Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE34365
PROVIDER: PRJNA156395 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Highly Parallel Genome-Wide Expression Analysis of Single Mammalian Cells (Optimization)
| PRJNA156397 | ENA
Project description:Assay Performance Metrics for Low RNA Inputs and Single Cells
2012-02-27 | GSE34363 | GEO
Project description:We report performance of six different protocols for small RNAseq library preparation and of a method utilizing sequencing of probes targeting microRNAs (HTG EdgeSeq). Recently, small RNA sequencing (small RNA-seq) has been introduced as a method for quantifying circulating microRNAs (miRNAs) and enabling their global profiling without prior knowledge of target sequences. Despite its great promise, small RNA-seq has not delivered the expected outcomes, particularly due to ligation and PCR bias introduced within the workflow. In this study, we assessed the performance of all existing approaches to the small RNA-seq of miRNAs in plasma samples: original two adapter ligation approach; single adapter ligation with subsequent circularization; polyadenylation; use of randomized adapters; and use of unique molecular identifiers (UMI). Using comprehensive set of metrics, we evaluated each protocol in terms of yield, precision, accuracy, sensitivity, and ability to detect isomiRs. Moreover, we assessed performance of targeted RNA-seq method utilizing hybridization probes across relevant metrics and together with RT-qPCR we used it as a reference for accuracy evaluation. The best results were delivered by targeted RNA-seq outperforming other methods in all relevant parameters. The protocols using randomized adapters or UMIs showed consistent good performance across all of the assessed metrics. In contrast, the polyadenylation approach generated a high percentage of discarded reads and impeded the analysis of isomiRs. The single adapter ligation with subsequent circularization failed to prevent ligation bias and the traditional two adapter ligation approach achieved the worse scores in the majority of tested metrics. To sum, we provide a comprehensive comparison that can serve as a guide for new users interested in analysis of circulating miRNAs and as a reference for further comparative studies.
2020-12-31 | GSE149513 | GEO
Project description:Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
| PRJNA267855 | ENA
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 pairing of CRISPR/Cas9-based gene editing with massively parallel single-cell readouts now enables large-scale lineage tracing. However, the rapid growth in complexity of data from these assays has outpaced our ability to accurately infer phylogenetic relationships. First, we introduce Cassiopeia - a suite of scalable maximum parsimony approaches for tree reconstruction. Second, we provide a simulation framework for evaluating algorithms and exploring lineage tracer design principles. Finally, we generate the most complex experimental lineage tracing dataset to date, 34,557 human cells continuously traced over 15 generations, and use it for benchmarking phylogenetic inference approaches. We show that Cassiopeia outperforms traditional methods by several metrics and under a wide variety of parameter regimes, and provide insight into the principles for the design of improved Cas9-enabled recorders. Together these should broadly enable large-scale mammalian lineage tracing efforts.Cassiopeia and its benchmarking resources are publicly available at https://www.github.com/YosefLab/Cassiopeia.
2020-03-11 | GSE146712 | GEO
Project description:MicroRNA microarray expression dataset used to develop novel and robust quality metrics to objectively assess platform performance of very different technologies.
2013-08-09 | GSE49661 | GEO
Project description:Single-nucleus RNA sequencing (snRNA-seq) was used to profile the transcriptome of 16,015 nuclei in human adult testis. This dataset includes five samples from two different individuals. This dataset is part of a larger evolutionary study of adult testis at the single-nucleus level (97,521 single-nuclei in total) across mammals including 10 representatives of the three main mammalian lineages: human, chimpanzee, bonobo, gorilla, gibbon, rhesus macaque, marmoset, mouse (placental mammals); grey short-tailed opossum (marsupials); and platypus (egg-laying monotremes). Corresponding data were generated for a bird (red junglefowl, the progenitor of domestic chicken), to be used as an evolutionary outgroup.
2021-11-08 | E-MTAB-11063 | biostudies-arrayexpress
Project description:Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel measurements across thousands of genes and gene products. Such high-throughput technologies have been extensively used to carry out genome-wide studies particularly in the context of diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome, and proteome of a single mammalian cell type to obtain a coherent view of the complex interplay between omes has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells, revealing hundreds of unannotated mRNA transcripts, miRNAs, pseudogenes, and noncoding RNAs. Additionally, we carried out a comparative analysis of naïve CD4+ T cells with primary resting memory CD4+ T cells, which have provided novel insights into T cell biology. Overall, our data will serve as a baseline reference of a single pure population of cells for future systems level analysis of other defined cell populations.
2016-04-04 | MSV000079624 | MassIVE