Profiling of CD34+ cells from human bone marrow to understand hematopoiesis

Default Boolean Operator: Terms in a search string will be connected by the AND operator, EXAMPLE: searching genomic melanoma will produce the same results as genomic AND melanoma .
AND, OR, NOT: Combine terms in a search with the boolean operators AND (to add specificity), OR (to broaden), or NOT (to eliminate a term). Operators must be capitalized (all other search terms are not case-sensitive).
Wild Card: Add wild card symbols * (open-ended) and ? (single character) to truncate terms. EXAMPLE: searching gen* shows results for 'gene', 'genetic', 'genomic, etc. Searching gene? returns results for 'gene' or 'genes' but not for 'genome'.
Phrase Searching: Enclose search in quotation marks "keyword or search term" to search for an exact phrase. EXAMPLE: "transcription factors" returns results for records where transcription is immediately followed by the word factors .
Diacritics: Words containing diacritics translate those characters to non-diacritic form. EXAMPLE: massague returns results for Massague&#769 .
Filter by: Located on the homepage (left panel), use filters to search for records or narrow the search. From search results page, filters can be removed by clicking on the term at the top of the page.

UID: 11418

Author(s): Setty, Manu*, Kiseliovas, Vaidotas*, Pe'er, Dana* * MSK affiliated

Description: Description from Human Cell Atlas:

"Differentiation is among the most fundamental processes in cell biology. Single cell RNA-seq studies have demonstrated that differentiation is a continuous process and in particular cell states are observed to reside on largely continuous spaces. We have developed Palantir, a graph based algorithm to model continuities in cell state transitions and cell fate choices. Modeling differentiation as a Markov chain, Palantir determines probabilities of reaching terminal states from cells in each intermediate state. The entropy of these probabilities represent the differentiation potential of the cell in the corresponding state. Applied to single cell RNA-seq dataset of CD34+ hematopoietic cells from human bone marrows, Palantir accurately identified key events leading up to cell fate commitment. Integration with ATAC-seq data from bulk sorted populations helped identify key regulators that correlate with cell fate specification and commitment."

Subject of Study

Subject(s)

Access via Human Cell Atlas

Access via BioStudies

Accession #: S-SUBS8

Access via European Nucleotide Archive

Accession #: PRJEB37166
Access Restrictions: Free to All
Access Instructions: Downloaded and exported data is governed by the HCA Data Release Policy and licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Associated Publications: Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe'er D. Characterization of cell fate probabilities in single-cell data with Palantir. Nature Biotechnology. 2019; 37(4):451-460.
Dataset Format(s): CSV, FASTQ, gzip, BAM, H5AD, LOOM
Dataset Size: 227.35 GB

Do you have or know of a dataset that should be added to the catalog? Let us know!