Arppa Reference R Arppa The nice thing about this folder is that it is special compared with other standard things. this folder will be used by r duing the installation. to my understanding, this folder will not be checked and subdirectories in this folder will be copied to the target location during installation and moved up by one level to the top level directory. Browse package contents vignettes man pages api and functions files ffeng23 arppa documentation built on may 16, 2019, 12:50 p.m.
Arppa Fcom We developed a high throughput cetsa using an acoustic rppa (ht cetsa arppa) protocol that is compatible with 96 and 384 well microplates from start to finish, using low speed centrifugation to remove thermally destabilized proteins. We developed a high throughput cetsa using an acoustic rppa (ht cetsa arppa) protocol that is compatible with 96 and 384 well microplates from start to finish, using low speed centrifugation. ###residualfunc.r###in this file, we define all the residual functions for 5pl fitting####for gainadjust.r project.###started 8 7 2017 feng @ boston university## > don't export####function not working, need manually change to work!!!# it assumes #===> ####always assume the first set of x and y are the reference ones####meaning without be. It could possible happen that yj i is not inside# reference range, meaning yj i
Arppa 29 10 2022 Lippu Fi ###residualfunc.r###in this file, we define all the residual functions for 5pl fitting####for gainadjust.r project.###started 8 7 2017 feng @ boston university## > don't export####function not working, need manually change to work!!!# it assumes #===> ####always assume the first set of x and y are the reference ones####meaning without be. It could possible happen that yj i is not inside# reference range, meaning yj i
Arppa To cope with limitations of throughput and protein amount requirements, we developed a new coupled assay combining the advantages of a nanoacoustic transfer system and reverse phase protein array. #####description for this file runstats.r###### this file is used to define the functions to run statistical analysis # in order to identify the proteins differentially binding to the testing abs############### # the following section is used to act like a holder for# importing libraryies# package limma is necessary for elist#' @title an empty. ##read load previous saved data including the linear regression## and then plot for diagnostics#### =========> here we only use the negative control cases as the example to show the assumptions## feng@bu#### ## dec 16, 2916, update## for the data analysis for performance, we changed to use the function for analysis## see the functionanalysis.r for function definition.## we also save the original code to modeltesting run4.r.old########################################## library (arppa)##first checking for homoscedastical datasetngene< 2000ntreatment< 2#number of different betasamplesize< 5##this is the number of repeats for each groupalpha.mean< 0#variance for alpha prioralpha.sigma< 3beta.mean< 0beta.sigma< 2#variance for beta priorgamma.sigma< 10# the unscaled factor for gamma given gamma<=>0prob.nonzero< 0.02#priors for variance distributiond0< 5s0< 2#####================assumption checking======= negative case#using the simulated data to show the linear regression #assumptions before and after the data transformation variance stabilization###start doing the simulation set.seed (2004);i< 1 cat ("doing ",i," ",repeats," analyses .\n")dataexp en list< simulateexpression(ngene,ntreatment,samplesize,control.negative= true,control.isotype= false,control.index= c (1),alpha.mean=alpha.mean,alpha.sigma=alpha.sigma,beta.mean=beta.mean,beta.sigma=beta.sigma,prob.nonzero=prob.nonzero,gamma.sigma=gamma.sigma,#epsilon.si=epsilon.si,epsilon.d0=d0,epsilon.s0=s0)#calculate the sample variance from the datadataexp en< dataexp en list [[1]]#now do the transformationdatatransformed< transformdata(dataexp en,ntreatment,samplesize)dexp en< matrix2dframe(dataexp en,ntreatment,samplesize);#untransformed #now do the linear regression with interaction lreg en< lm (exp ~gene* group, data =dexp en)lregsm< summary (lreg en)###dianostic plots setwd ("h:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar negativecon") pdf (file ="lregdiag.pdf")op< par (mfrow= c (2,2)) plot (lreg en) par (op) dev.off ()#we have the data, what to do.#reformat the data from data matrix to #dataframe for the linear regressdexp en< matrix2dframe(datatransformed,ntreatment,samplesize);#transformed#now do the linear regression with interaction lreg en< lm (exp ~gene* group, data =dexp en)lregsm< summary (lreg en)###dianostic plots setwd ("h:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar negativecon trans") pdf (file ="lregdiag.pdf")op< par (mfrow= c (2,2)) plot (lreg en) par (op) dev.off ()#####################done with the assumption checking## # start doing the analysis #setwd ("h:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar negativecon trans");filepath< "e:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar negativecon trans";rtlist< analyzedata(path=filepath,filename="result ",repeats=100,sample.size=5,proportion.nonzero=0.02,object.load="lsttosave", mode =1);########=================isotype control data analysis#setwd ("h:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar isotypecon trans");filepath< "e:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar isotypecon trans";rtlist< analyzedata(path=filepath,filename="result ",repeats=100,sample.size=5,proportion.nonzero=0.02,object.load="lsttosave ei", mode =1);#######====================no control #setwd ("h:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar uncontrol trans");filepath< "e:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar uncontrol trans";rtlist< analyzedata(path=filepath,filename="result ",repeats=100,sample.size=5,proportion.nonzero=0.02,object.load="lsttosave enc", mode =1);# << mode of 1filepath< "e:\\feng\\lab\\hg\\proteinarray masa\\arppa\\data\\unequalvar uncontrol trans";rtlist< analyzedata(path=filepath,filename="result ",repeats=100,sample.size=5,proportion.nonzero=0.02,object.load="lsttosave enc", mode =2);#<< mode of 2. Treated lysates are mixed at different percentages with untreated cell lysates ranging from 0 to 100% to generate a standard reference curve for activated proteins in different pathways.
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