Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.

ABSTRACT: A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.OEFinder is freely available at https://github.com/lengning/OEFinderrstewart@morgridge.org or lengning1@gmail.comSupplementary data are available at Bioinformatics online.

SUBMITTER: Leng N

PROVIDER: S-EPMC4848403 | biostudies-other | 2016 May

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.

Leng Ning N Choi Jeea J Chu Li-Fang LF Thomson James A JA Kendziorski Christina C Stewart Ron R

Bioinformatics (Oxford, England) 20160106 9

<h4>Unlabelled</h4>A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFi ...[more]

PMID: 26743507

Similar Datasets

BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis.

Project description:BackgroundRNA sequencing is an indispensable research tool used in a broad range of transcriptome analysis studies. The most common application of RNA Sequencing is differential expression analysis and it is used to determine genetic loci with distinct expression across different conditions. An emerging field called single-cell RNA sequencing is used for transcriptome profiling at the individual cell level. The standard protocols for both of these approaches include the processing of sequencing libraries and result in the generation of count matrices. An obstacle to these analyses and the acquisition of meaningful results is that they require programing expertise. Although some effort has been directed toward the development of user-friendly RNA-Seq analysis analysis tools, few have the flexibility to explore both Bulk and single-cell RNA sequencing.ImplementationBingleSeq was developed as an intuitive application that provides a user-friendly solution for the analysis of count matrices produced by both Bulk and Single-cell RNA-Seq experiments. This was achieved by building an interactive dashboard-like user interface which incorporates three state-of-the-art software packages for each type of the aforementioned analyses. Furthermore, BingleSeq includes additional features such as visualization techniques, extensive functional annotation analysis and rank-based consensus for differential gene analysis results. As a result, BingleSeq puts some of the best reviewed and most widely used packages and tools for RNA-Seq analyses at the fingertips of biologists with no programing experience.AvailabilityBingleSeq is as an easy-to-install R package available on GitHub at https://github.com/dbdimitrov/BingleSeq/.

| S-EPMC7761193 | biostudies-literature

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis.

Project description:As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

| S-EPMC7521718 | biostudies-literature

GUAVA: A Graphical User Interface for the Analysis and Visualization of ATAC-seq Data.

Project description:Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is a powerful genomic technology that is used for the global mapping and analysis of open chromatin regions. However, for users to process and analyze such data they either have to use a number of complicated bioinformatic tools or attempt to use the currently available ATAC-seq analysis software, which are not very user friendly and lack visualization of the ATAC-seq results. Because of these issues, biologists with minimal bioinformatics background who wish to process and analyze their own ATAC-seq data by themselves will find these tasks difficult and ultimately will need to seek help from bioinformatics experts. Moreover, none of the available tools provide complete solution for ATAC-seq data analysis. Therefore, to enable non-programming researchers to analyze ATAC-seq data on their own, we developed a tool called Graphical User interface for the Analysis and Visualization of ATAC-seq data (GUAVA). GUAVA is a standalone software that provides users with a seamless solution from beginning to end including adapter trimming, read mapping, the identification and differential analysis of ATAC-seq peaks, functional annotation, and the visualization of ATAC-seq results. We believe GUAVA will be a highly useful and time-saving tool for analyzing ATAC-seq data for biologists with minimal or no bioinformatics background. Since GUAVA can also operate through command-line, it can easily be integrated into existing pipelines, thus providing flexibility to users with computational experience.

| S-EPMC6056626 | biostudies-literature

CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data.

Project description:A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although there are various tools and methods to correct for batch effects, their performance can vary. Therefore, it is important to understand how batch effects manifest to adjust for them. Here, we systematically explore batch effects across various scRNA-seq datasets according to magnitude, cell type specificity, and complexity. We developed a cell-specific mixing score (cms) that quantifies mixing of cells from multiple batches. By considering distance distributions, the score is able to detect local batch bias as well as differentiate between unbalanced batches and systematic differences between cells of the same cell type. We compare metrics in scRNA-seq data using real and synthetic datasets and whereas these metrics target the same question and are used interchangeably, we find differences in scalability, sensitivity, and ability to handle 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.

| S-EPMC7994321 | biostudies-literature

SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data.

Project description:Single-cell RNA-seq data contain a large proportion of zeros for expressed genes. Such dropout events present a fundamental challenge for various types of data analyses. Here, we describe the SCRABBLE algorithm to address this problem. SCRABBLE leverages bulk data as a constraint and reduces unwanted bias towards expressed genes during imputation. Using both simulation and several types of experimental data, we demonstrate that SCRABBLE outperforms the existing methods in recovering dropout events, capturing true distribution of gene expression across cells, and preserving gene-gene relationship and cell-cell relationship in the data.

| S-EPMC6501316 | biostudies-literature

User-friendly, scalable tools and workflows for single-cell RNA-seq analysis.

Project description: Not available

| S-EPMC8299072 | biostudies-literature

eRNA: A graphic user interface-based tool for RNA sequencing data analysis [microRNA-Seq]

Project description:we performed RNA sequencing analysis using 7 human plasma samples and evaluated efficiency and accuracy of eRNA on miRNA-seq data analysis. We sequenced microRNAs from the 7 human tissue samples with replicates (A1/A8, B2/B9, C3/C10, D4/D11, E5/E12, F6/F13, G7/G14) using NEBNext Multiplex Small RNA library Prep Set from New England Biolab (NEB). After that, we identified miRNAs in these plasma samples against known human matured miRNAs.

2014-06-01 | E-GEOD-53451 | biostudies-arrayexpress

eRNA: A graphic user interface-based tool for RNA sequencing data analysis [mRNA-Seq]

Project description:we performed RNA sequencing analysis using 10 tissue samples from human prostate and evaluated efficiency and accuracy of eRNA on mRNA-seq data analysis. We sequenced mRNAs from the 10 human tissue samples. After that, we identified mRNAs in these samples against known human genes.

2014-06-01 | E-GEOD-53450 | biostudies-arrayexpress

eRNA: A graphic user interface-based tool for RNA sequencing data analysis [microRNA-Seq]

Project description:we performed RNA sequencing analysis using 7 human plasma samples and evaluated efficiency and accuracy of eRNA on miRNA-seq data analysis.

2014-06-01 | GSE53451 | GEO

eRNA: A graphic user interface-based tool for RNA sequencing data analysis [mRNA-Seq]

Project description:we performed RNA sequencing analysis using 10 tissue samples from human prostate and evaluated efficiency and accuracy of eRNA on mRNA-seq data analysis.

2014-06-01 | GSE53450 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data