Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

5e2e282b-6e6a-425a-94a1-e830f617eadd - samples

ABSTRACT: Shallow targeted sequencing with 462 mRNA and 97 antibodies of AML patientÃ¢Â€Â™s bone marrow mononuclear cells from iliac crest aspirations from. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://doi.org/10.6084/m9.figshare.14780127.v1 AMLQ4_SMK1 AML314 male AMLQ1_SMK2 AML116 female AMLQ3_SMK3 AML127 female AMLQ6_SMK4 AML183 male AMLQ2_SMK5 AML327 female AMLQ5_SMK6 AML334 male APLQ5_SMK7 APL124 male APLQ3_SMK8 APL142 male APLQ6_SMK9 APL218 female APLQ4_SMK10 APL147 male APLQ2_SMK11 APL223 female APLQ1_SMK12 APL224 female

PROVIDER: EGAD00001008185 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

5eb6eff8-6d88-4686-925d-144d9e316277 - samples

Project description:Targeted sequencing using BD Rhapsody with 462 mRNA and 197 antibodies of healthy young adult bone marrow mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://figshare.com/s/313b5739ff469dac8c01

| EGAD00001008189 | EGA

ea9e3f56-e351-49e2-9ae1-dfe6fbfe3335 - samples

Project description:Targeted sequencing using BD Rhapsody with 462 mRNA and 97 antibodies of healthy young and aged adult as well as AML bone marrow mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://figshare.com/s/0fda29b169c719223ee3.

| EGAD00001008188 | EGA

6d6b62b0-d009-4568-9d40-895c4eb22804 - samples

Project description:Whole transcriptome sequencing using BD Rhapsody with 97 antibodies of a healthy young adult bone marrow (Young3/BM3) mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access links is https://figshare.com/s/901bcddb9ee18e226031.

| EGAD00001008186 | EGA

Deciphering cell states and genealogies of human hematopoiesis with single-cell multi-omics

Project description:This SuperSeries is composed of the SubSeries listed below. The main processed data are organized in the following two Figshare links: Seurat objects: https://doi.org/10.6084/m9.figshare.23290004 ReDeeM-V output: https://doi.org/10.6084/m9.figshare.24418966.v1

2023-11-18 | GSE219015 | GEO

ab07ded7-815c-47f8-b1ef-e7dd7d5ed774 - samples

Project description:Targeted sequencing using BD Rhapsody with 462 mRNA of healthy young adult bone marrow mononuclear cells from iliac crest aspirations (BM3/Young3).

| EGAD00001008184 | EGA

Watanabe2018_Simple markov model

Project description:Simple Markov model. There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3. The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male. Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10.

2019-01-30 | MODEL1803120004 | BioModels

Single-cell proteo-genomic reference maps of the human hematopoietic system

Project description:We have generated high content single-cell proteo-genomic reference maps of human blood and bone marrow that quantitatively link the expression of up to 197 surface markers to cellular identities and biological processes across all major hematopoietic cell types in healthy aging and leukemia. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access links are https://figshare.com/s/0fda29b169c719223ee3 (97 surface marker, 9 individuals dataset), https://figshare.com/s/313b5739ff469dac8c01 (197 surface marker, bone marrow and blood dataset), https://figshare.com/s/901bcddb9ee18e226031 (97 surface markers and whole transcriptome dataset) and https://figshare.com/s/24e10bf129fae1918316 (97 surface marker, 15 AML datasets).

| EGAS00001005593 | EGA

Single-Cell, Single-Nucleus and Spatial RNA Sequencing of the Human Liver Identifies Hepatic Stellate Cell and Cholangiocyte Heterogeneity

Project description:Comparison of single cell RNAseq and single nucleus RNAseq on four healthy human liver caudate lobes, with cell-types validated using one slice of a fifth healthy human for VISIUM Spatial Transcriptomics. Raw UMI count tables can be found here: https://figshare.com/projects/Human_Liver_SC_vs_SN_paper/98981 Processed Seurat Objects can be found here: https://www.dropbox.com/sh/sso15ehqmrrh6mk/AACKHOsSlZW0_Zy9cbCkOmMfa?dl=0

2021-10-13 | GSE185477 | GEO

Watanabe2018_State Transition Model with Treatment and Costs

Project description:Model with functions depending on Age, Male, BP (Blood Pressure). There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male. Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time. Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10 and their ages and costs in each state. A stratified report by male and female and young – up to age 30 and old above age 30 is produced.

2019-01-30 | MODEL1803120006 | BioModels

Dynamic interplay between IL1 and WNT pathways in regulating dermal adipocyte lineage cells during skin development and wound regeneration

Project description:Cells were isolated from wounded skin tissue and administered into single cell RNA transcriptome sequencing to deconstruct cell hierarchy of dermal fibroblast population and underlying regulating mechanisms. In brief, 7~9 weeks old male C57BL/6 mice were anesthetized and one full-thickness wound (1.0 cm x 0.5 cm in size) was generated on shaved dorsal skin. Animals were sacrificed at indicated intervals after and wound bed tissues were collected for cell isolation and subsequent 10x Genomics single cell RNAseq. Sequencing was performed on Illumina Novaseq6000 platform (Illumina). The generated raw gene expression matrix was converted into Seurat objects using the R package Seurat v3.0. After low quality cells and outliers were discarded, 10233 cells from non-lesional skin, 7807 cells from wound beds at post-wounding day 2 (PWD2), and 4925 cells from wound tissue at PWD7 were used for downstream analysis. Next, unsupervised clustering and gene expression were visualized with the Seurat 3.0 on R studio, and assignment and identification of cell clusters was based on the expression of validated marker genes. Subsequent in-depth analysis was assisted by GENE DENOVO Inc (Guangzhou, China). Together, our study has identified a specific dermal fibroblast population with high DPP4, Sca1 and WNT2 expression, which is committed into adipocyte upon wounding, and define the differentiation trajectory of this adipocyte precursor during skin regeneration.

2023-06-10 | GSE196800 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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