github link
Showing
of 173 results
Sort by

Filters

Technology

Platform

accession-icon SRP045837
RNA-Seq analysis comparing p53-null versus ?Np63?/?;p53-null or ?Np73?/?;p53-null thymic lymphoma tumors
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

We performed an RNA-Seq analysis comparing thymic lymphoma tissues from the p53-null(n=2) and ?Np63?/?;p53-/- (n=3) or ?Np73?/?;p53-/-(n=3). Mice at 10 weeks of age were injected with either Ad-mCherry or Ad-CRE-mCherry to delete ?Np63/?Np73 in the thymic lmyphomas. We aimed to test by deleting the DNp63/DNp73 in these p53-deficient tumors will mediate tumor regression and analyze the expression profile of the genes Overall design: Examination of thymic lymphoma tissues in 3 different genotypes (p53-/- vs ?Np63?/?;p53-/- or ?Np73?/?;p53-/-)

Publication Title

IAPP-driven metabolic reprogramming induces regression of p53-deficient tumours in vivo.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP127409
4 NSCLC cell lines treated with GDC-0973, AZ-628 or a combination of both
  • organism-icon Homo sapiens
  • sample-icon 48 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

RNA was purified cancer cell lines. The "SAMPLE_ID" sample characteristic is a sample identifier internal to Genentech. The ID of this project in Genentech's ExpressionPlot database is PRJ0013114 Overall design: RNA from NSCLC cell lines after treatment with either DMSO, GDC-0973, AZ-628 or the combination of AZ-628 and GDC-0973 all at 0.1 micro-molar concentration.

Publication Title

Pharmacological Induction of RAS-GTP Confers RAF Inhibitor Sensitivity in KRAS Mutant Tumors.

Sample Metadata Fields

Cell line, Treatment, Subject

View Samples
accession-icon SRP059850
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of GMP)
  • organism-icon Mus musculus
  • sample-icon 123 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059903
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq from CMP)
  • organism-icon Mus musculus
  • sample-icon 85 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059844
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of bone marrow lineage-negative Sca1+ CD117+ cells)
  • organism-icon Mus musculus
  • sample-icon 88 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059848
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-/- GMP)
  • organism-icon Mus musculus
  • sample-icon 71 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059873
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Irf8 KO GMP)
  • organism-icon Mus musculus
  • sample-icon 63 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP071150
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-/- Irf8-/- GMP)
  • organism-icon Mus musculus
  • sample-icon 47 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

Specimen part, Cell line, Subject

View Samples
accession-icon SRP059847
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Gfi1-GFP GMP)
  • organism-icon Mus musculus
  • sample-icon 38 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP059904
Using single-cell RNA-Seq for unbiased analysis of developmental hierarchies (single cell RNA Seq of Irf8-GFP GMP)
  • organism-icon Mus musculus
  • sample-icon 37 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Single cell RNA seq and bioinformatic analysis is used to characterize myeloid differentiation to uncover novel transcriptional networks and key drivers of hematoipoietic development Overall design: Single cell RNA seq of different hematopoietic populations integrated with Chip seq involving multiple markers

Publication Title

Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.

Sample Metadata Fields

No sample metadata fields

View Samples