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Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Phylogenetic tree inference from single-cell RNA sequencing data

Single-cell RNA sequencing technologies enable the large-scale measurement of gene expression profiles at the individual cell level to assess cellular diversity and function. In oncology, leveraging these single-cell transcriptomic data to reconstruct the phylogenetic relationships among cancer cells can provide insights into tumor evolution, metastasis formation, and the development of treatment resistance. However, phylogenetic inference from single-cell RNA sequencing is challenging due to sparse and noisy data and large dataset sizes. We present a novel tree inference method designed for such data that takes reference and alternative read counts of single-nucleotide variants and reconstructs a phylogenetic tree of the sequenced cells via maximum likelihood using a random-scan greedy search. To overcome local optima in the search, our algorithm alternates between two different tree representations: cell lineage trees, where cells are represented by nodes and mutations are attached to edges, and mutation trees, where mutation nodes encode the mutational events and cells are attached to them. Because a local optimum in one tree space generally does not correspond to a local optimum in the other space, we maximize the likelihood by switching between the two tree spaces until convergence is achieved in both. We demonstrate superior performance of our approach on simulated data with complex clonal architectures compared to existing methods. Furthermore, we show its applicability to cancer single-cell RNA sequencing data, which allows us to link evolutionary trajectories of cells to their gene expression profiles.

genomics↗

bioRxiv2020

Red Panda: A novel method for detecting variants in single-cell RNA sequencing

Single-cell sequencing enables us to better understand genetic diseases, such as cancer or autoimmune disorders, which are often affected by changes in rare cells. Currently, no existing software is aimed at identifying single nucleotide variations or micro (1-50bp) insertions and deletions in single-cell RNA sequencing (scRNA-seq) data. Generating high-quality variant data is vital to the study of the aforementioned diseases, among others. In this study, we report the design and implementation of Red Panda, a novel method to accurately identify variants in scRNA-seq data. Variants were called on scRNA-seq data from human articular chondrocytes, mouse embryonic fibroblasts (MEFs), and simulated data stemming from the MEF alignments. Red Panda had the highest Positive Predictive Value at 45.0%, while other tools--FreeBayes, GATK HaplotypeCaller, GATK UnifiedGenotyper, Monovar, and Platypus--ranged from 5.8%-41.53%. From the simulated data, Red Panda had the highest sensitivity at 72.44%. We show that our method provides a novel and improved mechanism to identify variants in scRNA-seq as compared to currently-existing software. AvailabilitySource code freely available under the MIT License at https://github.com/adambioi/red_panda, and is supported on Linux

bioinformatics↗

bioRxiv2021

Differential dropout analysis captures biological variation in single-cell RNA sequencing data

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 differential dropout analysis (DDA) to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that DDA accurately detects biological variation and can assess the relative abundance of transcripts more robustly than methods relying on counts. DDA is available at https://github.com/gbouland/DDA.

bioinformatics↗

bioRxiv2024

MALAT1 expression indicates cell quality in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cell types and tissues. However, empty droplets and poor quality cells are often captured in single cell genomics experiments and need to be removed to avoid cell type interpretation errors. Many automated and manual methods exist to identify poor quality cells or empty droplets, such as minimum RNA count thresholds and comparing the gene expression profile of an individual cell to the overall background RNA expression of the experiment. A versatile approach is to use unbalanced overall RNA splice ratios of cells to identify poor quality cells or empty droplets. However, this approach is computationally intensive, requiring a detailed search through all sequence reads in the experiment to quantify spliced and unspliced reads. We found that the expression level of MALAT1, a non-coding RNA retained in the nucleus and ubiquitously expressed across cell types, is strongly correlated with this splice ratio measure and thus can be used to similarly identify low quality cells in scRNA-seq data. Since it is easy to visualize the expression of a single gene in single-cell maps, MALAT1 expression is a simple cell quality measure that can be quickly used during the cell annotation process to improve the interpretation of cells in tissues of human, mouse and other species with a conserved MALAT1 function.

bioinformatics↗

bioRxiv2020

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

Single-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.

