BIRD: identifying cell doublets via biallelic expression from single cells

ABSTRACT Summary Current technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods raises the risk of cell doublets contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell experimental data focus on doublets of different species, cell types or individuals. In this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power toward doublets’ identification. We present a pipeline called BIallelic Ratio for Doublets (BIRD) that relies on heterologous genetic variations, from single-cell RNA sequencing. For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-seq data from 163 primary fibroblast single cells. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets were verified based on a biallelic expression signal amongst X-chromosome of female fibroblasts. Data from 10X Genomics microfluidics of human peripheral blood cells achieved in average 83% (±3.7%) accuracy, and an area under the curve of 0.88 (±0.04) for a collection of ∼13 300 single cells. BIRD addresses instances of doublets, which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved for high-coverage data from Smart-seq. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage and depth. Supplementary information Supplementary data are available at Bioinformatics online.

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BIRD: Identifying Cell Doublets via Biallelic Expression from Single cells

10.1101/709451 ◽

2019 ◽

Author(s):

Kerem Wainer-Katsir ◽

Michal Linial

Keyword(s):

Single Cell ◽

Genetic Background ◽

Single Cells ◽

Area Under The Curve ◽

Genomic Diversity ◽

Experimental Methodology ◽

Sequence Coverage ◽

High Coverage ◽

Single Experiment ◽

Biallelic Expression

ABSTRACTMotivationCurrent technologies for single-cell transcriptomics allow thousands of cells to be analyzed in a single experiment. The increased scale of these methods led to a higher risk of cell doublets’ contamination. Available tools and algorithms for identifying doublets and estimating their occurrence in single-cell expression data focus on cell doublets from different species, cell types or individuals.ResultsIn this study, we analyze transcriptomic data from single cells having an identical genetic background. We claim that the ratio of monoallelic to biallelic expression provides a discriminating power towards doublets’ identification. We present a pipeline called BIRD (BIallelic Ratio for Doublets) that relies on heterologous genetic variations extracted from single-cell RNA-seq (scRNA-seq). For each dataset, doublets were artificially created from the actual data and used to train a predictive model. BIRD was applied on Smart-Seq data from 163 primary fibroblasts. The model achieved 100% accuracy in annotating the randomly simulated doublets. Bonafide doublets from female-origin fibroblasts were verified by the unexpected biallelic expression from X-chromosome. Data from 10X Genomics microfluidics of peripheral blood cells analyzed by BIRD achieved in average 83% (± 3.7%) accuracy with an area under the curve of 0.88 (± 0.04) for a collection of ∼13,300 single cells.ConclusionsBIRD addresses instances of doublets which were formed from cell mixtures of identical genetic background and cell identity. Maximal performance is achieved with high coverage data. Success in identifying doublets is data specific which varies according to the experimental methodology, genomic diversity between haplotypes, sequence coverage, and depth.

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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

10.1101/173997 ◽

2017 ◽

Cited By ~ 1

Author(s):

Zhun Miao ◽

Ke Deng ◽

Xiaowo Wang ◽

Xuegong Zhang

Keyword(s):

Single Cell ◽

Differential Expression ◽

Negative Binomial ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Binomial Model ◽

Supplementary Data ◽

Rna Seq ◽

Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

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Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

eLife ◽

10.7554/elife.63632 ◽

2021 ◽

Vol 10 ◽

Author(s):

Elliott Swanson ◽

Cara Lord ◽

Julian Reading ◽

Alexander T Heubeck ◽

Palak C Genge ◽

...

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Human Peripheral Blood ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Specific Gene ◽

Test Case ◽

Cell Assays ◽

Paired Measurement

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

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SCELLECTOR: ranking amplification bias in single cells using shallow sequencing

BMC Bioinformatics ◽

10.1186/s12859-020-03858-y ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Vivekananda Sarangi ◽

Alexandre Jourdon ◽

Taejeong Bae ◽

Arijit Panda ◽

Flora Vaccarino ◽

...

Keyword(s):

Single Cell ◽

Multiple Displacement Amplification ◽

Single Cells ◽

Sequencing Data ◽

High Coverage ◽

Amplification Bias ◽

Single Cell Profiling ◽

High Concordance ◽

Human Neuronal Cells

Abstract Background The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. Results We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. Conclusion Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

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Single cell network analysis with a mixture of Nested Effects Models

10.1101/258202 ◽

2018 ◽

Author(s):

Martin Pirkl ◽

Niko Beerenwinkel

Keyword(s):

Single Cell ◽

New Technologies ◽

Single Cells ◽

R Package ◽

Supplementary Information ◽

Data Sets ◽

Cell Network ◽

A Cell ◽

Supplementary Material ◽

Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

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TEA-seq: a trimodal assay for integrated single cell measurement of transcription, epitopes, and chromatin accessibility

10.1101/2020.09.04.283887 ◽

2020 ◽

Cited By ~ 3

Author(s):

Elliott Swanson ◽

Cara Lord ◽

Julian Reading ◽

Alexander T. Heubeck ◽

Adam K. Savage ◽

...

