Copyright 2011-2014 Broad Institute of MIT and Harvard.

A collection of software tools to read and analyze data produced from the L1000 project (www.lincscloud.org).

A brief description of the tools included in this software package is given below. The Matlab implementation of the tools is currently the most mature. Some basic utilities in R and java are also included. We will update the tools as they become available.

1. Matlab R2009a and above
2. Statistics Toolbox

Enter the "pathtool" command, click "Add with Subfolders...", and select the directory l1ktools/matlab.

• l1kt_dpeak.m: Performs peak deconvolution for all analytes in a single LXB file, and outputs a report of the detected peaks.
• l1kt_plot_peaks.m: Plots intensity distributions for one or more analytes in an LXB file.
• l1kt_parse_lxb.m: Reads an LXB file and returns the RID and RP1 values.
• l1kt_liss.m: Performs Luminex Invariant Set Smoothing on a raw (GEX) input .gct file
• l1kt_qnorm.m: Performs quantile normalization on an input .gct file
• l1kt_infer.m: Infers expression of target genes from expression of landmark genes in an input .gct file

See the documentation included with each script for a details on usage and input parameters.

• dpeak_demo.m: Demo of peak detection. To run the demo, start Matlab, change to the folder containing dpeak_demo and type dpeak_demo in the Command Window. This will read a sample LXB file (A10.lxb), generate a number of intensity distribution plots and create a text report of the statistics of the detected peaks (A10_pkstats.txt).

• example_methods.m: Reads in a .gct and a .gctx file, z-score the data in the .gctx file, and read in an .lxb file. To run the demo, start Matlab, change to the folder containing example_methods and type example_methods at the command line.

1. R versions 2.9 and above
2. prada package: http://www.bioconductor.org/packages/devel/bioc/html/prada.html
3. h5r package: http://cran.r-project.org/web/packages/h5r/index.html

R tools are found under R/cmap

• lxb2txt.R: Saves values from an LXB file as a tab-delimited text file.
• lxb2txt.sh: Bash wrapper to lxb2txt.R
• io.R: Classes for reading and writing .gct / .gctx files

• example_methods.R: To run the demo, change to the folder containing example_methods.R and source the script. It will read in a .gctx file and display its contents.

• GctxDataset.java: Class to store a GCTX data set.
• GctxReader.java: Class to read a GCTX file.
• LXBUtil.java: Class to read and store .lxb files.

1. Python 2.7 (untested under Python 3)
2. numpy: http://numpy.scipy.org
3. pandas: http://pandas.pydata.org/
4. requests: http://docs.python-requests.org/en/latest/
5. pytables: http://www.pytables.org/moin
6. blessings: http://pypi.python.org/pypi/blessings

Append l1ktools/python to the PYTHONPATH environment variable.

• cmap/io/gct.py : Classes to interact with .gct and .gctx files.
• cmap/util/api_utils.py: Classes to make calls to the LINCS annotation API and return results as Python data structures.

• example_methods.py: To run the demo, change to the folder containing example_methods.py and run the script. It will read in a .gctx file, display its contents, and write to disc.

Below are summarized the tools available to perform so common data analysis tasks.

• MATLAB: Use the parse_gctx function.
• R: Source the script l1ktools/R/cmap/io.R. Then use the parse.gctx function.
• Python: Import the module cmap.io.gct. Then instantiate a GCT object, and call its read() method. For more information, see the documentation on the GCT class.
• Java: See ReadGctxExample.java for an example.

• MATLAB: Use the mkgct and mkgctx functions.
• Python: Import cmap.io.gct and instantiate a GCT object. Then call the "build" method or "build_from_DataFrame" method to assmble a GCT object from a data matrix and optionally row and column annotations. Finally, call the "write" method to write to file as a .gctx.

• MATLAB: Use the robust_zscore function. Also see the example_methods.m script.

• MATLAB: To read an .lxb into the MATLAB workspace, use the l1kt_parse_lxb function.
• R: To convert an .lxb file to text, use the R/cmap/lxb2txt.sh script.

The CMAP Cloud API offers programmatic access to annotations and perturbational signatures in the LINCS L1000 dataset via a collection of HTTP-based RESTful web services. These services support complex queries via simple HTTP GET requests that can be executed in a web browser or any programming language. The results are returned as standard JSON objects. Click on the links on the left for usage instructions and examples.

In order to make the API call, an API key must be provided. If you do not have a key, contact lincs@broadinstitute.org.

• Lincscloud website: To view the available services and live examples, visit http://api.lincscloud.org/.
• Programmatic access via Python: Import the module cmap.util.api_utils. The classes CMapAPI and APIContainer handle calls to the API; see their documentation for more details. See example_methods.py for an example API call.

• api_examples.py: To run the demo, change to the folder containing api_examples.py and run the script. It will make calls to the API and store their results as Python data structures.

This software is restricted to research use only within academic, not-for-profit institutions.

For licensing information see http://lincscloud.org/license/.