ImmuneSpace modules: ImmuneSpace provides a set of standardized modules – available under the Modules tab within each study – to explore datasets, and perform specific analyses. Currently, the following modules are available:

1. Data Explorer: This module can be used to quickly plot a selected immunological response variable (e.g. HAI) in one or more cohorts across multiple analytes (when applicable).
2. Gene Expression Explorer: This module can be used to quickly plot the expression level of one or more genes against a selected immunological response variable (currently HAI) in one or more cohorts.
3. Gene Set Enrichment Analysis: This module can be used to perform a gene set enrichment analysis across time (or across a prespecified contrast) within a specified cohort.
4. Immune Response Predictor: This module can be used to automatically select a group of genes whose expression at a given time point (e.g. gene expression levels at day 0) best predicts a given immunological response at a later time point (e.g. HAI at day 28). 

For specific examples with ImmuneSpace data, please visit study SDY269, which has all of the modules enabled.

Accessing ImmuneSpace data from R: In addition to the modules described above, we have also created the ImmuneSpaceR package to incorporate ImmuneSpace data into your current R workflow. ImmuneSpaceR is a thin wrapper around the Rlabkey package to query the ImmuneSpace database from R. It greatly simplifies access to data by taking advantage of the standardization of the ImmuneSpace database to hide all the Rlabkey specific code away from the user. ImmuneSpaceR will make it easy to use ImmuneSpace data to perform complex cross-assay and cross-study analyses leveraging the power of R and Bioconductor. Please refer to the ImmuneSpaceR manual for further details on installation and usage. 

Other tools of interest developed by the HIPC and ImmPort communities: Through HIPC and ImmPort, NIAID has funded the development of several other tools that might be of interests to ImmuneSpace users. Some of these are listed below:

• ImmuneRegulation is a user-friendly web interface that allows you to interactively explore the regulation of your genes and/or gene sets of interest by querying their regulation in the eQTL, Transcription Factor, and GQAS datasets in real time. ImmuneRegulation was developed by the Gumus lab at the Mount Sinai School of Medicine.
• The immune signature collection part of MSigDB created by the Haining lab.
• The interactive gene expression browser (GXB) developed by the Chaussabel lab. GXB is described in this paper.
• Meta Comparison Analysis Tool (MetaCAT) developed by the Chaussabel lab.
• ReliefSeq is a machine learning feature selection method for GWAS, RNA-Seq and other high-dimensional data sets that is able to identify genetic variables that influence continuous or dichotomous outcomes through interactions with other genetic variables. ReliefSeq was developed by the McKinney and Oberg labs. ReliefSeq is described in this paper