Multiplexed droplet single-cell RNA-sequencing using natural genetic variation.
- Authors
- Kang, Hyun Min; Subramaniam, Meena; Targ, Sasha; Nguyen, Michelle; Maliskova, Lenka; McCarthy, Elizabeth; Wan, Eunice; Wong, Simon; Byrnes, Lauren; Lanata, Cristina M; Gate, Rachel E; Mostafavi, Sara; Marson, Alexander; Zaitlen, Noah; Criswell, Lindsey A; Ye, Chun Jimmie
- Year
- 2018
- Journal
- Nature biotechnology
- PMID
- 29227470
- DOI
- 10.1038/nbt.4042
- PMCID
- PMC5784859
Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-Ξ² and perform eQTL analysis on 23 pooled samples.
Demuxlet: demultiplexing and doublet identification from single cell dataa) Pipeline for experimental multiplexing of unrelated individuals, loading onto droplet-based single-cell RNA-sequencing instrument, and computational demultiplexing (demux) and doublet removal using demuxlet. Assuming equal mixing of 8 individuals, b) 4 genetic variants can recover the sample identity of a cell, and c) 87.5% of doublets will contain cells from two different samples.
Performance of demuxleta) Experimental design for equimolar pooling of cells from 8 unrelated samples (S1-S8) into three wells (W1-W3). W1 and W2 contain cells from two disjoint sets of 4 individuals. W3 contains cells from all 8 individuals. b) Demultiplexing single cells in each well recovers the expected individuals. c) Estimates of doublet rates versus previous estimates from mixed species experiments. d) Cell type identity determined by prediction to previously annotated PBMC data. e) t-SNE plot of two individuals (S1 and S5) from different wells are qualitatively concordant.
Inter-individual variability in IFN-Ξ² responsea) t-SNE plot of unstimulated (blue) and IFN-Ξ²-stimulated (red) PBMCs and the estimated cell types. b) Cell type-specific expression in stimulated (left) and unstimulated (right) cells. Differentially expressed genes shown (FDR < 0.05, |log(FC)| > 1). Each column represents cell type-specific expression for each individual from demuxlet. c) Observed variance (y-axis) in mean expression over all PBMCs from each of the 8 individuals versus expected variance (x-axis) over synthetic replicates sampled across all cells (light blue, pink) or replicates matched for cell type proportion (blue, red). d) Cell type proportions for each individual in unstimulated and stimulated cells. e) Correlation between sample replicates in control and stimulated cells. f) Number of significantly variable genes in each cell type and condition.
Genetic control over cell type proportion and gene expression (N=23)a) Observed variance (y-axis) in mean expression over all PBMCs from each individual versus expected variance (x-axis) over synthetic replicates sampled across batch 1 (left, N=8) and batch 3 (right, N=15). b) Association of chr10:3791224 with NK cell type proportions. c) Genome-wide and chromosome 6 Manhattan plots across all major cell types. Horizontal lines correspond to FDR < 0.1 (blue) and FDR < 0.05 (red). d) Q-Q plots across all genes and subsets of previously published eQTLs in relevant cell types are shown for B, cM, and Th populations. e) Notable cis-eQTLs across all major immune cell types are marked with *(FDR < 0.25), ** (FDR < 0.1), and *** (FDR < 0.05). Lack of association is marked with NS (not significant).
