Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

GEO accession GSE243881 is currently private and is scheduled to be released on Sep 01, 2024.

ABSTRACT: GEO accession GSE243881 is currently private and is scheduled to be released on Sep 01, 2024.

PROVIDER: PRJNA1020404 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis

Project description:An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis

| PRJNA1033622 | ENA

An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [WGS]

Project description:An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [WGS]

| PRJNA1033625 | ENA

An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [WGS]

Project description:The hepatocyte is a vital cell type responsible for numerous physiological processes in the body. We developed a machine learning-based AI algorithm to monitor morphological changes in hESCs during their differentiation into mature hepatocytes.Furthermore, we developed a modified strategy that safely and efficiently produces HLCs from hESCs.

2025-12-31 | GSE246556 | GEO

An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis [RNA-seq and ATAC-seq]

2025-12-31 | GSE243881 | GEO

An Optimized Hepatocyte-like Cells Differentiation Protocol Using Machine Learning Assisted Approach and Multi-omics Analysis

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

2025-12-31 | GSE246557 | GEO

Machine learning-optimized targeted detection of alternative splicing

Project description:Machine learning-optimized targeted detection of alternative splicing

| PRJNA1032463 | ENA

An optimized protocol for retina single-cell RNA sequencing [scRNA-Seq]

Project description:In this work, we compared different protocols to prepare single-cell suspensions used for scRNAseq and suggest an optimized dissociation protocol for mouse retina, which preserves cell morphology to a higher level leading to an overall increase of gene number per cell. We compared scRNAseq libraries generated with our optimized protocol to publicly available scRNAseq data of mouse retina. We further demonstrate a pipeline to reduce noise in scRNAseq caused by multiplets and ambient RNA.

2020-10-23 | GSE153673 | GEO

An optimized protocol for retina single-cell RNA sequencing [snRNA-Seq]

2020-10-23 | GSE153672 | GEO

An optimized protocol for single cell transcriptional profiling by combinatorial indexing

Project description:Single cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here we report a simplified, optimized version of the three-level sci-RNA-seq protocol that is faster, higher yield, more robust, and more sensitive, than the original sci-RNA-seq3 protocol, with reagent costs on the order of 1 cent per cell or less. We showcase the optimized protocol via whole organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a “tiny sci-*” protocol for experiments where input is extremely limited.

2021-11-05 | GSE186824 | GEO

An optimized protocol for retina single-cell RNA sequencing

Project description:An optimized protocol for retina single-cell RNA sequencing

| PRJNA643586 | ENA