Keyword(s):

Cell Surface ◽

Single Cell ◽

Human Peripheral Blood ◽

Signal To Noise Ratio ◽

Single Cells ◽

Cell Types ◽

Chromatin Accessibility ◽

Specific Gene ◽

Test Case ◽

Cell Assays

AbstractSingle-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to extracellular signals, and human disease states. scATAC-seq has been particularly challenging due to the large size of the human genome and processing artefacts resulting from DNA damage that are an inherent source of background signal. Downstream analysis and integration of scATAC-seq with other single-cell assays is complicated by the lack of clear phenotypic information linking chromatin state and cell type. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases the signal-to-noise ratio and allows simultaneous measurement of cell surface markers: Integrated Cellular Indexing of Chromatin Landscape and Epitopes (ICICLE-seq). We extended this approach using a droplet-based multiomics platform to develop a trimodal assay to simultaneously measure Transcriptomic state (scRNA-seq), cell surface Epitopes, and chromatin Accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

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starmapVR: immersive visualisation of single cell spatial omic data

10.1101/2020.09.01.277079 ◽

2020 ◽

Author(s):

Andrian Yang ◽

Yu Yao ◽

Xiunan Fang ◽

Jianfu Li ◽

Yongyan Xia ◽

...

Keyword(s):

Single Cell ◽

Low Cost ◽

Single Cells ◽

Three Dimensional ◽

Supplementary Information ◽

Phenotypic Properties ◽

Histological Image ◽

Link Type ◽

Molecular Expression ◽

Omic Data

AbstractMotivationAdvances in high throughput single-cell and spatial omic technologies have enabled the profiling of molecular expression and phenotypic properties of hundreds of thousands of individual cells in the context of their two dimensional (2D) or three dimensional (3D) spatial endogenous arrangement. However, current visualisation techniques do not allow for effective display and exploration of the single cell data in their spatial context. With the widespread availability of low-cost virtual reality (VR) gadgets, such as Google Cardboard, we propose that an immersive visualisation strategy is useful.ResultsWe present starmapVR, a light-weight, cross-platform, web-based tool for visualising single-cell and spatial omic data. starmapVR supports a number of interaction methods, such as keyboard, mouse, wireless controller and voice control. The tool visualises single cells in a 3D space and each cell can be represented by a star plot (for molecular expression, phenotypic properties) or image (for single cell imaging). For spatial transcriptomic data, the 2D single cell expression data can be visualised alongside the histological image in a 2.5D format. The application of starmapVR is demonstrated through a series of case studies. Its scalability has been carefully evaluated across different platforms.Availability and implementationstarmapVR is freely accessible at https://holab-hku.github.io/starmapVR, with the corresponding source code available at https://github.com/holab-hku/starmapVR under the open source MIT license.Supplementary InformationSupplementary data are available at Bioinformatics online.

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DART-seq

10.21203/rs.2.1655/v1 ◽

2019 ◽

Author(s):

Iwijn De Vlaminck ◽

Mridusmita Saikia ◽

Philip Burnham ◽

Sara H. Keshavjee ◽

Michael F. Z. Wang ◽

...

Keyword(s):

Single Cell ◽

Human Peripheral Blood ◽

Single Cells ◽

Amplicon Sequencing ◽

Droplet Microfluidics ◽

Cell Repertoire ◽

Rna Sequences ◽

Peripheral Blood Mononuclear ◽

T4 Dna Ligase ◽

B Cell Repertoire

Abstract Here we describe the step-by-step protocol of Droplet Assisted RNA Targeting by single cell sequencing \(DART-seq). DART-seq allows simultaneous multiplexed amplicon sequencing and transcriptome analysis in single cells. Using a simple and quick modification of commercially available barcoded beads we have expanded the application of droplet microfluidics based high throughput single cell RNA sequencing technology to include non-polyadenylated RNA transcripts. Specific probes targeting RNA of interest were attached to the beads using a T4 DNA Ligase based enzymatic reaction.The modified DART-seq beads were then used in standard Drop-seq platform to generate single cell libraries for sequencing. We have applied DART-seq to analyze reovirus RNA sequences in infected murine L cells. As a second application we used DART-seq to investigate the B cell repertoire in human peripheral blood mononuclear cell \(PBMC) samples.

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Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btaa611 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4817-4818 ◽

Cited By ~ 2

Author(s):

Gregor Sturm ◽

Tamas Szabo ◽

Georgios Fotakis ◽

Marlene Haider ◽

Dietmar Rieder ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Large Scale ◽

Single Cells ◽

Cell Receptor ◽

Supplementary Information ◽

Sequencing Data ◽

Seamless Integration ◽

T Cell Phenotypes ◽

Cell Phenotypes

Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

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scds: computational annotation of doublets in single-cell RNA sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btz698 ◽

2019 ◽

Cited By ~ 3

Author(s):

Abha S Bais ◽

Dennis Kostka

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Binary Classification ◽

Single Cells ◽

Computational Cost ◽

Original Data ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Single Cell Rna Sequencing

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a study’s conclusions, and therefore computational strategies for the identification of doublets are needed. Results With scds, we propose two new approaches for in silico doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and, employing a binomial model for the co-expression of pairs of genes, yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from original data. We apply our methods and existing computational doublet identification approaches to four datasets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, at comparably little computational cost. We observe appreciable differences between methods and across datasets and that no approach dominates all others. In summary, scds presents a scalable, competitive approach that allows for doublet annotation of datasets with thousands of cells in a matter of seconds. Availability and implementation scds is implemented as a Bioconductor R package (doi: 10.18129/B9.bioc.scds). Supplementary information Supplementary data are available at Bioinformatics online.

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