Connectivity Map, Broad Institute of MIT and Harvard
http://cmappy.readthedocs.io/en/latest/
For more information on the file formats and available resources, please see clue.io/gctx.
Oana Enache
As of April 11th, 2017, cmapPy has the following dependencies:
- Python 2.7 (specifically, we use 2.7.11)
- h5py==2.6.0
- numpy==1.11.2
- pandas==0.18
- requests==2.13.0
Note that more recent versions of these packages should also work (except for Python3), should you choose to use them; however, we cannot guarantee package behavior for more recent packages. For contributors (see "Contributing" for further details), we request that you develop and submit any pull requests using the above environment.
We recommend the use of cmapPy with a virtual environment (here we demonstrate how to use it with conda).
Option 1 (recommended): Setup pandasGEXpress in a new conda environment
- (If you haven't already) install
miniconda - Download/follow instructions provided here. Unless you have personal preferences/reasons to do so, we recommend installing Miniconda over Anaconda because it's more lightweight.
- On the command line, type
conda infoto verify that conda has been properly instaled on your system. You should see some information about the "current conda install"; if not, your installation didn't work.
- (If you haven't already) install
- Create your cmapPy environment:
conda create --name my_cmapPy_env python=2.7.11 numpy=1.11.2 pandas=0.18 h5py=2.6.0 requests==2.13.0 - Activate your cmapPy environment:
source activate my_cmapPy_env - In your activated conda environment, pip install cmapPy:
pip install cmapPy
Option 2: Install cmapPy from PyPI
pip install cmapPy- Note: For use of other virtualenvs, we include a requirements.txt file in the cmapPy package that you can use to install the proper versions of depencies.
- pandasGEXpress: Parsers, writers, and utility methods for .gctx and .gct files. Integrated with pandas, enabling easy access to popular and powerful Python data science tools for analysis pipelines.
- cmap_api_client: A Python API client for accessing and retrieving information from the Connectivity Map API
pandasGEXpress represents .gctx and .gct files as instances of a GCToo object; in essence, this is a class that contains and maintains requirements between separate pandas DataFrames of the files' expression data, row metadata, and column metadata and some file attributes (source, version). More details on clue.io/gctx. Note that we currently only support GCTX version 1.0 and GCT versions 1.2 and 1.3.
GCToo.py: main class for storing contents of .gctx, .gct files.concat_gctoo.py: Concatenate (horizontally or vertically) two or more GCToo instances in a Python session or two {.gct, .gctx} files from the command line.gct2gctx.py: Command line tool to convert a .gct file to .gctx.gct2gctx.py: Command line tool to convert a .gctx file to a .gctparse.py: Parse in .gct, .gctx files to a new GCToo instanceplate_grp.py: Read and write .grp files to a list.random_slice.py: Slices a random subset of a GCToo file to a specified size.slice_gct.py: Slice a GCToo instance by including/excluding a list of row ids, column ids, row indexes, column indexes, or a combination thereof.write_gct.py: Write a .gct file from a GCToo instance.write_gctx.py: Write a .gctx file from a GCToo instance.
A suite of unit tests is also included in the /tests directory. Sample files for testing can be found in /tests/functional_tests.
We recommend referencing our Tutorial section on clue.io/gctx to see further demonstrations on how one might use a combination of cmapPy and pandas methods for common data analysis and manipulation tasks.
clue_api_client is a simple class that can be used to query the CLUE API.
cell_queries.py: Methods for cell line-related queries.clue_api_client.py: Main class representing a client, for running queries against the CLUE API.gene_queries.py: Methods for gene-related queries.macchiato_queries.py: Methods for brew_prefix related queries.mock_clue_api_client.py: Mock API for testing.pert_queries.py: Methods for perturbagen-related queries.
Coming soon!
We welcome contributors! For your pull requests, please include the following:
- Sample code/file that reproducibly causes the bug/issue
- Documented code providing fix
- Unit tests evaluating added/modified methods.