I sat in on an interesting webinar by Jian Cao a few weeks ago on using the mixed model platform, during which I learned that you actually don't need to set up repeated measures ANOVA designs under MANOVA. This opens up more flexibility, so I wanted to try it. I have a series of corals ("pucks" in the attached data file) that were repeatedly measured (as color) over time, and I was interested in testing for the fixed effects of treatment (two temperatures), genotype (5-6 genotypes), time (calendar dates), and their interaction. I know how to do this under the MANOVA platform, but if I add the two fixed effects to the mixed model platform, then "Date" as the repeated measure, one of two things happens. It either ONLY gives the output of the fixed effects (i.e., NOT the effect of time), OR, if I include "Date" as a fixed factor as well, it causes it to crash, potentially since the software sees "Date" in two places. What Jian evidently did was trick JMP by calling "Date" something else in his example. I think he had "Time" (which was date x time) as the repeated measure and then "Date" (=date only) as a fixed factor. Surely I don't actually have to do recode a dummy variable like this every time?! I guess I could recode "Date" to "experiment day" and then have "date" be the repeated measure and "experiment day" be in the fixed factor model, but it seems like there must be a simpler way. In other words, how do I set it up under the mixed model platform to where I also get the time effect (as can be done under the MANOVA platform)? Or maybe interaction effects with the repeated measure are NOT possible under mixed model (and only with MANOVA)?  ... View more

I routinely use response screening to quickly look at tons of comparisons of experimental factors (e.g., temperature) on protein concentrations. However, I now have binomial (presence/absence) data (either 0 or 1), and I see that you can change the distribution to Cauchy or Poisson at the bottom, but I don't see binomial. I see "Same Y scale" though I am not sure if that would do it. Obviously, I could do individual generalized linear models under "Fit model" but I feel like there could be a faster option is response screening is inappropriate. IN this case, there are only 22 proteins, so it's not the end of the world to do it manually, but for when I have hundreds of proteins, it would be nice to use something akin to response screening. GLM with a column switcher to quickly go through proteins could be one slightly slower option. Any other ideas? ... View more

Given my ignorance of how computers actually work, this may be beyond what JMP developers can do and might be one for the Apple people, but one thing that would benefit me greatly would be the ability to use the Mac's Spotlight feature to search text within JMP files. Part of this is my fault for having so many JMP files that I can't keep track of what's in what. Spotlight will find words in the JMP file names but not actually within the files themselves (unlike Word, Excel, and other programs, in which you could conceivably even search by number strings, I'm guessing). Or perhaps this is already doable, just not via Spotlight? ... View more

I just saw a cool webinar today on using some features in R that are not available in JMP via JSL. I am specifically talking about t-SNE: Data visualization with t-SNE and UMAP . I am running JMP 14 on Mac OS 10.13 (High Sierra) and, according to the add-in link above, the R version to get is 3.3.3, so I downloaded that, restarted, and followed this: https://www.jmp.com/support/help/en/15.1/index.shtml#page/jmp/installing-r.shtml#. I cannot get even the very simple script in this trouble-shooting guide to work, not an error, just nothing. The JMP scripting guide is written for Windows, but I tried manually setting the "set environmental variable" to the applications folder, but that did not work either. R works and is stored in my Applications folder. I had older versions of R on my computer, but I deleted them (and restarted afterwards). I made it to where I can read and write files (it is my personal computer, and I am the administrator), yet even "R Init ();" does nothing.  As of a few months ago, JMP14, R ver. 3.3.3., and MAC OS10.13 were all theoretically compatible. Did anything happen in the meantime that would have made it to where I can no longer use R via the script window? ... View more

Coral Biologist Anderson Mayfield has dedicated his career to preserving, protecting and restoring the world’s oceans and aquatic resources. In this segment of JMP On Air’s Earth Day Episode, Dr. Mayfield shows how he is using data analytics (and JMP Pro) to predict and positively influence the health of the world’s coral reefs. You’ll also learn more about how better understanding global threats, (e.g. climate change, human population growth, overfishing, water pollution and disease) can help us build up resilience in our threatened coral environments. ... View more

