TUTORIALS and DEMOs | Creative Data Solutions

TUTORIALS and DEMOs

Newest release here

Interactive Demos:

Interactive Tools:

MetaMapR
DeviumWeb

Workshops:

Lectures:

Multivariate data analysis and visualization (2012)

Overview
Dimensions of Data
Correlation and Regression
Cluster Analysis
Complexity Reduction
Principal Components Analysis (PCA)
Partial Least Squares (PLS & PLS-DA)
Predictive Modeling
Machine -Learning
Conclusion

Manuals:

Brief Introduction to Common Multivariate Approaches

R code examples:

Teaching Demos

5 responses

dksam

Dear Dmitry Grapov, thanks for a wonderful blog (of course I couldn’t understand all, but your blog is a wonderful resource to learn) thanks Dr.D.K.Samuel, Bangalore, India

April 7, 2014 at 4:14 am

Reply
Jayram

I using ImDev software, after installing all three, with excel 2013,

Error: there seem to be no r process connected to excel

January 9, 2016 at 10:08 am

Reply
- dgrapov
  
  Hi,
  
  The last verified compatibility with Excel was for versions 2003-2010. If you can configure Rexcel for 2013 then it might work, but I have not tested.
  
  -Dmitry
  
  January 16, 2016 at 2:39 am
  
  Reply
Raj

Hey there Dmitry!

Thanks a million for the content! Maybe I have missed this somewhere, but have you published the lectures already?

May 22, 2017 at 6:44 pm

Reply
- dgrapov
  
  The best place to find upto date presentations is at:
  https://www.slideshare.net/search/slideshow?searchfrom=header&q=dmitry+grapov
  
  -Dmitry
  
  June 4, 2017 at 3:17 am
  
  Reply

Leave a Reply Cancel reply

Enter your comment here...

Fill in your details below or click an icon to log in:

Email (required) (Address never made public)

Name (required)

Website

You are commenting using your WordPress.com account. ( Log Out / Change )

You are commenting using your Twitter account. ( Log Out / Change )

You are commenting using your Facebook account. ( Log Out / Change )

Connecting to %s

Follow Creative Data Solutions on WordPress.com

Top Posts & Pages

Data visualization Gallery

Treatment effects network

PCA normalizations

LOESS_span

WCMC network

known partial correlation network2

genotype network

network_1

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).

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.

ASMS 2014 j fahrman

network

network

imDEV clouds

PLS-DA NETWORK

OPLS-DA network

Bionetwork1

netmaping

loess_norm50

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.

mc 2

333

Cancer tissue network

Tissue network cancer

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).

PLS_DA repeated measures trajectory

journal.pone.0048852.g002

g9135

network_1

C and E figure

Treatment response 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).

composite2

journal.pone.0048852.g001

cough syrup network

Topics

Suggested Blogs

Creative Data Solutions

When you want to get to know and love your data

R news and tutorials contributed by hundreds of R bloggers

%d bloggers like this: