sisana 1.6.0

A command line interface tool to reconstruct and analyze single sample networks.

SiSaNA - Single Sample Network Analysis

Read about SiSaNA

SiSaNA can currently be found on bioRxiv

Table of contents

• SiSaNA introduction
  • What is SiSaNA?
  • What are PANDA and LIONESS?
  • How do you interpret the gene regulatory networks?
• Requirements
• Installation
• Before you begin
  • Example input files
  • Help documentation
  • Setting up your params.yml file
• Running SiSaNA
  • Step 1: Pre-processing of data
  • Step 2: Reconstruction and analysis of networks
  • Step 3: Comparing two experimental groups
  • Step 4: Survival analysis
  • Step 5: Performing gene set enrichment analysis
  • Step 6: Visualization of results
  • Step 7: Summarize your results

SiSaNA introduction

What is SiSaNA?

SiSaNA is a command line tool that utiliizes the PANDA and LIONESS algorithms from the netZooPy module to reconstruct single sample regulatory networks. Using SiSaNA, users can easily calculate in- and out-degree for each of the reconstructed networks. Additionally, SiSaNA can compare the expression/degree between groups of interest, including performing statistical tests, visualizing the results (volcano plots, boxplots, violin plots, and heatmaps), and compare the survival between groups. All this is accomplished via the command line, with little to no prior programming experience required.

What are PANDA and LIONESS?

PANDA is a tool developed to reconstruct gene regulatory networks from bulk input data (such as RNA-Seq data). It uses a message passing approach, along with prior protein-protein interaction data and prior regulatory knowledge information to refine a single network that reflects the regulatory landscape of the input samples. LIONESS utilizes the PANDA algorithm and iteratively removes one sample (with replacement), then uses PANDA to reconstruct a network with all samples minus one. It then uses differences in the original PANDA network and the newly created network with n-1 samples to reconstruct single-sample regulatory networks.

How do you interpret the gene regulatory networks?

These networks consist of two types of nodes: transcription factors (TFs) and genes, with an edge that connects each TF to each gene. The weight (or value) of the edge denotes the likelihood of a TF to regulate that gene. A larger edge weight means a higher likelihood of regulation.

SiSaNA will also calculate in-degree and out-degree, where in-degree is the sum of edge weights coming in to a gene while out-degree is the sum of edge weights coming out from a TF. As you may have guessed, a larger in-degree means that, on average, a gene is being more highly regulated in the modeled disease. Meanwhile, a larger out-degree means that, on average, that TF is regulating more genes in the modeled disease.

Requirements for using SiSaNA

• python v3.9.19 (see installation steps for creating a conda environment with this specific Python version). SiSaNA should work with versions of Python 3.9.0 or greater, but as it has been written and tested on this version, we will use 3.9.19.

Installation

1. Create a conda virtual environment with python version 3.9.19. Note: You need to substitute the path you want on your own system for the --prefix argument

conda create --prefix </path/to/env-name> python=3.9.19

2. Enter the conda environment

conda activate </path/to/env-name>

3. Install SiSaNA via the pip package installer

pip3 install sisana

4. Create a directory for the analysis and move into the analysis directory

mkdir sisana
cd sisana

Before you begin

Pipeline overview

Example input files

Example input files can be obtained using the command

sisana -e

These files will be downloaded from Zenodo and stored in a directory called "example_inputs". One of these example files is the params.yml file, which can be used as a template and edited for your own data (see next section). Each user-defined parameter in the params.yml file is documented with a comment to explain the function of the parameter. The comments do not need to be removed prior to running SiSaNA. The files in this example_inputs directory can be used in the commands listed down below.

SiSaNA help documentation

To view help documentation on which subcommands are available, the following can be used:

sisana -h

For further information on these subcommands, simply put the name of the subcommand before the -h

sisana <subcommand> -h

Setting up your params.yml file

The most important thing to get right in order to correctly run SiSaNA is the structure of your params.yml file. SiSaNA comes with a params.yml file that is annotated to explain the function of each argument. The params.yml file is separated into 'chunks' that reflect the same subcommands available in SiSaNA on the command line. For each step of SiSaNA, you will need to use the correct subcommand, as well as have the parameters set up in the params.yml file.

In the below example, the user is running the "preprocess" step of SiSaNA. They have specified the paths to the input files as well as the value for the number of samples a gene must be expressed in (in their case, 5), along with the path to the output directory in which to store their results.

Running SiSaNA

Step 1: Pre-processing of data

The "preprocess" subcommand is the first stage of SiSaNA, where it preprocess the input data to get it in a format that the PANDA and LIONESS algorithms can handle. This will likely involve the removal of genes or transcription factors that are not consistent across files. Information regarding the removal of these factors is given at the end of the preprocessing step.

Example command

sisana preprocess ./example_inputs/params.yml

Outputs

Three files, one for each of the three filtered input files.

Step 2: Reconstruction and analysis of networks

This second SiSaNA stage, "generate", uses the PANDA and LIONESS algorithms of netZooPy to reconstruct gene regulatory networks. Documentation for netZooPy can be found at https://github.com/netZoo/netZooPy/tree/master. It then performs basic analyses of these networks by calculating in-degree of genes (also called gene targeting scores) and out-degree of transcription factors (TFs).

Example command

sisana generate ./example_inputs/params.yml

Outputs

1. lioness.npy, which contains all calculated edges for each sample
2. lioness.pickle, which is the same thing, just serialized to make reading into python quicker
3. A file containing the calculated indegree and another file with the outdegree of each gene and transcription factor, respectively.

Step 3: Comparing two experimental groups

The next stage in SiSaNA, "compare", is used to find out how the in-degree and out-degree differ between each group. SiSaNA offers multiple ways to do this comparison, including t-tests (and Mann-Whitney tests) and paired t-tests (and Wilcoxon paired t-tests).

Example commands

To compare the values between two groups in order to identify differentially expressed genes or differential degrees, you can use the following command:

sisana compare ./example_inputs/params.yml

Step 4: Survival analysis

For performing survival analyses, you can use a command like this:

sisana survival ./example_inputs/params.yml

Step 5: Performing gene set enrichment analysis

"sisana gsea" is used to perform gene set enrichment analysis (GSEA) to identify pathways that are differentially regulated based on the gene targeting scores. It uses the ranks of genes found in the previous step (sisana compare) as input.

Example commands

sisana gsea ./example_inputs/params.yml

Step 6: Visualization of results

The "visualize" command allows you to visualize the results of your analysis on publication-ready figures. There are multiple types of visualization you can perform, including generating volcano plots...

sisana visualize volcano ./example_inputs/params.yml

...making boxplots or violin plots of expression/degrees...

sisana visualize quantity ./example_inputs/params.yml

...and creating heatmaps

sisana visualize heatmap ./example_inputs/params.yml

Step 7: Summarize your results

The final stage of SiSaNA, "summarize", takes all the created images and outputs them in a single html file for convenience. This can then be opened in a web browser. Please note that you must be in the directory containing the log_files subdirectory for this command to work.

sisana summarize