Mark A. Miller wrote: > My laboratory is measuring the abundance of various proteins in the > blood from either healthy individuals or from individuals with various > diseases. I would like to determine which proteins, if any, have > significantly different abundances between the healthy and diseased > individuals. Currently, one of my colleagues is performing an ANOVA on > each protein with MS Excel. I would like to analyze the data sets with > a scriptable tool, like R. I could use another tool, but I am trying > to stick to open source. I have basic procedural programming skills (I > do a lot of PHP/MySQL), but I'm not very good with anything that > requires thinking in vectors and matrices. > One approach I'm imagining is looping through all of the columns and > doing an ANOVA, like my colleague is doing manually. I have heard > other people in my field talking about other tests for this kind of > data. Would a Kruskal-Wallis test, hierarchical data clustering, > principal component analysis, or random forests be appropriate for the > question I am asking? If so, how would I write a reusable script for > the test? The data table will always have the same basic structure, > but the number of proteins could vary, as could the number of > conditions or the number of repeats within each condition. > I especially want to export the results of this test in a format > roughly like the example below. (I'd like the mean of each protein's > abundance for each condition, some measure of variability within each > condition, and a measure of significance for whether the protein > abundances are different between conditions.) I have gotten to the > point of doing an ANOVA on a single protein R and viewing the results > interactively, but I have no idea how to analyze the differences for > all of the proteins (in a loop, or all at once) or how to save the > results to a file. > Any suggestions? > > Example input (tab delimited) > condition protA protB protC protD protE protF protG protH > healthy1 11111 22222 33333 70681 61735 66666 77777 88888 > healthy1 12121 21111 32132 57230 69715 67890 87878 98989 > healthy1 10101 20202 30303 67223 51967 65656 78900 111111 > healthy2 12345 23111 32100 65931 67650 60001 80001 101010 > healthy2 13333 21231 34111 58761 54086 60002 80002 122222 > healthy2 13232 20101 30009 68752 70360 60003 80003 91919 > asthma 32132 19889 30733 59959 71783 60237 65603 20374 > asthma 34344 20483 31182 70531 59630 40445 56370 98404 > asthma 39999 20464 29793 58395 66976 50577 39908 65367 > diabetes 10000 20102 29486 51260 68447 42960 50875 216227 > diabetes 10111 19143 31275 52573 55459 71337 53090 151505 > diabetes 10001 21790 31470 54222 57318 64058 44166 207427 > diabetes 15555 20123 30131 59882 71191 46203 44633 197430 > acne 12222 31221 51381 64431 55016 43463 60388 74243 > acne 12221 30535 49199 61419 65096 71551 41811 104317 > acne 10001 30649 49199 56731 69871 61816 44321 125068 > > > Desired output > condition protA protB protC protD protE protF protG protH > healthy1.mean > healthy1.sd > healthy1.pval > healthy2.mean > healthy2.sd > healthy2.pval > asthma.mean > asthma.sd > asthma.pval > diabetes.mean > diabetes.sd > diabetes.pval > acne.mean > acne.sd > acne.pval > Hi, Mark. With data like these, you will want to look at the BioConductor (http://www.bioconductor.org) project. If you transpose your matrix so that individuals are in columns and proteins are in rows, then you have data in exactly the same form as a microarray analysis, so most of the tools in BioConductor will apply. In addition, there are tools specifically designed for mass-spec data. For your question directly, look at the limma package; it will do a protein-by-protein anova for you. There is an extensive user guide available.
Sean ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
