Schema for Fetal Assay - Fetal Gene Atlas binned by assay (cell/nucleus) from Cao et al 2020
  Database: hg38    Primary Table: fetalGeneAtlasAssay Data last updated: 2021-01-11
Big Bed File Download: /gbdb/hg38/bbi/fetalGeneAtlas/
Item Count: 60,060
The data is stored in the binary BigBed format.

Format description: BED6+5 with additional fields for category count and median values, and sample matrix fields
chromchr1Reference sequence chromosome or scaffold
chromStart166022214Start position in chromosome
chromEnd166023027End position in chromosome
nameENSG00000226292.1Name or ID of item
score0Score from 0-1000, typically derived from total of median value from all categories
strand++ or - for strand. Use . if not applicable
name2RP11-7G12.1Alternative name for item
expCount2Number of categories
expScores0.00611847,0.00644703Comma separated list of category values

Sample Rows

Fetal Assay (fetalGeneAtlasAssay) Track Description


This group of tracks shows data from A human cell atlas of fetal gene expression. This is a collection of single cell and single nucleus combinatorial indexing-based RNA-seq data covering 4 million cells from 15 organs obtained during mid-gestation. The cells were sequenced in a highly multiplexed fashion and then clustered with annotations as described in Cao et al., 2020.

The Fetal Cells subtrack contains the data organized by cell type, with RNA signals from all cells of a given type pooled and averaged into one bar for each cell type. The Fetal Lineage subtrack shows similar data, but with the cell types subdivided more finely and by organ. Additional bar chart subtracks pool the cell by other characteristics such as by sex (Fetal Sex), assay (FetalAssay), donor (Fetal Donor ID), experiment (Fetal Exp), organ (Fetal Organ), and reverse transcription group (Fetal RT Group).

Please see for further interactive displays and additional data.

Display Conventions and Configuration

The cell types are colored by which class they belong to according to the following table. The coloring algorithm allows cells that show some blended characteristics to show blended colors so there will be some color variation within a class. The colors will be purest in the Fetal Cells 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 well.

Color Cell classification


Three-level single-cell combinatorial indexing (sci-RNAseq3) as described in Cao et al., 2020 was used on 121 samples from 28 fetuses estimated 72 to 129 days post-conception. This included samples from 15 organs. and resulted in RNA profiles for 4 million cells. The samples were flash-frozen for majority of the experiments and then nuclei extracted for sequencing. Samples from tissues from the kidney and digestive system were fixed after disassociation to deactivate endogenous RNases and proteases.

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.

Data Access

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 information.


Thanks 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.


Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M, Daza RM, Zager MA, Aldinger KA, Blecher-Gonen R, Zhang F et al. A human cell atlas of fetal gene expression. Science. 2020 Nov 13;370(6518). PMID: 33184181; PMC: PMC7780123

Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. PMID: 30787437; PMC: PMC6434952