forked from varnivey/ipanda
-
Notifications
You must be signed in to change notification settings - Fork 2
/
MANUAL.txt
115 lines (73 loc) · 3.66 KB
/
MANUAL.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
This file is a part of iPANDA package
(in silico Pathway Activation Network Decomposition Analysis)
The package is distributed under iPANDA license
Version 0.0.1
Copyright © 2016 Insilico Medicine Inc.
USA, Johns Hopkins University, ETC B301,
1101 East 33rd St. Baltimore, MD 21218
iPANDA is a pathway analysis method that aids interpretation of large-scale
gene expression data. The method introduced here includes coexpression analysis
and gene importance estimation to robustly identify relevant pathways and
biomarkers for patient stratification, drug discovery and other applications.
The details of the iPANDA algorithm can be found in the iPANDA manuscript:
Ozerov I.V. et al., 2016, Nature Communications, 'In silico Pathway
Activation Network Decomposition Analysis (iPANDA) as a method for biomarker
development'.
The usage of the code supplied within this package is described hereafter.
The code is written in Python 2.7. The code depends on numpy and scipy
packages, which can be obtained by the following links:
http://www.numpy.org/
https://www.scipy.org/
The package was tested with the following configuration:
system: x86_64 GNU/Linux Kubuntu 14.04
python version: 2.7.6
numpy version: 1.9.2
scipy version: 0.16.0
The only executable file inside this package is ipanda.py
To run the calculation download this package, enter the ipanda directory and
run ipanda.py executable:
Usage: ipanda.py [options]
Options:
-h, --help show this help message and exit
-i EXPR_FILE Input file
-o OUTFILE Outfile
-p PW_DIR Pathway_set
Input expression file <EXPR_FILE> should be supplied in a tab separated format.
File should contain the table of gene expression values with genes in lines and
samples in columns (see the sample gene expression file within this package
<ipanda_dir>/sample_input/sample_gene_expression_file.tsv). iPANDA algorithm
requires two sets of samples to compare:
case samples (e.g. tumor samples, condition under study samples)
normal samples (normal-control tissue samples)
The names of normal samples should be always preceded by a keyword "Normal_".
In case one of the tumor or normal groups of samples contains only one sample
single sample T-test is utilized for the statistical weights estimation. In case
there is only one sample in both groups statistical weights are disabled
(equal to 1).
Input pathway database <PW_DIR> should be supplied in a separate directory
according to the following specification:
1. gene_list.txt
List of all gene symbols in pathway database
2. modules.txt
File containing coexpression modules. Each line contains genes corresponding to
a single module.
3. pathway_contents.txt
List of genes and gene modules in each pathway (columns correspond to pathways).
Modules should be named as "mod<4-digit number>", where <4-digit number>
corresponds to a number of the line in modules file containing genes within the
module.
4. pathway_akk.txt
List of topology coefficients placed in the order corresponding to
pathway_contents.txt file.
5. pathway_list.txt
File containing a list of pathway names within the pathway database.
Output file <OUTFILE> is a space separated file containing pathway activation
scores for the tumor samples only.
There is a sample pathway database containing 19 KEGG pathways with
precalculated topology weights (see the sample pathway database in
<ipanda_dir>/sample_input/sample_kegg).
Please cite the iPANDA paper in any publication containing results acquired
using this package.
Ozerov I.V. et al., 2016, Nature Communications, 'In silico Pathway
Activation Network Decomposition Analysis (iPANDA) as a method for biomarker
development'.