Systematic comparative analysis of single-nucleotide variants detection methods from single-cell RNA sequencing data
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PROVIDER: EGAS00001003883 | EGA |
REPOSITORIES: EGA
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Project description: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 cell types (or clusters), and thus are focused on finding markers for cell populations 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 specific immune cell subsets across cells measured from patients within 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 R package.
2019-08-11 | E-MTAB-8192 | biostudies-arrayexpress
Project description:Chromosome copy number variations (CNV) are a near-universal feature of cancer however their specific effects on cellular function are poorly understood. Single-cell RNA sequencing (scRNA-seq) can reveal cellular gene expression however cannot directly link this to CNVs. Here we report scRNA-seq normalization methods that improve inter-cellular expression alignment, increasing the sensitivity of scRNA-seq for CNV detection. We additionally report sciCNV, a tool for inferring CNVs from scRNA-seq data. Together, these tools enable simultaneous profiling of DNA CNV and RNA within single cells. We apply these tools to multiple myeloma and examine the roles of pan cancer CNVs +8q and +1q. Cells with +8q23-24 upregulate MYC, MYC-target genes, mRNA translation, protein synthesis capacity and DEPTOR; and show reduced transcriptome size. Cells with +1q21-44 reconfigure translation to suppress unfolded protein stress and show increased proliferation, oxidative phosphorylation and MCL1. Overall, we report scRNA-seq methods that can reveal the function of CNVs in cancer.
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Project description:The growth plate, which comprises sequentially differentiated cell layers, is a critical structure for bone elongation and regeneration. Although several key regulators in growth plate development have been identified using primarily genetic perturbation, the systematic understanding is still limited. Here we used single cell RNA-seq to interrogate gene expression profiles of 217 single cells from growth plates, and developed the bioinfromatics pipeline Sinova to de-novo reconstruct physiological growth plate development in both temporal and spatial high-resolution. Our unsupervised model not only confirmed prior knowledge but also enabled systematic discovery of novel genes, potential signal pathways and surface markers CD9/CD200 to precisely depict the development. Sinova further identified effective transcriptional factor portfolio directing growth plate maturation, which was cross-validated experimentally using an in-vitro EGFP-Col10a screening system. Our case demonstrated systematic reconstructing of molecular cascades of a developmental process from single-cell profiling, and the workflow is readily transferable to other physiological scenarios. 217 single-cell RNA-seq for cell isolated from mouse growth plate at postnatal day7
2016-06-14 | E-GEOD-76157 | biostudies-arrayexpress
Project description:A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented the \texttt{cms}, as well as related metrics to detect batch effects or measure structure preservation, in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.
2021-01-31 | E-MTAB-9916 | biostudies-arrayexpress
Project description:The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and spike-ins comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. This SuperSeries is composed of the following subset Series: GSE9732: Spike-in Experiment for ChIP-chip Simulation GSE9842: Systematic evaluation of variability in simulated ChIP-chip experiments GSE9848: Systematic evaluation of variability in simulated ChIP-chip experiments Kevin_Encode GSE9849: Systematic evaluation of variability in simulated ChIP-chip experiments Myles_Encode GSE10004: ENCODE Spike-In, Yale Group GSE10076: ENCODE spikein, amplified DNA samples, NimbleGen arrays GSE10090: ENCODE spikein, nonamplified DNA samples, NimbleGen arrays GSE10112: Systematic evaluation of variability in simulated ChIP-chip experiments Keywords: SuperSeries For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series
2010-09-30 | E-GEOD-10114 | biostudies-arrayexpress
Project description:Single-cell RNA-sequencing (scRNA-seq) is valuable for analyzing cellular heterogeneity. Cell composition accuracy is critical for analyzing cell-cell interaction networks from scRNA-seq data because cell abundancy might affect to the network. We developed terminator-assisted solid-phase cDNA amplification and sequencing (TAS-Seq), a scRNA-seq method that relied on terminator, terminal transferase, and nanowell/beads-based scRNA-seq platform, that could acquire scRNA-seq data with higher correlation with flow-cytometric data, gene-detection sensitivity, and robustness than widely-used methods.
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Project description:The B-cell receptor (BCR) enables individual B cells to identify diverse antigens, including bacterial and viral proteins. While advances in RNA-seq have enabled high throughput profiling of transcript expression in single cells, the unique task of assembling the full-length heavy and light chain sequences from single cell RNA-sequencing (scRNA-seq) in B cells has been largely unstudied. We developed a new software tool, BASIC, which allows investigators to use scRNA-seq for assembling BCR sequences at single cell level. To demonstrate the utility of our software, we subjected single B cells from a human donor to scRNA-seq, assembled the full-length heavy and the light chains, and experimentally confirmed these results by using single cell primer based nested PCRs and Sanger sequencing.
2016-06-02 | E-MTAB-4745 | biostudies-arrayexpress
Project description:The development of temporal single-cell RNA-seq (scRNA-seq) assay enabled us to systemically investigate the effects of various types of perturbations in a time course with single-cell resolution. However, the existing temporal scRNA-seq technologies have certain limitations in reliability, detection efficacy and detection diversity. In the current study, we develop scBiopsy-seq assay that combines a synergistic electroosmosis-electrophoresis extraction method for efficient RNA extraction and digital microfluidics with contamination-isolated hydrophobic interface for high-performance sample processing. scBiopsy-seq extracts the cytoplasm at well-controlled volume to detect >10K genes per extraction with 90% successful rate. Functional enrichment revealed that the genes robustly detected by scBiopsy-seq were associated to diverse biological processes, demonstrating its superb diversity of detection. scBiopsy-seq can perform the sequential extraction of the cytoplasm from the same cell multiple times, which allows us to associate the cell phenotypic responses with its transcriptic dynamics. We employed scBiopsy-seq to analyze the temporal response to BRD4 inhibition with single-cell resolution of SW480 cells and identified the key roles of fatty acid oxidation in this biological process. scBiopsy-seq with comparable data quality with scRNA-seq will dramatically expand the application of temporal scRNA-seq towards a broader spectrum of biological research.
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