Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming I

ABSTRACT: Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming I

PROVIDER: PRJNA417182 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR6257340.fastq.gz	Fastqsanger.gz
	SRR6257350.fastq.gz	Fastqsanger.gz
	SRR6257341.fastq.gz	Fastqsanger.gz
	SRR6257351.fastq.gz	Fastqsanger.gz
	SRR6257342.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 22

Similar Datasets

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Project description:Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

| PRJNA505909 | ENA

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming III

Project description:Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming III

| PRJNA505874 | ENA

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming II

Project description:Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming II

| PRJNA476566 | ENA

Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Project description:Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

| S-EPMC6402800 | biostudies-literature

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-01-31 | GSE122662 | GEO

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming III

Project description:We collected triplicate samples at day 15 under serum condition, generated single cell suspensions and performed scRNA-Seq (10X Genomics). We also collected samples from a bulk population and performed bulk RNA-seq (Quantseq, Lexogen).

2019-01-31 | GSE122653 | GEO

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming II

Project description:We collected duplicate samples at the various time points, generated single cell suspensions and performed scRNA-Seq. We also collected samples from established iPSC lines reprogrammed from the same MEFs, maintained in either 2i or serum conditions. Overall, we profiled ~250,000 cells.

2019-01-31 | GSE115943 | GEO

Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming I

Project description:We collected single or duplicate samples at the various time points, generated single cell suspensions and performed scRNA-Seq. We also collected samples from established iPSC lines reprogrammed from the same MEFs, maintained in either 2i or serum conditions. Overall, we profiled 68,339 cells to an average depth of 38,462 reads per cell. After discarding cells with less than 1,000 genes detected, we retained a total of 65,781 cells, with a median of 2,398 genes and 7,387 unique transcripts per cell.

2019-01-31 | GSE106340 | GEO

Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of reprogramming outcome

Project description:Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of reprogramming outcome

| PRJNA415061 | ENA

Tracking Early Mammalian Organogenesis – Prediction and Validation of Differentiation Trajectories at Whole Organism Scale

Project description:This project is based on the extension of the single cell transcriptomics atlas of mouse development towards E9.5. Inference of cell differentiation trajectories was carried out using Waddington-Optimal Transport, and the resulting computational reconstruction was contrasted with grafting experiments as well as complementary scRNA-seq experiments.

2023-01-01 | E-MTAB-11763 | biostudies-arrayexpress

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