bioinformatics↗

bioRxiv2019

Network-based imputation of dropouts in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values ( dropout imputation). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 12.6% to 48.2% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor https://bioconductor.org/packages/release/bioc/html/ADImpute.html) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells. Author summarySingle-cell RNA-sequencing (scRNA-seq) allows for gene expression to be quantified in individual cells and thus plays a critical role in revealing differences between cells within tissues and characterizing them in healthy and pathological conditions. Because scRNA-seq captures the RNA content of individual cells, lowly expressed genes, for which few RNA molecules are present in the cell, are easily missed. These events are called dropouts and considerably hinder analysis of the resulting data. In this work, we propose to make use of gene-gene relationships, learnt from external and more complete datasets, to estimate the true expression of genes that could not be quantified in a given cell. We show that this approach generally outperforms previously published methods, but also that different genes are better estimated with different methods. To allow the community to use our proposed method and combine it with existing ones, we created the R package ADImpute, available through Bioconductor.

bioinformatics↗

bioRxiv2024

scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis

Single-cell RNA sequencing (scRNA-seq) has transformed biomedical research by enabling transcriptomic analysis at single-cell resolution. Yet, existing computational approaches remain primarily data-driven and lack the ability to integrate research context, limiting their interpretability and impact on hypothesis generation or experimental planning. We present scChat, a large language model (LLM)-powered co-pilot for contextualized scRNA-seq analysis. Unlike conventional pipelines restricted to tasks such as cell type annotation or enrichment analysis, scChat has an interactive, reasoning-based framework. It combines quantitative algorithms with retrieval-augmented generation and a multi-agent architecture to support hypothesis validation, mechanistic interpretation, and next-step experimental design. Through showcase and benchmarking studies, we demonstrate that scChat not only achieves high accuracy in cell type annotation but also provides biologically grounded explanations and contextual insights.

bioinformatics↗

bioRxiv2017

DrImpute: Imputing dropout events in single cell RNA sequencing data

The single cell RNA sequencing (scRNA-seq) technique began a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological noise. Because of the low number of RNA transcriptomes and the stochastic nature of the gene expression pattern, there is a high chance of missing nonzero entries as zero, which are called dropout events. However, many statistical methods used for analyzing scRNA-seq data in cell type identification, visualization, and lineage reconstruction do not model for dropout events. We have developed DrImpute to impute dropout events, and it improves many of the statistical tools used for scRNA-seq analysis that do not account for dropout events. Our numerical studies with real data demonstrate the promising performance of the proposed method, which has been implemented in R.

bioinformatics↗

bioRxiv2019

On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has quickly become an empowering technology to profile the transcriptomes of individual cells on a large scale. Many early analyses of differential expression have aimed at identifying differences between subpopulations, and thus are focused on finding subpopulation markers either in a single sample or across multiple samples. More generally, such methods can compare expression levels in multiple sets of cells, thus leading to cross-condition analyses. However, given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis. For example, one could investigate the condition-specific responses of cell subpopulations measured from patients from each condition; however, it is not clear which statistical framework best handles this situation. In this work, we surveyed the methods available to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated "pseudobulk" data. We developed a flexible simulation platform that mimics both single and multi-sample scRNA-seq data and provide robust tools for multi-condition analysis within the muscat R package.

bioinformatics↗

bioRxiv2026

GraphHDBSCAN*: Graph-based Hierarchical Clustering on High Dimensional Single-cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) is widely used to resolve cellular heterogeneity across thousands to millions of cells. A major challenge is to identify biologically meaningful cell populations while preserving their hierarchical organization, because broad cell types frequently split into more specialized subtypes. However, state-of-the-art approaches mostly focus on flat partitions and ignore the hierarchical structure of single-cell data. Here we introduce GraphHDBSCAN*, a graph-based, hyperparameter-free extension of HDBSCAN* that performs hierarchical density-based clustering on a graph representation of the data, enabling robust recovery of both single-level and hierarchical relationships in high-dimensional and sparse datasets. We evaluate GraphHDBSCAN* across multiple scRNA-seq datasets and show that it recovers biologically meaningful hierarchies that reveal fine-grained structure in complex data, including monocyte subpopulations. In addition, the method yields high-quality flat partitions that outperform widely used community-detection methods.