Hello all,     This is more "spit balling" than anything, so here goes. In a nutshell, I am trying to develop a diagnostic test for reef corals so that we may know which ones will likely die as a result of climate change and which will not. From a series of experiments carried out in the laboratory, I now know the protein signatures associated with resilient and stress-sensitive corals so that, in theory, I may now have the capacity to go out into the ocean, sample new colonies that I don't have any a priori knowledge on, and make predictions about their health.    Analytically, I have tried quite a few things with the proteins I have profiled, large proportions of which are collinear (making standard regression approaches poorly suited). It seems like using the discriminant analysis approach for "healthy" vs. "stressed" coral datasets could give me the answer that I want. I played around with stepwise discriminant analysis and basically tried to identify the minimum number of proteins that would 100% correctly sort the samples by health status. Am I interpreting this correctly? It seems like discriminant analysis is basically a MANOVA with some sort of predictive function (though MANOVA cannot be used in this case since I have 272 proteins measured in only 11 samples).    I saw a great JMP webinar today on generalized regression, so I have played around with that. I set my Y as health status (binomial: healthy vs. stressed) and used the 272 proteins as x variables. In some cases, the final set of "most informative" proteins were similar to those derived from DCA, but there tended to be more (on the order of five proteins could basically be used to build a model that could accurately predict coral health). One issue, of course, is that I am using samples where I already know the answer (i.e., whether the corals lived or died). Therefore, I risk having overfit it. Discriminant analysis seems to be literally just fitting the data entirely, perhaps not leaving any wiggle room for randomness or noise. Since I created a validation column for generalized regression, that should, in part, reduce the possibility of over-fitting, right?   And then there is partial least squares, but every time I've used PLS with OMICS (transcriptome or proteome) datasets, the R2 is always pitifully low, like 0.05-0.07. I saw a really excellent webinar last week on generalized regression in which the presenter basically use Formula Depot to compile the models derived from a number of approaches, which basically gave me the idea to do this sort of analysis. I guess, in closing, my main question is, when you are trying to make predictive models for a binomial (healthy vs. sick, in this case) response with hundreds to thousands of predictors, is any one of these methods considered the "gold standard?" It might also be worth mentioning that the data are typically non-normally distributed. This dataset is very small, and, if you click on the PCA, the unhealthy samples are very easily distinguished.  ... View more

This might be more of a "wish list" post, but I am interested in the capacity of carrying out correlation network analysis in JMP with OMICS datasets (in my case, protein concentration data). There is a popular approach called WGCNA ( weighted gene co-expression network analysis) , described as follows (from https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/ 😞 "Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial."   It seems to me that this is just a fancy way of clustering by covariance, which I DO know how to do with JMP Pro 14 (I've looked at clustering across my sample set=biological replicates alongside clustering of the various proteins whose concentrations I measured in each sample). Basically, I like what the authors have done in the first figure I've attached: : clustered genes into modules based on covariance and then looked at correlation against categorical properties (time, temperature, and thermotolerance). In this example, the dakgrey module featured 2,053 genes and was positively correlated with temperature. I feel like this sort of "sexified" bioinformatics analysis should be achievable in JMP Pro (or even in regular JMP), right? I am wondering if their "module membership measure" is essentially the eigenvector value or something along those lines. From my data the samples (left side) are in two clusters based on their proteome profiles, whereas on the x axis, you can see 10-12 general clusters of proteins (I could transpose the dataset and have each protein assigned a cluster).  Basically, I want to look at module (cluster?) correlations across temperature, time, and genotype in this attached dataset to where I can identify clusters of proteins that correlate with these experimental factors of interest. It actually seems like partial least squares could be used for this, too.....Any ideas out there? ... View more

I really love the Venn diagram add-in, and I know you can "copy to journal," but would it be possible to enable "copy to script" for them? I know it's not a big deal to start from scratch for simple Venn diagrams, but sometimes I have to create more complex ones, in which case just simply clicking on the script saved in the file would greatly speed things up (otherwise, I need to remember how to set it up properly in the table!).  ... View more