Creative Data Solutions | When you want to get to know and love your data

face_ann Hello,

My name is Dmitry Grapov. I am a data scientist who likes high-dimensional data (mostly biological) and enjoys writing about multivariate data analysis, visualization, machine learning and R programming. You can learn more about this in my blog, createdatasol.com and the links below.

8 responses

Sangdon Lee

Thanks for the great work

November 8, 2012 at 3:53 pm

Reply
Mateo

Looking forward to DEVIUM

March 6, 2013 at 11:36 pm

Reply
Wolfamuzika.Blogspot.Co.Uk

Just want to say your article is as surprising. The clarity to your post is just cool.
Thank you and please continue the great work.

September 26, 2013 at 8:48 pm

Reply
Vladimir Chupakhin

Excellent work!

November 16, 2013 at 1:02 pm

Reply
Fidele

This is indeed good work

November 21, 2014 at 12:02 pm

Reply
Paul Stewart

Your tutorials (git) and blog posts are very helpful. Thank you!

March 30, 2015 at 3:24 pm

Reply
Aurino M. Kemas

Your blog is very informative and valuable. I am a young researcher and am about to explore on metabolomics research for drug development/biomarker discovery.

May 25, 2016 at 1:08 am

Reply
boost brain power

You actually revealed that really well!

June 12, 2016 at 4:56 am

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Cancer tissue network

network_1

imDEV clouds

network

journal.pone.0048852.g002

Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).

Horizontal scatter plots of the log transformed concentrations for each model variable are shown. The horizontal arrangement of metabolite scatter plots is scaled to their loading in the discriminant model. A given species importance in the classification increases with increasing displacement from the origin (broken line). The direction of the displacement, left or right, designates whether the species was decreased (left) or increased (right) in the diabetic relative to the non-diabetic patients. The overall model discrimination performance is presented as a scatter plot of subject model scores (inset).

Treatment response network

Scatterplot matrix for overview of correlations and regressions, displaying box plots for Iris data species, variable histograms, correlation statistics, stripcharts and best fit lines.

Scatterplot matrix for overview of correlations and regressions, displaying box plots for Iris data species, variable histograms, correlation statistics, stripcharts and best fit lines.

network_1

mc 2

333

journal.pone.0048852.g001

ASMS 2014 j fahrman

WCMC network

cough syrup network

C and E figure

g9135

known partial correlation network2

Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p<0.05 by Mann-Whitney U test adjusted for FDR (q = 0.1).

Metabolites are represented by circular “nodes” linked by “edges” with arrows designating the direction of the biosynthetic gradient (i.e. substrate to product). Some metabolites are linked by more than one enzymatic step. Node sizes represent magnitudes of differences in plasma metabolite geometric means (ΔGM). Arrow widths represent magnitudes of changes in product over substrate ratios (ΔP:S). Colors of node borders and arrows represent the significance and direction of changes relative to non-diabetics as per the figure legend. Differences are significant at p<0.05 by Mann-Whitney U test adjusted for FDR (q = 0.1).

Tissue network cancer

loess_norm50

Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).

Spearman’s correlations were used to generate multi-dimensionally scaled parameter connectivity networks for variable intercorrelations. Networks were oriented with fasting glucose at the origin and SFA in the lower right quadrant. Colored ellipses represent the 95% probability locations of metabolite classes (Hoettlings T2, p<0.05). Nodes indicate clinical parameters (diamonds), <20-carbon fatty acid metabolites (circles) and ≥20-carbon fatty acid metabolites (triangles), with discriminant model variables and glucose enlarged. Significant correlations between species are designated by orange (positive) or blue (negative) connecting lines (p<0.05, non-diabetic; p<0.01, diabetic participants).

composite2

PLS-DA NETWORK

Bionetwork1

LOESS_span

genotype network

OPLS-DA network

network

Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

Figure 1. The type 2 diabetes-associated lipidomic changes projected in context of their biological relationships in obese African-American women.

Treatment effects network

PCA normalizations

netmaping

PLS_DA repeated measures trajectory

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