scholarly journals Normalization of single-cell RNA-seq counts by log(x + 1)* or log(1 + x)*

2021 ◽  
Author(s):  
A Sina Booeshaghi ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
The Past ◽  
Highly Expressed Genes ◽  
Low Levels ◽  
Resolution Cell ◽  
Host Genes

Abstract   Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don’t hold when examining low-expressed genes, with the result that standard workflows can produce misleading results. Supplementary information Supplementary data and all of the code to reproduce Figure 1 are available here: https://github.com/pachterlab/BP_2020_2/.

scholarly journals Normalization of single-cell RNA-seq counts by log(x+1)* or log(1+x)*

2020 ◽  
Author(s):  
A. Sina Booeshaghi ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
List Type ◽  
Rna Seq ◽  
Cell Type Specificity ◽  
Alternative Approach ◽  
Key Points ◽  
Highly Expressed Genes ◽  
Low Levels ◽  
Resolution Cell ◽  
Host Genes

AbstractSingle-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses don’t hold when examining low-expressed genes, with the result that standard workflows can produce misleading results.Key PointsLowly expressed genes in single-cell RNA-seq can be easliy misanalyzed.log(1+x) count normalization introduces errors for lowly expressed genesThe average log(1+x) expression differs considerably from log(x) when x is smallAn alternative approach is to use the fraction of cells with non-zero expression

scholarly journals Single cell RNA‐seq in the sea urchin embryo show marked cell‐type specificity in the Delta/Notch pathway

2019 ◽  
Vol 86 (8) ◽  
pp. 931-934 ◽  
Author(s):  
Stephany Foster ◽  
Yee Voan Teo ◽  
Nicola Neretti ◽  
Nathalie Oulhen ◽  
Gary M. Wessel
Keyword(s):  
Single Cell ◽  
Sea Urchin ◽  
Notch Pathway ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Sea Urchin Embryo ◽  
Marked Cell

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

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.

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

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Dustin J Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cost Constraints ◽  
Mouse Tissues

Abstract RNA 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 leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and 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. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 750
Author(s):  
Olukayode A. Sosina ◽  
Matthew N. Tran ◽  
Kristen R. Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Moderate Correlation ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Reference Dataset ◽  
The Past ◽  
Cell Type Composition ◽  
Type Composition

Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

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

2019 ◽  
Vol 36 (8) ◽  
pp. 2474-2485 ◽  
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.

scholarly journals Interpretable factor models of single-cell RNA-seq via variational autoencoders

2020 ◽  
Vol 36 (11) ◽  
pp. 3418-3421 ◽  
Author(s):  
Valentine Svensson ◽  
Adam Gayoso ◽  
Nir Yosef ◽  
Lior Pachter
Keyword(s):  
Single Cell ◽  
Factor Model ◽  
Factor Models ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Massive Datasets ◽  
Domain Specific ◽  
Variational Autoencoder ◽  
Inference Methods

Abstract Motivation Single-cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. Results We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be useful for other applications. Availability and implementation The factor model is available in the scVI package hosted at https://github.com/YosefLab/scVI/. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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

2018 ◽  
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.

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

2019 ◽  
Vol 35 (14) ◽  
pp. i436-i445 ◽  
Author(s):  
Gregor Sturm ◽  
Francesca Finotello ◽  
Florent Petitprez ◽  
Jitao David Zhang ◽  
Jan Baumbach ◽  
...  
Keyword(s):  
Single Cell ◽  
Computational Methods ◽  
Immune Cell ◽  
Comprehensive Evaluation ◽  
Cell Types ◽  
R Package ◽  
Supplementary Information ◽  
Rna Seq ◽  
Cell Type ◽  
Real World Datasets

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.

scholarly journals The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Peng Xu ◽  
Minghui Wang ◽  
Won-min Song ◽  
Qian Wang ◽  
Guo-Cheng Yuan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Control Cell ◽  
Protein Accumulation ◽  
Human Tissues ◽  
Cellular Interactions ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cell Type Specific

Abstract Background Cellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood. Methods This study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity. Results Through a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis. Conclusions This study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

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