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| Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines. | Benaglio P et al. | β | 2022 | β |
| Unbiased integration of single cell transcriptome replicates. | Loza M et al. | β | 2022 | β |
| Using topic modeling to detect cellular crosstalk in scRNA-seq. | Pancheva A et al. | β | 2022 | β |
| Variational autoencoders learn transferrable representations of metabolomics data. | Gomari DP et al. | β | 2022 | β |
| A clinically applicable integrative molecular classification of meningiomas. | Nassiri F et al. | β | 2021 | β |
| A human multi-lineage hepatic organoid model for liver fibrosis. | Guan Y et al. | β | 2021 | β |
| A map of transcriptional heterogeneity and regulatory variation in human microglia. | Young AMH et al. | β | 2021 | β |
| A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. | Chen W et al. | β | 2021 | β |
| Analysis of single-cell RNA sequencing data based on autoencoders. | Tangherloni A et al. | β | 2021 | β |
| An Integrated Electrochemical Nanodevice for Intracellular RNA Collection and Detection in Single Living Cell. | Wang HY et al. | β | 2021 | β |
| A pipeline for RNA-seq based eQTL analysis with automated quality control procedures. | Wang T et al. | β | 2021 | β |
| Application of single-cell transcriptomics to kinetoplastid research. | Briggs EM et al. | β | 2021 | β |
| Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia. | Michael Deans PJ et al. | β | 2021 | β |
| ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. | Granja JM et al. | β | 2021 | β |
| Asc-Seurat: analytical single-cell Seurat-based web application. | Pereira WJ et al. | β | 2021 | β |
| A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases. | Pratella D et al. | β | 2021 | β |
| Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data. | Xi NM et al. | β | 2021 | β |
| Biomarkers of inflammation and repair in kidney disease progression. | Puthumana J et al. | β | 2021 | β |
| BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments. | Huang Y et al. | β | 2021 | β |
| Building a high-quality Human Cell Atlas. | Rozenblatt-Rosen O et al. | β | 2021 | β |
| CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data. | LΓΌtge A et al. | β | 2021 | β |
| Cell type prioritization in single-cell data. | Skinnider MA et al. | β | 2021 | β |
| Coding and non-coding roles of MOCCI (C15ORF48) coordinate to regulate host inflammation and immunity. | Lee CQE et al. | β | 2021 | β |
| Comparison of induced neurons reveals slower structural and functional maturation in humans than in apes. | SchΓΆrnig M et al. | β | 2021 | β |
| Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing. | Tian L et al. | β | 2021 | β |
| Computational principles and challenges in single-cell data integration. | Argelaguet R et al. | β | 2021 | β |
| Confronting false discoveries in single-cell differential expression. | Squair JW et al. | β | 2021 | β |
| Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis. | Wu SZ et al. | β | 2021 | β |
| Cycling cancer persister cells arise from lineages with distinct programs. | Oren Y et al. | β | 2021 | β |
| De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. | Hufford MB et al. | β | 2021 | β |
| Differential analysis of binarized single-cell RNA sequencing data captures biological variation. | Bouland GA et al. | β | 2021 | β |
| Differential gene expression analysis for multi-subject single-cell RNA-sequencing studies with aggregateBioVar. | Thurman AL et al. | β | 2021 | β |
| Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing. | Marshall AS et al. | β | 2021 | β |
| Dissecting the Landscape of Activated CMV-Stimulated CD4+ T Cells in Humans by Linking Single-Cell RNA-Seq With T-Cell Receptor Sequencing. | Lyu M et al. | β | 2021 | β |
| Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling. | Sun B et al. | β | 2021 | β |
| Doublet identification in single-cell sequencing data using <i>scDblFinder</i>. | Germain PL et al. | β | 2021 | β |
| DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data. | Ranjan B et al. | β | 2021 | β |
| EmptyNN: A neural network based on positive and unlabeled learning to remove cell-free droplets and recover lost cells in scRNA-seq data. | Yan F et al. | β | 2021 | β |
| Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction. | Ma W et al. | β | 2021 | β |
| Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design. | Weber LM et al. | β | 2021 | β |
| Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images. | Currant H et al. | β | 2021 | β |
| Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease. | Iqbal F et al. | β | 2021 | β |
| Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits. | Orchard P et al. | β | 2021 | β |
| Inhibition of MET Signaling with Ficlatuzumab in Combination with Chemotherapy in Refractory AML: Clinical Outcomes and High-Dimensional Analysis. | Wang VE et al. | β | 2021 | β |
