Releasing A New Genome Browser Track Group: Single-Cell RNA-seq
Brittney Wick
Hello everyone,
We are excited to release a new Genome Browser track group with single-cell RNA-seq (scRNA-seq) datasets for the hg38 assembly. Data generated by scRNA-seq allows us to study the heterogeneity of cells in organs, explore gene expression at a cellular level, and track cellular states in both development and disease.
We are starting with 14 scRNA-seq tracks covering different major organs of the body. Each new scRNA-seq track contains anywhere from 2-19 individual mRNA expression tracks in barChart format. By default, tracks display gene expression per individual cell type annotation and are colored according to cell class:
| Color | Cell Classification |
|---|---|
| neural | |
| stem cell | |
| adipose | |
| fibroblast | |
| myeloid | |
| immune | |
| lymphoid | |
| muscle | |
| hepatocyte | |
| kidney tubule | |
| secretory | |
| ciliated | |
| epithelial | |
| endothelial | |
| glia |
When available, expression tracks organized by other categories such as sex, donor, sample, detailed cell type, etc. are also included in these container tracks. In all of the tracks, the gene expression is averaged for each item in a category.
Clicking on any of the track titles below will lead to the track description page, which includes additional information and allows for configuration of various display options.
This release includes the following tracks:
- Blood (PBMC) Hao - This track displays peripheral blood mononuclear cell expression data from Hao et al., 2020 for 3 levels of cell type annotations, donor, phase, and time.
- Colon Wang - This track shows colon expression data from Wang et al., 2020 grouped by cell type and donor.
- Cortex Velmeshev - This track shows cortex expression data from Velmeshev et al., 2019 grouped by cell type, sex, sample, donor, and diagnosis.
- Fetal Gene Atlas - This track shows expression data from Cao et al., 2020 binned by cell type and other categories including sex, organ, experiment, donor, etc.
- Heart Cell Atlas - This track shows heart expression data from Litviňuková et al., 2020 binned by cell type and various categories including cell state, sample, region, donor, age, etc.
- Ileum Wang - This track shows ileum expression data from Wang et al., 2020 grouped by cell type and donor.
- Kidney Stewart - This track shows kidney expression data from Stewart et al., 2019 grouped by cell type, detailed cell type, project, experiment, etc.
- Liver MacParland - This track shows liver expression data from MacParlandet al., 2018 grouped by cell type, broad cell type, and donor.
- Lung Travaglini - This track shows lung expression data from Travaglini et al., 2020 binned by categories such as cell type, sample, donor, compartment, etc. using both 10x and Smart-seq2 library preparation methods.
- Muscle De Micheli - This track shows muscle expression data from De Micheli et al., 2020 grouped by cell type and sample.
- Pancreas Baron - This track shows pancreas expression data from Baron et al., 2016 grouped by cell type, detailed cell type, donor, and batch.
- Placenta Vento-Tormo - This track shows placenta and matched decidua and maternal PBMCs expression data from Vento-Tormo et al., 2018 grouped by cell type, detailed cell type, stage, etc. using both 10x and Smart-seq2 library preparation methods.
- Rectum Wang - This track shows rectum expression data from Wang et al., 2020 grouped by cell type and donor.
- Skin Sole-Boldo - This track shows skin expression data from Solé-Boldo et al., 2020 grouped by cell type, cell type with donor's age, donor, and age.
Many of these datasets can also be explored interactively using the UCSC Cell Browser. We are interested in bringing more single-cell datasets into the Genome Browser and the UCSC Cell Browser as well as the Human Cell Atlas project. If you have data you would like to contribute, check out our questionnaire.
We would like to thank the publication authors for making these data available. We would also like to thank Jim Kent, Brittney Wick, Luis Nassar, Daniel Schmelter, Jairo Navarro, and Gerardo Perez for building and testing these tracks.