Schema for Pancreas Cells - Pancreas cells binned by cell type from Baron et al 2016
Database: hg38 Primary Table: pancreasBaronCellType Data last updated: 2022-05-12|
Big Bed File: /gbdb/hg38/bbi/pancreasBaron/cell_type.bb
Item Count: 17,530
Format description: BED6+5 with additional fields for category count and median values, and sample matrix fields
|chrom||chr1||Reference sequence chromosome or scaffold|
|chromStart||166070019||Start position in chromosome|
|chromEnd||166166969||End position in chromosome|
|name||FAM78B||Name or ID of item|
|score||0||Score from 0-1000, typically derived from total of median value from all categories|
|strand||-||+ or - for strand. Use . if not applicable|
|name2||ENSG00000188859.6||Alternative name for item|
|expCount||11||Number of categories|
|expScores||0.00294575,0,0.000739534,0.00436678,0.00330237,0.0121987,0.00441826,0,0.0196171,0,0||Comma separated list of category values|
Pancreas Cells (pancreasBaronCellType) Track Description
This track shows data from A Single-Cell Transcriptomic Map of the Human and Mouse
Pancreas Reveals Inter- and Intra-cell Population Structure. Pancreas
tissue was analyzed using droplet-based single-cell RNA-sequencing (scRNA-seq)
and subsequent clustering distinguished 14 pancreas-resident cell types based
on their identified marker genes found in Baron et al., 2016.
There are four bar chart tracks in this track collection with pancreas cells
grouped by either batch (Pancreas Batch),
cell type (Pancreas Cells), detailed
cell type (Pancreas Details) and
donor (Pancreas Donor). The default track
displayed is pancreas cells grouped by cell type.
The cell types are colored by which class they belong to according to the following table.
Cells that fall into multiple classes will be colored by blending the colors
associated with those classes. The colors will be purest in the
subtrack, where the bars represent relatively pure cell types. They can give an
overview of the cell composition within other categories in other subtracks as
Human islets were obtained from two female cadaveric donors ages 51 (human2)
and 59 (human4) and two male cadaveric donors ages 17 (human1) and 38 (human3).
The samples collected from human 1-3 were non-diabetic and human 4 had type 2
diabetes mellitus. Using single-cell RNA-sequencing ~10,000 human pancreatic
cells were isolated and sequenced. For each donor, several separate batches of
~800 cells were prepared and sequenced to obtain an average of about 100,000
reads per cell. Cells were barcoded using the inDrop platform which follows the
CEL-Seq protocol for library construction. Paired end sequencing was done on
the Illumina Hiseq 2500. After filtering out cells with limited numbers of
detected genes, the dataset contained 8,629 cells from the four donors.
The cell/gene matrix and cell-level metadata was downloaded from the
UCSC Cell Browser.
The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used
to transform these into a bar chart format bigBed file that can be visualized. The coloring
was done by defining colors for the broad level cell classes and then using another UCSC utility,
hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on
our download server.
The raw bar chart data can be
explored interactively with the Table
Browser or the Data Integrator. For
automated analysis, the data may be queried from our REST API. Please refer to our mailing
list archives for questions, or our Data Access FAQ for more
Thanks to Mayaan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, and to
the many authors who worked on producing and publishing this data set. The data
were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then
reviewed by Jairo Navarro. The UCSC work was paid for by the Chan Zuckerberg Initiative.
Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell
Cell Syst. 2016 Oct 26;3(4):346-360.e4.
PMID: 27667365; PMC: PMC5228327