| Inhibition of Podocytes DPP4 Activity Is a Potential Mechanism of Lobeliae Chinensis Herba in Treating Diabetic Kidney Disease. | Wang X et al. | β | 2021 | β |
| Interferon-Gamma-Producing CD8<sup>+</sup> Tissue Resident Memory T Cells Are a Targetable Hallmark of Immune Checkpoint Inhibitor-Colitis. | Sasson SC et al. | β | 2021 | β |
| <i>satuRn</i>:Β Scalable analysis of differential transcript usageΒ for bulk and single-cell RNA-sequencing applications. | Gilis J et al. | β | 2021 | β |
| Iterative single-cell multi-omic integration using online learning. | Gao C et al. | β | 2021 | β |
| Leveraging Single-Cell Approaches in Cancer Precision Medicine. | Nath A et al. | β | 2021 | β |
| Microfluidics applications for high-throughput single cell sequencing. | Zhou WM et al. | β | 2021 | β |
| Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome. | Shen H et al. | β | 2021 | β |
| Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. | Nathan A et al. | β | 2021 | β |
| Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19. | Wilk AJ et al. | β | 2021 | β |
| Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution. | Cheng J et al. | β | 2021 | β |
| Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics. | Penter L et al. | β | 2021 | β |
| Network Modeling in Biology: Statistical Methods for Gene and Brain Networks. | Wang YXR et al. | β | 2021 | β |
| No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells. | McGinnis CS et al. | β | 2021 | β |
| noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise. | Moutsopoulos I et al. | β | 2021 | β |
| Optimizing expression quantitative trait locus mapping workflows for single-cell studies. | Cuomo ASE et al. | β | 2021 | β |
| Point-of-care DNA testing by automatically and sequentially performing extraction, amplification and identification in a closed-type cassette. | Dong T et al. | β | 2021 | β |
| POLYseq: A poly(Ξ²-amino ester)-based vector for multifunctional cellular barcoding. | Dunn A et al. | β | 2021 | β |
| Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation. | Jerber J et al. | β | 2021 | β |
| Prioritization of cell types responsive to biological perturbations in single-cell data with Augur. | Squair JW et al. | β | 2021 | β |
| Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis. | Xi NM et al. | β | 2021 | β |
| Robust integration of multiple single-cell RNA sequencing datasets using a single reference space. | Liu Y et al. | β | 2021 | β |
| Sample processing and single cell RNA-sequencing of peripheral blood immune cells from COVID-19 patients. | Yao C et al. | β | 2021 | β |
| Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. | Mimitou EP et al. | β | 2021 | β |
| SCITO-seq: single-cell combinatorial indexed cytometry sequencing. | Hwang B et al. | β | 2021 | β |
| scMC learns biological variation through the alignment of multiple single-cell genomics datasets. | Zhang L et al. | β | 2021 | β |
| scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies. | Schmid KT et al. | β | 2021 | β |
| scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets. | Liu H et al. | β | 2021 | β |
| scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing. | Wilson GW et al. | β | 2021 | β |
| Semisupervised adversarial neural networks for single-cell classification. | Kimmel JC et al. | β | 2021 | β |
| Significance of intratumoral infiltration of B cells in cancer immunotherapy: From a single cell perspective. | Gu S et al. | β | 2021 | β |
| Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility. | Wang K et al. | β | 2021 | β |
| Single Cell Analysis of Blood Mononuclear Cells Stimulated Through Either LPS or Anti-CD3 and Anti-CD28. | Lawlor N et al. | β | 2021 | β |
| Single-cell analysis of cell fate bifurcation in the chordate Ciona. | Winkley KM et al. | β | 2021 | β |
| Single-Cell and Bulk RNA-Sequencing Reveal Differences in Monocyte Susceptibility to Influenza A Virus Infection Between Africans and Europeans. | O'Neill MB et al. | β | 2021 | β |
| Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells. | Neavin D et al. | β | 2021 | β |
| Single-Cell Mapping of Progressive Fetal-to-Adult Transition in Human Naive T Cells. | Bunis DG et al. | β | 2021 | β |
| Single Cell RNA Sequencing in Autoimmune Inflammatory Rheumatic Diseases: Current Applications, Challenges and a Step Toward Precision Medicine. | Kuret T et al. | β | 2021 | β |
| Single-cell RNA sequencing in cancer research. | Zhang Y et al. | β | 2021 | β |
| Single-Cell Toolkits Opening a New Era for Cell Engineering. | Lee S et al. | β | 2021 | β |
| Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain. | Tran MN et al. | β | 2021 | β |
| Somatic mutations and single-cell transcriptomes reveal the root of malignant rhabdoid tumours. | Custers L et al. | β | 2021 | β |
| splatPop: simulating population scale single-cell RNA sequencing data. | Azodi CB et al. | β | 2021 | β |