bioinformatics↗

bioRxiv2020

Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Single-cell RNA sequencing (scRNA-seq) enables the study of cell biology with high resolution. scRNA-seq expression analyses rely on the availability of a high quality annotation of genes in the genome. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, gene annotations often fail to cover the full transcriptome of every cell type at every stage of development, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant information beyond the scope of the best available gene annotation. This is achieved by performing single-cell expression analysis on any region in the genome for which transcriptional products are detected. Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

bioinformatics↗

bioRxiv2020

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within the singleCellTK R package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.

bioinformatics↗

bioRxiv2019

Kinetic foundation of the zero-inflated negative binomial model for single-cell RNA sequencing data

Single-cell RNA sequencing data have complex features such as dropout events, over-dispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are statistically characterized in terms of a zero-inflated negative binomial (ZINB) model. Here we provide a mesoscopic kinetic foundation of the widely used ZINB model based on the biochemical reaction kinetics underlying transcription. Using multiscale modeling and simplification techniques, we show that the ZINB distribution of mRNA abundance and the phenomenon of transcriptional bursting naturally emerge from a three-state stochastic transcription model. We further reveal a nontrivial quantitative relation between dropout events and transcriptional bursting, which provides novel insights into how and to what extent the burst size and burst frequency could reduce the dropout rate. Three different biophysical origins of over-dispersion are also clarified at the single-cell level.

systems biology↗

bioRxiv2016

Comparative analysis of single-cell RNA sequencing methods

Single-cell RNA sequencing (scRNA-seq) offers exciting possibilities to address biological and medical questions, but a systematic comparison of recently developed protocols is still lacking. Here, we generated data from 447 mouse embryonic stem cells using Drop-seq, SCRB-seq, Smart-seq (on Fluidigm C1) and Smart-seq2 and analyzed existing data from 35 mouse embryonic stem cells prepared with CEL-seq. We find that Smart-seq2 is the most sensitive method as it detects the most genes per cell and across cells with the most even coverage, well suited for annotating transcriptomes. However, we also find that unique molecular identifiers (UMIs), available for CEL-seq, Drop-seq and SCRB-seq, reduce the measurement noise considerably, which is most relevant for quantifying transcriptomes. Importantly, we show by power simulations that SCRB-seq and Drop-seq are the most cost-efficient methods for detecting differentially expressed genes. Our analyses offer a solid basis for an informed choice among five prominent scRNA-seq protocols and for future evaluations of protocol improvements.

Genomics↗

bioRxiv2020

Effect of methanol fixation on single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-seq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.

genetics↗

bioRxiv2021

Random Forest Factorization Reveals Latent Structure in Single Cell RNA Sequencing Data

Single-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes.

bioinformatics↗

bioRxiv2018

Self-assembling Manifolds in Single-cell RNA Sequencing Data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is a challenging task. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

bioinformatics↗

bioRxiv2019

Probabilistic index models for testing differential expression in single cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) technologies profile gene expression patterns in individual cells. It is often of interest to test for differential expression (DE) between conditions, e.g. treatment vs control or between cell types. Simulation studies have shown that non-parametric tests, such as the Wilcoxon-rank sum test, can robustly detect significant DE, with better performance than many parametric tools specifically developed for scRNA-seq data analysis. However, these rank tests cannot be used for complex experimental designs involving multiple groups, multiple factors and confounding variables. Further, rank based tests do not provide an interpretable measure of the effect size. We propose a semi-parametric approach based on probabilistic index models (PIM) that form a flexible class of models that generalize classical rank tests. Our method does not rely on strong distributional assumptions and it allows accounting for confounding factors. Moreover, it allows for the estimation of the effect size in terms of a probabilistic index. Real data analysis demonstrate that PIM is capable of identifying biologically meaningful DE. Our simulation studies also show that DE tests succeed well in controlling the false discovery rate at its nominal level, while maintaining good sensitivity as compared to competing methods.

genomics↗