starCAT

Implements starCellAnnoTator (AKA starCAT), annotating scRNA-Seq with predefined gene expression programs

Citation

If you use starCAT, please cite our manuscript.

Installation

You can install starCAT and its dependencies via the Python Package Index.

pip install starcatpy

We tested it with scikit-learn 1.3.2, AnnData 0.9.2, and python 3.8. To run the tutorials, you also need jupyter or jupyterlab as well as scanpy and cnmf:

pip install jupyterlab scanpy cnmf

Published and custom references programs

Several gene expression program references are available for annotation with starCAT, including the T cell reference described in our manuscript. Download and learn more about them on Zenodo.

We also provide example scripts for constructing custom starCAT references from a single cNMF run or multiple cNMF runs. Email me at dkotliar@broadinstitute.org if you are interested in making your reference available for others to re-use.

Basic starCAT usage

Please see our tutorials in python and R. A sample pipeline using a pre-built reference programs (TCAT.V1) is shown below.

# Load default TCAT reference from starCAT databse
tcat = starCAT(reference='TCAT.V1')

# tcat.ref.iloc[:5, :5]

#                     A1BG       AARD     AARSD1      ABCA1     ABCB1
# CellCycle-G2M   2.032614  22.965553  17.423538   3.478179  2.297279
# Translation    35.445282   0.000000   9.245893   0.477994  0.000000
# HLA            18.192997  14.632670   2.686475   3.937182  0.000000
# ISG             0.436212   0.000000  18.078197  17.354506  0.000000
# Mito           10.293049   0.000000  52.669895  14.615502  3.341488

# Load cell x genes counts data
adata = tcat.load_counts(datafn)

# Run starCAT
# expects the input data to be raw counts and to be stored in adata.X
# rather than adata.layers['counts']

usage, scores = tcat.fit_transform(adata)

usage.iloc[0:2, 0:4]
#                             CellCycle-G2M  Translation       HLA       ISG
# CATGCCTAGTCGATAA-1-gPlexA4       0.000039     0.001042  0.001223  0.000162
# AAGACCTGTAGCGTCC-1-gPlexC6       0.000246     0.100023  0.002991  0.042354

scores.iloc[0:2, :]
#                                  ASA  Proliferation  ASA_binary  \
# CATGCCTAGTCGATAA-1-gPlexA4  0.001556        0.00052       False   
# AAGACCTGTAGCGTCC-1-gPlexC6  0.012503        0.01191       False   

#                             Proliferation_binary Multinomial_Label  
# CATGCCTAGTCGATAA-1-gPlexA4                 False         CD8_TEMRA  
# AAGACCTGTAGCGTCC-1-gPlexC6                 False         CD4_Naive  

starCAT also can be run in the command line.

starcat --reference "TCAT.V1" --counts {counts_fn} --output-dir {output_dir} --name {outuput_name}

• –reference - name of a default reference to download (ex. TCAT.V1) OR filepath containing a reference set of GEPs by genes (*.tsv/.csv/.txt), default is ‘TCAT.V1’
• –counts - filepath to input (cell x gene) counts matrix as a matrix market (.mtx.gz), tab delimited text file, or anndata file (.h5ad)
• –scores - optional path to yaml file for calculating score add-ons, not necessary for pre-built references
• –output-dir - the output directory. all output will be placed in {output-dir}/{name}…’. default directory is ‘.’
• –name - the output analysis prefix name, default is ‘starCAT’

For code to reproduce figures and analyses from our manuscript, please refer to the TCAT analysis Github.

starCAT website

For small datasets (smaller than ~50,000 cells or 700 MB), try running starCAT on our website.