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Project description:MotivationIn single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data.ResultsWe introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features.Availability and implementationSCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9825775 | biostudies-literature
Project description:PurposeCorneal epithelial homeostasis is maintained by coordinated gene expression across distinct cell populations, but the gene regulatory programs underlying this cellular diversity remain to be characterized. Here we applied single-cell multi-omics analysis to delineate the gene regulatory profile of mouse corneal epithelial cells under normal homeostasis.MethodsSingle cells isolated from the cornea epithelium (with marginal conjunctiva) of adult mice were subjected to scRNA-seq and scATAC-seq using the 10×Genomics platform. Cell types were clustered by the graph-based visualization method uniform manifold approximation and projection and unbiased computational informatics analysis. The scRNA-seq and scATAC-seq datasets were integrated following the integration pipeline described in ArchR and Seurat.ResultsWe characterized diverse corneal epithelial cell types based on gene expression signatures and chromatin accessibility. We found that cell type-specific accessibility regions were mainly located at distal regions, suggesting essential roles of distal regulatory elements in determining corneal epithelial cell diversity. Trajectory analyses revealed a continuum of cell state transition and higher coordination between transcription factor (TF) motif accessibility and gene expression during corneal epithelial cell differentiation. By integrating transcriptomic and chromatin accessibility analysis, we identified cell type-specific and shared gene regulation programs. We also uncovered critical TFs driving corneal epithelial cell differentiation, such as nuclear factor I (NFI) family members, Rarg, Elf3. We found that nuclear factor-κB (NF-κB) family members were positive TFs in limbal cells and some superficial cells, but they were involved in regulating distinct biological processes.ConclusionsOur study presents a comprehensive gene regulatory landscape of mouse cornea epithelial cells, and provides valuable foundations for future investigation of corneal epithelial homeostasis in the context of cornea pathologies and regenerative medicine.
| S-EPMC10043503 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-02-07 | GSE144880 | GEO
Project description:Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a 'direction of change' and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.
| S-EPMC7822509 | biostudies-literature
Project description:Single-cell multiomics data continues to grow at an unprecedented pace. Although several methods have demonstrated promising results in integrating several data modalities from the same tissue, the complexity and scale of data compositions present in cell atlases still pose a challenge. Here, we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.
| S-EPMC9186323 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-05-19 | E-GEOD-67123 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-07-13 | GSE207768 | GEO
Project description:The assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) is an inexpensive protocol for measuring open chromatin regions. ATAC-seq is also relatively simple and requires fewer cells than many other high-throughput sequencing protocols. Therefore, it is tractable in numerous settings where other high throughput assays are challenging to impossible. Hence it is important to understand the limits of what can be inferred from ATAC-seq data. In this work, we leverage ATAC-seq to predict the presence of nascent transcription. Nascent transcription assays are the current gold standard for identifying regions of active transcription, including markers for functional transcription factor (TF) binding. We combine mapped short reads from ATAC-seq with the underlying peak sequence, to determine regions of active transcription genome-wide. We show that a hybrid signal/sequence representation classified using recurrent neural networks (RNNs) can identify these regions across different cell types.
| S-EPMC7192442 | biostudies-literature
Project description:mouse RNA-seq, human RNA-seq, ATAC-seq, and scRNA-seq analyses data
| PRJNA932220 | ENA
Project description:BackgroundSingle-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin accessibility in single nuclei. These two data types provide complementary information for deciphering cell types and states. However, when analyzed individually, they sometimes produce conflicting results regarding cell type/state assignment. The power is compromised since the two modalities reflect the same underlying biology. Recently, it has become possible to measure both gene expression and chromatin accessibility from the same nucleus. Such paired data enable the direct modeling of the relationships between the two modalities. Given the availability of the vast amount of single-modality data, it is desirable to integrate the paired and unpaired single-modality datasets to gain a comprehensive view of the cellular complexity.ResultsWe benchmark nine existing single-cell multi-omic data integration methods. Specifically, we evaluate to what extent the multiome data provide additional guidance for analyzing the existing single-modality data, and whether these methods uncover peak-gene associations from single-modality data. Our results indicate that multiome data are helpful for annotating single-modality data. However, we emphasize that the availability of an adequate number of nuclei in the multiome dataset is crucial for achieving accurate cell type annotation. Insufficient representation of nuclei may compromise the reliability of the annotations. Additionally, when generating a multiome dataset, the number of cells is more important than sequencing depth for cell type annotation.ConclusionsSeurat v4 is the best currently available platform for integrating scRNA-seq, snATAC-seq, and multiome data even in the presence of complex batch effects.
| S-EPMC10594700 | biostudies-literature