dcona 0.0.10

Differential Correlation Network Analysis

DCoNA: tool for fast Differential Correlation Network Analysis

TODO: what is DCoNA and why should you use it? TODO: put a short feature description.

Table of Contents

1. Installation
• Install1
• Install2
2. Usage
• Data structure
• Working modes
• Ztest
• Hypergeom
• Zscore
• Network and exhaustive regimes

Installation

Installation as a command line tool

TODO: installation from pip3

Installation from source code

Start with cloning the repo. It is really crucial to clone it with all the submodules as follows:

git clone --recurse-submodules [e-mail]:zhiyanov/DCoNA.git

Linux (Ubuntu)

TODO: change the example below First, install the necessary dependencies:

sudo apt-get install -y g++ make cmake python3-dev python3-pip python3-numpy
sudo pip3 install cython pot

OS X

All instructions are the same, except that you need to install dependencies differently, through a combination of pip3 and brew.

Downloading TCGA-PRAD dataset

TODO: put the link here.

Usage

Data structure

To run the tool you need the following data

• config.json data filenames and tool usage parameters:

{
	"data_path": "~/input_directory/data.csv",
	"description_path": "~/input_directory/description.csv",
	"interaction_path": "~/input_directory/interaction.csv",
	"output_dir_path": "~/output_directory",
	
	"reference_group": "Normal",
	"experimental_group": "Tumor",

	"correlation": "spearman",
	"alternative": "two-sided",
	"score": "mean",
	"repeats_number": 800,
	"process_number": 64,

	"fdr_threshold": 0.05
}

Both relative and absolute file paths can be used.

Data description:

• data_path : data.csv contains an expression table. Rows of the table should be grouped by genes, miRNAs, isomiRNAs and other items. Columns of the table are grouped by patients taken from two different groups.

  Structure of data.csv :

  	sample_1	...	sample_n
  gene_1	1.2345	...	1.2345
  ...	...	...	...
  gene_n	1.2345	...	1.2345

• description_path : description.csv divide patients into two non-intersecting groups (e.g. Normal and Tumor patients). It is assumed that a patient does not belong to the both groups simultaneously.

  Structure of description.csv:

  Sample	Group
  sample_1	condition_1
  ...	...
  sample_n	condition_2

  Column names have to be exactly Sample and Group.

• interaction_path : interaction.csv (optionally) contains source/target pairs: correlations will be computed among this pairs (in network mode). You should delete this line from the config file in exhaustive mode.

  Structure of interaction.csv:

  Source	Target
  source_gene_1	target_gene_2
  ...	...
  source_gene_n	target_gene_n

  Column names have to be exactly Source and Target.

• output_dir_path is a path to an output directory.

Usage parameters:

• reference_group, experimental_group are names of the patient groups.

• correlation : spearman or pearson, defines the type of correlation that will be used in the tool.

• alternative : two-sided, less or greater.

  TODO: describe the parameter meaning in ztest and zscore regimes.

Working modes

Ztest

TODO: description

Usage:

# For network regime
python3 ~/network/ztest.py config.json
# For exhaustive regime
python3 ~/exhaustive/ztest.py config.json

Hypergeom

TODO: description

Usage:

# For network regime
python3 ~/network/hypergeom.py config.json
# For exhaustive regime
python3 ~/exhaustive/hypergeom.py config.json

Zscore

TODO: description

Usage:

# For network regime
python3 ~/network/zscore.py config.json
# For exhaustive regime
python3 ~/exhaustive/zscore.py config.json

Network and exhaustive regimes

DCoNA has two working regimes:

• Network (interactions) regime - performs calculations only on given gene pairs. Requires an interaction.csv file.
• Exhaustive (all vs all) regime - generates all possible gene pairs from genes listed indata.csv and performs calculations. An interaction.csv file is not needed.