iOmicsPASS+

Integrative -Omics Predictive Analysis of Subnetwork Signatures (Version II - An R-package)

To start, either download iOmicsPASSplus.zip file and unzip to local directory or use command line/Terminal to clone the entire github directory:

> git clone https://github.com/cssblab/iOmicsPASSplus.git

Introduction

iOmicsPASS+ is a R-package incorporating iOmicsPASS (Koh et al., 2019), extended to other types of -omics data allowing for flexibility and increasing usability. It includes several module including a network inference module NetDeconvolute() using graphical LASSO (glasso) to estimate a sparse inverse covariance matrix, creating a confounding-free partial correlation network among features from up to three types of -omics datasets.

iOmicsPASS has been improved to iOmicsPASS+ allowing for higher flexibility and enabling applications to different types of omics data. Improvements include:

• Specification of direction of association
  Users may now specify the direction for every pair of interacting or co-varying molecule by adding an additional column in the network file. However, only molecules that show a concordance in the sign of correlation in the empirical data as the user-specified direction of association will be considered.

• Allows for a single network and input data
  Previously, at least two data and two networks were required as input. Now, users can input only one single data and create co-expressions among the variables in the data with a single network file.

• Addition of a Network estimation module NetDeconvolute()
  Estimates a correlation network, linking the different features from up to three different data, using graphical LASSO (glasso) to estimate a sparse inverse covariance matrix, creating a confounding-free partial correlation network

• New functions to help users compile and run iOmicsPASS using R
  Functions included in the R package facilitate users to build INSTALL.iOmicsPASS(), create input parameter file createInputParam(), create prior probabilities createPrior() and run the software run.iOmicsPASS() in the R-console.

• Addition of a Prediction module Predict.iOmicsPASS()
  Uses the network signatures identified in the subentwork discovery module run.iOmicsPASS()to assign new samples to the phenotypic groups.

• Adjustment for clinical information
  Users can incorporate clinical information such as age, gender and BMI, to modify the prior class probabilities used for assigning samples to the different groups.

  The figure below illustrates the overview of iOmicsPASS+