| Stem cell-derived neurons reflect features of protein networks, neuropathology, and cognitive outcome of their aged human donors. | Lagomarsino VN et al. | β | 2021 | β |
| The effect of low-dose IL-2 and Treg adoptive cell therapy in patients with type 1 diabetes. | Dong S et al. | β | 2021 | β |
| Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. | Liu C et al. | β | 2021 | β |
| Transcriptional enhancers and their communication with gene promoters. | Ray-Jones H et al. | β | 2021 | β |
| Transcriptional Profiling During Neural Conversion. | Afeworki Y et al. | β | 2021 | β |
| Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. | Maynard KR et al. | β | 2021 | β |
| Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19. | van der Wijst MGP et al. | β | 2021 | β |
| Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. | Datlinger P et al. | β | 2021 | β |
| VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. | Seninge L et al. | β | 2021 | β |
| Whole-organism eQTL mapping at cellular resolution with single-cell sequencing. | Ben-David E et al. | β | 2021 | β |
| A Multiplexed Barcodelet Single-Cell RNA-Seq Approach Elucidates Combinatorial Signaling Pathways that Drive ESC Differentiation. | Yeo GHT et al. | β | 2020 | β |
| Ancestry-agnostic estimation of DNA sample contamination from sequence reads. | Zhang F et al. | β | 2020 | β |
| An entropy-based metric for assessing the purity of single cell populations. | Liu B et al. | β | 2020 | β |
| A Random Matrix Theory Approach to Denoise Single-Cell Data. | Aparicio L et al. | β | 2020 | β |
| A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits. | Dahl A et al. | β | 2020 | β |
| A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. | Slyper M et al. | β | 2020 | β |
| A single-cell atlas of the peripheral immune response in patients with severe COVID-19. | Wilk AJ et al. | β | 2020 | β |
| Autism spectrum disorder at the crossroad between genes and environment: contributions, convergences, and interactions in ASD developmental pathophysiology. | Cheroni C et al. | β | 2020 | β |
| Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. | Mereu E et al. | β | 2020 | β |
| BIRD: identifying cell doublets via biallelic expression from single cells. | Wainer-Katsir K et al. | β | 2020 | β |
| Breathing fresh air into respiratory research with single-cell RNA sequencing. | Alexander MJ et al. | β | 2020 | β |
| Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. | Kotliarov Y et al. | β | 2020 | β |
| Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. | McCarthy DJ et al. | β | 2020 | β |
| Cell atlas of the foetal human heart and implications for autoimmune-mediated congenital heart block. | Suryawanshi H et al. | β | 2020 | β |
| Clinical value of DNA methylation markers in autoimmune rheumatic diseases. | Ballestar E et al. | β | 2020 | β |
| Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing. | Senabouth A et al. | β | 2020 | β |
| Comprehensive aptamer-based screening identifies a spectrum of urinary biomarkers of lupus nephritis across ethnicities. | Stanley S et al. | β | 2020 | β |
| Conditional out-of-distribution generation for unpaired data using transfer VAE. | Lotfollahi M et al. | β | 2020 | β |
| CSS: cluster similarity spectrum integration of single-cell genomics data. | He Z et al. | β | 2020 | β |
| Decontamination of ambient RNA in single-cell RNA-seq with DecontX. | Yang S et al. | β | 2020 | β |
| Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. | Li X et al. | β | 2020 | β |
| Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign. | Chen S et al. | β | 2020 | β |
| DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data. | Simon LM et al. | β | 2020 | β |
| Eleven grand challenges in single-cell data science. | LΓ€hnemann D et al. | β | 2020 | β |
| Estimating the Allele-Specific Expression of SNVs From 10Γ Genomics Single-Cell RNA-Sequencing Data. | M PN et al. | β | 2020 | β |
| Functional CRISPR dissection of gene networks controlling human regulatory T cell identity. | Schumann K et al. | β | 2020 | β |
| Functionally Diverse Inflammatory Responses in Peripheral and Liver Monocytes in Alcohol-Associated Hepatitis. | Kim A et al. | β | 2020 | β |
| GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing. | Xin H et al. | β | 2020 | β |
| Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant. | Malone AF et al. | β | 2020 | β |
| Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. | Gehring J et al. | β | 2020 | β |
| High-throughput screening by droplet microfluidics: perspective into key challenges and future prospects. | Payne EM et al. | β | 2020 | β |
| hubViz: A Novel Tool for Hub-centric Visualization. | Nam JH et al. | β | 2020 | β |
| Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection. | Kazer SW et al. | β | 2020 | β |
| Jointly defining cell types from multiple single-cell datasets using LIGER. | Liu J et al. | β | 2020 | β |
| LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS. | Amodio M et al. | β | 2020 | β |
| <i>Drosophila</i> Voltage-Gated Sodium Channels Are Only Expressed in Active Neurons and Are Localized to Distal Axonal Initial Segment-like Domains. | Ravenscroft TA et al. | β | 2020 | β |
| mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data. | Kaspi A et al. | β | 2020 | β |
| Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. | McFarland JM et al. | β | 2020 | β |
| muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. | Crowell HL et al. | β | 2020 | β |
| Navigating differential structures in complex networks. | Portes LL et al. | β | 2020 | β |
| Needle in a Haystack: The NaΓ―ve Repertoire as a Source of T Cell Receptors for Adoptive Therapy with Engineered T Cells. | D'Ippolito E et al. | β | 2020 | β |
| One for All: A Pooled Approach to Classify Functional Impacts of Multiple Mutations. | Jourdon A et al. | β | 2020 | β |
| Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis. | Mandric I et al. | β | 2020 | β |
| Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity. | Kinker GS et al. | β | 2020 | β |
| Past, Present, and Future of Affinity-based Cell Separation Technologies. | Bacon K et al. | β | 2020 | β |
| pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools. | Germain PL et al. | β | 2020 | β |
| Recent Advances in Droplet Microfluidics. | Ding Y et al. | β | 2020 | β |
| scds: computational annotation of doublets in single-cell RNA sequencing data. | Bais AS et al. | β | 2020 | β |
| SCelVis: exploratory single cell data analysis on the desktop and in the cloud. | Obermayer B et al. | β | 2020 | β |
| Single-cell network biology for resolving cellular heterogeneity in human diseases. | Cha J et al. | β | 2020 | β |
| Single cell profiling of capillary blood enables out of clinic human immunity studies. | Dobreva T et al. | β | 2020 | β |
| Single Cell RNA Sequencing in Atherosclerosis Research. | Williams JW et al. | β | 2020 | β |
| Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression. | Cuomo ASE et al. | β | 2020 | β |
| Single-Cell Transcriptome Analysis of Colon Cancer Cell Response to 5-Fluorouracil-Induced DNA Damage. | Park SR et al. | β | 2020 | β |
| Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma. | Seumois G et al. | β | 2020 | β |
| Single-cell transcriptomics in cancer: computational challenges and opportunities. | Fan J et al. | β | 2020 | β |
| Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning. | Bernstein NJ et al. | β | 2020 | β |
| Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes. | Heaton H et al. | β | 2020 | β |
| Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis. | Russkikh N et al. | β | 2020 | β |
| The Application of Single-Cell RNA Sequencing in Vaccinology. | NoΓ© A et al. | β | 2020 | β |
| The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. | Rozenblatt-Rosen O et al. | β | 2020 | β |
| The Impact of Single-Cell Genomics on Adipose Tissue Research. | Deutsch A et al. | β | 2020 | β |
| The single-cell eQTLGen consortium. | van der Wijst M et al. | β | 2020 | β |
| Tools and Concepts for Interrogating and Defining Cellular Identity. | McKinley KL et al. | β | 2020 | β |
| Transcriptional Programs of Circuit Assembly in the Drosophila Visual System. | Kurmangaliyev YZ et al. | β | 2020 | β |
| V-SVA: an R Shiny application for detecting and annotating hidden sources of variation in single-cell RNA-seq data. | Lawlor N et al. | β | 2020 | β |
| A Bioinformatic Toolkit for Single-Cell mRNA Analysis. | BaΓler K et al. | β | 2019 | β |
| A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications. | Peng H et al. | β | 2019 | β |
| A test metric for assessing single-cell RNA-seq batch correction. | BΓΌttner M et al. | β | 2019 | β |
| CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics. | Guo C et al. | β | 2019 | β |
| ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions. | Gao X et al. | β | 2019 | β |
| Current best practices in single-cell RNA-seq analysis: a tutorial. | Luecken MD et al. | β | 2019 | β |
| Deciphering Brain Complexity Using Single-cell Sequencing. | Mu Q et al. | β | 2019 | β |
| Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing. | Choi YH et al. | β | 2019 | β |
| DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data. | DePasquale EAK et al. | β | 2019 | β |
| DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. | McGinnis CS et al. | β | 2019 | β |
| Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. | Hie B et al. | β | 2019 | β |
| FluoroCellTrack: An algorithm for automated analysis of high-throughput droplet microfluidic data. | Vaithiyanathan M et al. | β | 2019 | β |
| Functional interpretation of single cell similarity maps. | DeTomaso D et al. | β | 2019 | β |
| Genotype-free demultiplexing of pooled single-cell RNA-seq. | Xu J et al. | β | 2019 | β |
| Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. | Hie B et al. | β | 2019 | β |
| Heterogeneity and plasticity in healthy and atherosclerotic vasculature explored by single-cell sequencing. | van Kuijk K et al. | β | 2019 | β |
| How mutations express themselves in blood-cell production. | Raju S et al. | β | 2019 | β |
| Immunology Driven by Large-Scale Single-Cell Sequencing. | Gomes T et al. | β | 2019 | β |
| Integrative single-cell analysis. | Stuart T et al. | β | 2019 | β |
| Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics. | Ludwig LS et al. | β | 2019 | β |
| Mapping human cell phenotypes to genotypes with single-cell genomics. | Camp JG et al. | β | 2019 | β |
| Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. | Shin D et al. | β | 2019 | β |
| MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. | McGinnis CS et al. | β | 2019 | β |
| Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. | Gaublomme JT et al. | β | 2019 | β |
| Organoid single-cell genomic atlas uncovers human-specific features of brain development. | Kanton S et al. | β | 2019 | β |
| Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity | Kinker GS et al. | β | 2019 | β |
| Predicting Cell Populations in Single Cell Mass Cytometry Data. | Abdelaal T et al. | β | 2019 | β |
| Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. | Aran D et al. | β | 2019 | β |
| scGen predicts single-cell perturbation responses. | Lotfollahi M et al. | β | 2019 | β |
| Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. | Wolock SL et al. | β | 2019 | β |
| Single-Cell High-Throughput Technologies in Cerebrospinal Fluid Research and Diagnostics. | Lanz TV et al. | β | 2019 | β |
| Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity. | Zeng T et al. | β | 2019 | β |
| Single-Cell RNA Sequencing of the Cardiovascular System: New Looks for Old Diseases. | Chaudhry F et al. | β | 2019 | β |
| Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer. | Fasterius E et al. | β | 2019 | β |
| Single-cell technologies - studying rheumatic diseases one cell at a time. | Cheung P et al. | β | 2019 | β |
| Single cell transcriptional signatures of the human placenta in term and preterm parturition. | Pique-Regi R et al. | β | 2019 | β |
| Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. | Clarke J et al. | β | 2019 | β |
| Single-cell Transcriptomics and Solid Organ Transplantation. | Malone AF et al. | β | 2019 | β |
| SNV identification from single-cell RNA sequencing data. | Schnepp PM et al. | β | 2019 | β |
| Targeting individual cells by barcode in pooled sequence libraries. | Ranu N et al. | β | 2019 | β |
| Transcriptomics and single-cell RNA-sequencing. | Chambers DC et al. | β | 2019 | β |
| Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference. | Huang Y et al. | β | 2019 | β |
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| Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. | Stoeckius M et al. | β | 2018 | β |
| Characterization of human mosaic Rett syndrome brain tissue by single-nucleus RNA sequencing. | Renthal W et al. | β | 2018 | β |
| Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. | Freytag S et al. | β | 2018 | β |
| Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments. | Bloom JD | β | 2018 | β |
| Experimental Considerations for Single-Cell RNA Sequencing Approaches. | Nguyen QH et al. | β | 2018 | β |
| Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of <i>ERAP2</i> transcripts under balancing selection. | Ye CJ et al. | β | 2018 | β |
| Genetic determinants of co-accessible chromatin regions in activated T cells across humans. | Gate RE et al. | β | 2018 | β |
| Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. | Fan J et al. | β | 2018 | β |
| Microfluidic and Paper-Based Devices for Disease Detection and Diagnostic Research. | Campbell JM et al. | β | 2018 | β |
| Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues. | Birnbaum KD | β | 2018 | β |
| Quantitative single-cell transcriptomics. | Ziegenhain C et al. | β | 2018 | β |
| Single Cell Gene Expression to Understand the Dynamic Architecture of the Heart. | Massaia A et al. | β | 2018 | β |
| Single-cell genomics to guide human stem cell and tissue engineering. | Camp JG et al. | β | 2018 | β |
| Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells. | Gupta I et al. | β | 2018 | β |
| Single-cell reconstruction of the early maternal-fetal interface in humans. | Vento-Tormo R et al. | β | 2018 | β |
| Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs. | van der Wijst MGP et al. | β | 2018 | β |
| Systems Immunology: Learning the Rules of the Immune System. | Villani AC et al. | β | 2018 | β |
| Toward mapping the human body at a cellular resolution. | Roy AL et al. | β | 2018 | β |
| Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. | Lafzi A et al. | β | 2018 | β |
| Using Gene Expression to Annotate Cardiovascular GWAS Loci. | Heinig M | β | 2018 | β |