Package Name : EnrichmentPackage
Package Version : v3r1
Package Description: Use of the Hypergeometric distribution, to calculate enrichment in clustered PPI networks.
Date : 2016
Author : Colin D Mclean Colin.D.Mclean@ed.ac.uk
Copyright (C) 2016 Colin Mclean
Use of the Hypergeometric distribution to calculate enrichment in clustered PPI networks, built upon:
[1] the gamma function (and class) as given in Numerical Recipes 3rd Edition W. Press, S. Teukolsky, W. Vetterling, B. Flannery.
[2] M. Galassi et al, GNU Scientific Library Reference Manual (3rd Ed.), ISBN 0954612078.
[3] Pocklington A, Cumiskey D, Armstrong D, Grant S: The proteomes of neurotransmitter receptor complexes from modular networks with distributed functionality underlying plasticity and behaviour, MSB, 2, (2006).
[4] Alex T. Kalinka, The probablility of drawing intersections: extending the hypergeometric distribution, arXiv:1305.0717v5, (2014).
[5] Benjamini, Y., and Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57 (1995), 289–300.
[6] Benjamini, Y., and Liu, W. A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence. Journal of Statistical Planning and Inference 82 (1999), 163–170.
[7] Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29, 1165-1188.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License (GNU_GPL_v3) along with this program. If not, see http://www.gnu.org/licenses/.
This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1).
(1) This package makes use of the GNU Scientific Library (GSL); upon request, we can provide an implementation which is independent of libraries, making use of the subroutines from Numerical Recipes We use the default location of the gsl directory at: /usr/local/include/gsl. If the directory is not installed on the standard search path of your compiler, you will also need to provide its location in the Makefile at: GSLCFLAGS = -I/location/to/your/gsl
However we can provide upon request, an implementation which is independent of libraries, making use of the subroutines found in Numerical Recipes 3rd Edition.
(2) Running 'make' should build the executable called 'run'.
(3) To see all options, and some command line examples, you can type the command: './run'.
The package makes use of the clustering results (of the respective graphs located in the Graphs directory) found the directory 'Clustering', and the annotation files stored in directory 'Annotation'. The directory 'parameterFiles'
To run the clustering package at the command line type the following:
EXAMPLE 1:
To run cluster enrichment of the Spectral method on the Presynaptic network for disease annotation:
./run -opt 1 -Comfile ../Clustering/PPI_Presynaptic_Published/Spectral_communities_cytoscape.csv -Annofile ../Annotations/flatfile_human_gene2HDO.csv
EXAMPLE 2:
To run cluster enrichment of the Spectral method on the Presynaptic network for the overlap of disease and synaptic functional annotation:
./run -opt 2 -Comfile ../Clustering/PPI_Presynaptic_Published/Spectral_communities_cytoscape.csv -Annofile ../Annotations/flatfile_human_gene2HDO.csv -Annofile ../Annotations/SCH_flatfile.csv
EXAMPLE 3:
To run network level enrichment for disease, synaptic function and cell type annotation sets:
./run -opt 4 -Comfile ../Clustering/PPI_Presynaptic_Published/Spectral_communities_cytoscape.csv -Annofile ../Annotations/flatfile_human_gene2HDO.csv -Annofile ../Annotations/SCH_flatfile.csv -Annofile ../Annotations/celltypes_PMID27991900_L2.csv
EXAMPLE 4:
In addition to the single command line studies above, we also provide the bash scripts 'submitClustEnrch.sh' and 'submitOverlapEnrch.sh', to the enrichment package through various combinations of clustering results, annotation sets and graphs. This is controlled by the parameter files found in the folder '../parameterFiles'.
EXAMPLE 5:
For example, get the cluster enrichment results for algorithms: 'Spectral', 'infomap' and 'sgG5' for disease and synaptic functional groups on the presynaptic network, we would set:
emacs ../parameterFiles/graphs.csv
1 PPI_Presynaptic 0 PPI_PSP 0 PPI_PSP_consensus
emacs ../parameterFiles/clusteringAlg.csv
0 fc_communities.csv fc 0 lourvain_communities.csv lourvain 0 lec_communities.csv lec 1 Spectral_communities.csv Spectral 1 infomap_communities.csv infomap 0 sgG1_communities.csv sgG1 0 SVI_communities.csv SVI 0 wt_communities.csv wt
emacs ../parameterFiles/annotations.csv
2 flatfile_human_gene2HDO.csv topOnto_ovg 0 flatfile_chua.csv chua 1 SCH_flatfile.csv SCH 0 flatfile.go.MF.csv GOMF 0 flatfile.go.BP.csv GOBP 0 flatfile.go.CC.csv GOCC
Then run:
./submitClustEnrch.sh
EXAMPLE 6:
To get the clusteral enrichment overlaps for algorithms: 'Spectral', 'infomap' and 'sgG5' for disease and synaptic functional groups on the presynaptic network, we would then run:
./submitOverlapEnrch.sh