Bioconductor Conference

[gabrielodom]

Conference link: http://bioc2019.bioconductor.org/index

We plan to apply for a 10-minute talk + poster and a 50-minute workshop + poster.

10-minute talk + poster: https://docs.google.com/forms/d/e/1FAIpQLSfRuukXm89JOXiUPHBZSijvmozfwK7jT8sTiDXBnGFV4uWHXA/viewform

Workshop: https://docs.google.com/forms/d/e/1FAIpQLSdUoxsRN3M4FjSkFjSlxk04j79dIOAWPk1BSK1nZp-3VPPpGA/viewform

[gabrielodom]

Overall info: http://bioc2019.bioconductor.org/call-for-abstracts

[gabrielodom]

Due by 11 March:

• draft of workshop syllabus (as .Rmd): https://github.com/Bioconductor/BioC2019/blob/master/docs/workshop-syllabus.md
• 300-word abstract for presentation (NAR paper abstract?)

[gabrielodom]

Check out this very rough draft of a possible presentation (it's web hosted!!): http://rpubs.com/gabrielodom/test20190308

Also, the .Rmd and resulting .html and .docx files are in the BBSR package directory under "BioC2019-pathwayPCA"

[gabrielodom]

Abstract

With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. These new data sources necessitate new functionalities to analyze multiple types of -omics data simultaneously and to provide sample-specific estimates of pathway activities (important for precision medicine). To supply these needs, we present pathwayPCA, an R package for integrative pathway analysis that utilizes modern statistical methodology including supervised PCA and adaptive, elastic-net, sparse PCA. pathwayPCA can analyze continuous, binary, and survival outcomes in studies with multiple covariate and/or interaction effects. We will provide three case studies to illustrate pathway analysis with gene selection: integrative analysis of multi-omics datasets to identify potential driver genes, estimating and visualizing sample-specific pathway activities in ovarian cancer, and identifying sex-specific pathway effects in kidney cancer. We expect pathwayPCA to be a useful tool for the analyses and interpretation of the wealth of multi-omics data recently made available by TCGA, CPTAC, and other large consortia.

@lxw391, thoughts?

[gabrielodom]

From @jamesban2015:

In the slides for presentation, I would recommend 1. adding the table to compare pathwayPCA with other pathway analysis tools, 2. making the title and content of each slide correspond to each of the features presented in the table.

[gabrielodom]

Edited version of the abstract from @lxw391

With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. However, analyzing and interpreting multi-omics datasets remains challenging because of the high dimensionality of omics datasets. Here we present pathwayPCA, an R package for integrative pathway analysis. In particular, pathwayPCA provides estimates for sample-specific pathway activities which facilitate integrative analysis of multiple data types at the pathway level. pathwayPCA utilizes modern statistical methodology including supervised PCA and adaptive, elastic-net, sparse PCA. It can analyze continuous, binary, and survival outcomes in studies with multiple covariate and/or interaction effects. We will provide three case studies to illustrate pathway analysis with gene selection: integrative analysis of multi-omics datasets to identify potential driver genes, estimating and visualizing sample-specific pathway activities in ovarian cancer, and identifying pathways with sex-specific effects in kidney cancer. pathwayPCA is a useful tool for the analyses and interpretation of the wealth of multi-omics data recently made available by TCGA, CPTAC, and other large consortia.

[gabrielodom]

We have submitted an packet for the 10-minute talk + poster and a 1-hour workshop + poster