Machine Learning Powered Biological Network Analysis
Video
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
The following presentation also shows a sneak peak of a new data analysis visualization software, DAVe: Data Analysis and Visualization engine. Check out some early features. DAVe is built in R and seeks to support a seamless environment for advanced data analysis and machine learning tasks and biological functional and network analysis.
As an aside, building the main site (in progress) was a fun opportunity to experiment with Jekyll, Ruby and embedding slick interactive canvas elements into websites. You can checkout all the code here https://github.com/dgrapov/CDS_jekyll_site.
slides: https://www.slideshare.net/dgrapov/machine-learning-powered-metabolomic-network-analysis
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
High Dimensional Biological Data Analysis and Visualization
High dimensional biological data shares many qualities with other forms of data. Typically it is wide (samples << variables), complicated by experiential design and made up of complex relationships driven by both biological and analytical sources of variance. Luckily the powerful combination of R, Cytoscape (< v3) and the R package RCytoscape can be used to generate high dimensional and highly informative representations of complex biological (and really any type of) data. Check out the following examples of network mapping in action or view a more indepth presentation of the techniques used below.
Partial correlation network highlighting changes in tumor compared to control tissue from the same patient.
Biochemical and structural similarity network of changes in tumor compared to control tissue from the same patient.
Hierarchical clusters (color) mapped to a biochemical and structural similarity network displaying difference before and after drug administration.
Partial correlation network displaying changes in metabolite relationships in response to drug treatment.
Partial correlation network displaying changes in disease and response to drug treatment.
Check out the full presentation below.
Comparison of Serum vs Urine metabolites +
Primary metabolites in human serum or urine.
Oh oh, there seem to be some outliers: serum samples looking like urine and vice versa. Fix these and evaluate using PCA and hierarchical clustering on rank correlations.
Now things look more believable. Next let us test the effects of data pre-treatment on PLS-DA model scores for a 3 group comparison in serum. Ideally group scores would be maximally resolved in the dimension of the first latent variable (x) and inter-group variance would be orthogonal or in the y-axis.
Compared to raw data (TOP) where ~ 3 top variables (glucose, urea and mannitol) dominate the variance structure, the autoscaled model, due to variable-wise mean subtraction and division by the standard deviation, displays a more balanced contribution to scores variance by variables. The larger separation between WHITE and RED class scores along the x-axis suggest improved classifier performance over raw data model and overview of samples with scores outside their respective group’s Hotelling’s T ellipse (95%) might point to a sample outlier to further investigate or potentially exclude from the current test.
Creative Data Solutions 