SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

Abstract Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

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SoupX removes ambient RNA contamination from droplet based single-cell RNA sequencing data

10.1101/303727 ◽

2018 ◽

Cited By ~ 58

Author(s):

Matthew D Young ◽

Sam Behjati

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

List Type ◽

Sequencing Data ◽

Sequencing Technologies ◽

Single Cell Rna Sequencing ◽

Biological Interpretation ◽

Downstream Analysis ◽

Rna Contamination

AbstractBackgroundDroplet based single-cell RNA sequence analyses assume all acquired RNAs are endogenous to cells. However, any cell free RNAs contained within the input solution are also captured by these assays. This sequencing of cell free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data.ResultsWe demonstrate that contamination from this ‘soup’ of cell free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating ‘background corrected’ cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics.ConclusionsWe present ‘SoupX’, a tool for removing ambient RNA contamination from droplet based single cell RNA sequencing experiments. This tool has broad applicability and its application can improve the biological utility of existing and future data sets.Key PointsThe signal from droplet based single cell RNA sequencing is ubiquitously contaminated by capture of ambient mRNA.SoupX is a method to quantify the abundance of these ambient mRNAs and remove them.Correcting for ambient mRNA contamination improves biological interpretation.

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Differential analysis of binarized single-cell RNA sequencing data captures biological variation

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab118 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Gerard A Bouland ◽

Ahmed Mahfouz ◽

Marcel J T Reinders

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Biological Variation ◽

Expression Data ◽

Differential Analysis ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

Cell Expression ◽

Zero Counts

Abstract Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.

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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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A map of tumor–host interactions in glioma at single-cell resolution

GigaScience ◽

10.1093/gigascience/giaa109 ◽

2020 ◽

Vol 9 (10) ◽

Cited By ~ 3

Author(s):

Francesca Pia Caruso ◽

Luciano Garofano ◽

Fulvio D'Angelo ◽

Kai Yu ◽

Fuchou Tang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Talk ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

Host Interaction ◽

Receptor Interactions ◽

Single Cell Rna Sequencing

ABSTRACT Background Single-cell RNA sequencing is the reference technique for characterizing the heterogeneity of the tumor microenvironment. The composition of the various cell types making up the microenvironment can significantly affect the way in which the immune system activates cancer rejection mechanisms. Understanding the cross-talk signals between immune cells and cancer cells is of fundamental importance for the identification of immuno-oncology therapeutic targets. Results We present a novel method, single-cell Tumor–Host Interaction tool (scTHI), to identify significantly activated ligand–receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand–receptor interactions in glioma using 6 publicly available human glioma datasets encompassing 57,060 gene expression profiles from 71 patients. By leveraging this large-scale collection we show that unexpected cross-talk partners are highly conserved across different datasets in the majority of the tumor samples. This suggests that shared cross-talk mechanisms exist in glioma. Conclusions Our results provide a complete map of the active tumor–host interaction pairs in glioma that can be therapeutically exploited to reduce the immunosuppressive action of the microenvironment in brain tumor.

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Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Simulation Based ◽

Single Cell Rna Sequencing ◽

Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

10.1101/2020.04.01.020586 ◽

2020 ◽

Author(s):

Weimiao Wu ◽

Qile Dai ◽

Yunqing Liu ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing ◽

Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

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EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

Bioinformatics ◽

10.1093/bioinformatics/btz435 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4827-4829 ◽

Cited By ~ 6

Author(s):

Xiao-Fei Zhang ◽

Le Ou-Yang ◽

Shuo Yang ◽

Xing-Ming Zhao ◽

Xiaohua Hu ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Ensemble Learning ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Rna Sequencing ◽

The Individual ◽

Downstream Analysis ◽

Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

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Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequencing data analysis

10.1101/2020.12.11.421883 ◽

2020 ◽

Author(s):

Tamim Abdelaal ◽

Jeroen Eggermont ◽

Thomas Höllt ◽

Ahmed Mahfouz ◽

Marcel J.T. Reinders ◽

...

Keyword(s):

Data Analysis ◽

Single Cell ◽

Rna Sequencing ◽

Visual Exploration ◽

Sequencing Data ◽

Interactive Analysis ◽

Levels Of Detail ◽

Single Cell Rna Sequencing ◽

Downstream Analysis ◽

Low Dimensional

SummaryThe ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells.AvailabilityCytosplore-Transcriptomics can be freely downloaded from [email protected]

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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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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009029 ◽

2021 ◽

Vol 17 (5) ◽

pp. e1009029

Author(s):

Weimiao Wu ◽

Yunqing Liu ◽

Qile Dai ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

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