When you want to get to know and love your data

Posts tagged “statistical analysis

Complex Systems Biology Informed Data Analysis

materials_of_analysisMetabolomics 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

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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.

deviuWeb

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

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!

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