA-seq experiments with multiple samples are increasingly used to discover cell types and their molecular features that may influence sample phenotype (e.g. disease). However, analyzing and visualizing the complex cell type-phenotype association remains nontrivial. TreeCorTreat is an open source R package that tackles this problem by using a tree-based correlation screen to analyze and visualize the association between phenotype and transcriptomic features and cell types at multiple cell type resolution levels. With TreeCorTreat, one can conveniently explore and compare different feature types, phenotypic traits, analysis protocols and datasets, and evaluate the impacts of potential confounders.

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