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ParalES

Enrichment Score(GSEA) compute Tools for Lincs data

Tools

A brief description of the tools is given below. The tools implement the function of parallel compute ES in multi-core environment. The Matlab implementation of the tools is currently the most mature. I used the 1ktools(https://github.com/cmap/l1ktools) tools to parse the .gctx file which stored gene profile data defined by Lincs(CMap) based on HDF5 file format. However,the performance is not very satisfactory. The C implementation to achieve at least 30 times faster than Matlab. But 1ktools can not parse the .gctx file by C. So I used Matlab to parse the .gctx file、extract the gene profile sets and write to .txt file. C will read the file 、complete parallel computing ES and write out the result in binary file . There also is a Matlab Script to read the binary files and can be later analysis. I will update the tools as they become available.

Matlab Tools: matlab/

Requirements:

  1. Matlab R2009a and above

Setting the MATLAB path:

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

Tools:

  • [ESquick.m]: Compute single Enrichment Scores.
  • [ESScore.m]: parallel compute Multiple pairs of ES.
  • [getSampleforMat.m]: parse the .gctx file and extract the gene profile sets we need.
  • [ParalES.m]:main entrance . Setting Parameters and complete parallel Computing tasks.
  • [getSample.m]: parse the .gctx file and extract the gene profile sets we need for Writting.
  • [PreESforC.m] : Setting Parameters 、 extract the gene profile sets and write to .txt file.
  • [importES.m] : Read the Enrichment Scores Result Matrix computed by C.

C Tools: c/

Tools:

  • [ParalES.c] read the .txt file 、complete parallel computing ES and write out the result in binary file

Demo:

  • [compileParalES.sh]: compile the C source code to Executable files.
  • [runParalESLinux.sh]: run Executable files in Linux.

The LINCS Dataset

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. You can get the .gctx file stored gene profile data by the Website.

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