## Complex Systems Biology Informed Data Analysis

### Metabolomics and the greater sphere of ‘Omic analyses are a burgeoning set tools for investigation of environmental and organismal mechanisms and interactions. Carrying out data analyses within complex biological system contexts is rewarding but also difficult. The following presentation considers components involved in conducting multivariate data analysis, modeling and visualization within biological contexts.

slides: https://www.slideshare.net/dgrapov/complex-systems-biology-informed-data-analysis-and-machine-learning

June 11, 2017 | Categories: Uncategorized | Tags: clustering, data visualization, genomics, lectures, machine learning, metabolomics, network, pathways, proteomics, research, science, software, statistical analysis | Leave a comment

## Multivariate Data Analysis and Visualization Through Network Mapping

Recently I had the pleasure of speaking about one of my favorite topics, Network Mapping. This is a continuation of a general theme I’ve previously discussed and involves the merger of statistical and multivariate data analysis results with a network.

Over the past year I’ve been working on two major tools, DeviumWeb and MetaMapR, which aid the process of biological data (metabolomic) network mapping.

DeviumWeb– is a shiny based GUI written in R which is useful for:

- data manipulation, transformation and visualization
- statistical analysis (hypothesis testing, FDR, power analysis, correlations, etc)
- clustering (heiarchical, TODO: k-means, SOM, distribution)
- principal components analysis (PCA)
- orthogonal partial least squares multivariate modeling (O-/PLS/-DA)

MetaMapR– is also a shiny based GUI written in R which is useful for calculation and visualization of various networks including:

- biochemical
- structural similarity
- mass spectral similarity
- correlation

Both of theses projects are under development, and my ultimate goal is to design a one-stop-shop ecosystem for network mapping.

In addition to network mapping,the video above and presentation below also discuss normalization schemes for longitudinal data and genomic, proteomic and metabolomic functional analysis both on a pathway and global level.

As always happy network mapping!

June 27, 2014 | Categories: Uncategorized | Tags: biochemical network, chemical similarity network, correlation network, Cytoscape, data analysis, data visualization, DeviumWeb, ggplot2, metabolomics, MetaMapR, multivariate, network mapping, O-PLS, R, r-bloggers, shiny, statistical analysis | 6 Comments