Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

ABSTRACT: Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

PROVIDER: PRJEB39222 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	ERR4318120_1.fastq.gz	Fastqsanger.gz
	ERR4318120_2.fastq.gz	Fastqsanger.gz
	ERR4318121_1.fastq.gz	Fastqsanger.gz
	ERR4318121_2.fastq.gz	Fastqsanger.gz
	ERR4318122_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 28

Similar Datasets

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Project description:Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

| S-EPMC7862875 | biostudies-literature

Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs

Project description:This dataset contains single cell RNAseq and CITE-seq of human hematopoietic progenitors differentiated in vitro from hiPSCs. We provide processed files corresponding to counts and metadata for the RNA-seq and the CITE-seq. For the RNA-seq dataset suspension CD235a-CD43+live cells collected at day 13 of differentiation were analysed. For the CITE-seq dataset, suspension CD235a-live cells and adherent CD31+ and CD31- were analysed. ADT tags for membrane markers are also contained in the dataset. More details are available in Fidanza et al, Blood 2020.

2020-07-06 | E-MTAB-9295 | biostudies-arrayexpress

Machine Learning-based Peptide ID Rescoring

Project description:Machine Learning-based Mascot Rescoring to Reduce False Positives in Tumor Immunopeptidomics using Mass Spectrometry

2024-08-25 | PXD044852 | JPOST Repository

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Transcriptomics]

Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

2019-07-18 | GSE134056 | GEO

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data [Methylomics]

2019-07-18 | GSE134052 | GEO

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

Project description:A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

| PRJNA675152 | ENA

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning for discovery: deciphering RNA splicing logic

| PRJNA822943 | ENA

Identifying transcription factor-DNA interactions using machine learning

Project description:Identifying transcription factor-DNA interactions using machine learning

| PRJNA796028 | ENA

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

2022-10-01 | GSE200096 | 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