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PubChem 446220 = Yeyo

google

I just updated my R package, CTSgetR, for biological database translation using the Chemical Translation Service (CTS). While making code examples I came across some humorous chemical name synonyms for the molecule referenced in PubChem as CID = 446220. google Below are a few examples, can you guess what this is?

Badrock, Bazooka, Bernice, Bernies, Blast, Blizzard, Bouncing Powder, Bump, Burese, C Carrie, Cabello, Candy, Caviar, Cecil, Charlie, Chicken Scratch, Cholly, Coca, Cocktail, Cola, Dama blanca, Dust, Flake, Flex, Florida Snow, Foo Foo, Freeze, Girl, Gold dust, Goofball, Green gold, G-Rock, Happy dust, Happy powder, Happy trails, Heaven, Hell, Jam, Kibbles n’ Bits, Kokan, Kokayeen, Lady, Leaf, Line, Moonrocks, Pimp’s drug, Prime Time, Rock, Sleighride, Snort, Snow (birds), Star dust, Star-spangled powder, Sugar, Sweet Stuff, Toke, Toot, Trails, White girl or lady, Yeyo, Zip

August 8, 2014 | Categories: Uncategorized | Tags: CTS, CTSgetR, database translation, r-bloggers | Leave a comment

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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.

journal.pone.0048852.g001

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).

network

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).

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Bionetwork1

PCA normalizations

ASMS 2014 j fahrman

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.

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).

WCMC network

OPLS-DA network

g9135

loess_norm50

cough syrup network

journal.pone.0048852.g002

Treatment response network

genotype network

Cancer tissue network

PLS_DA repeated measures trajectory

network_1

netmaping

network_1

C and E figure

imDEV clouds

network

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