Validation of noise models for single-cell transcriptomics
Transcriptomics Dataset
FAIR Metrics
0 evaluations
General Information
AccessionGSE54695
TitleValidation of noise models for single-cell transcriptomics
DescriptionSingle-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst single cells. Here we identify two major sources of technical variability, sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression variability.
Landing Pagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54695
RepositoryGene Expression Omnibus
Tools used to analyze the dataset
Dataset Accession
ARCHS4
Published September 2017
ARCHS4 provides access to gene counts from HiSeq 2000 and HiSeq 2500 platforms for human and mouse experiments from GEO ...
4645
Clustergrammer
Published October 2017
Web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable heatmaps
4645
Canned Analyses generated with the dataset
Filters  
Dataset Accession
Tool Name
Interactive heatmap visualization of RNA-seq dataset GSE54695
ARCHS4, Clustergrammer
Highly interactive web-based heatmap visualization of the top 500 most variable genes in t...
FAIR Evaluation
Sorry, you must be signed in to evaluate this object.