dirichlet_regression = function(counts, covariates, formula){ # Dirichlet multinomial regression to detect changes in cell frequencies # formula is not quoted, example: counts ~ condition # counts is a [samples x cell types] matrix # covariates holds additional data to use in the regression # #...
covariates=ep.cov, formula=counts ~ condition)$pvals# Calculate regressioncounts=as.data.frame(counts)counts$counts=DR_data(counts)data=cbind(counts,covariates)fit=DirichReg(counts~condition,data)# Get p-valuesu=summary(fit)#compared with healthy condition, 15 vars. non...
首先我们先了解下 Dirichlet-multinomial regression 。 让我们从数学层面开始： 假设从正常组织取了sample i，正常组织本身包含了 p 种cell type，假设各种cell type出现的概率为 可能会相差很大，具有‘超散布性’ （overdispersion），也就是说观测到的不同样本中成分参数的方差会显著大于多项分布...
For this, we describe a general class of hierarchical Dirichlet-multinomial regression models that use spike-and-slab priors for the selection of the significant associations. We further illustrate extensions that accommodate various parameterizations of a predictor's prior probability of inclusion and ...
multinomialRegressionSourcesofvariationA generic random effects formulation for the Dirichlet negative multinomial distribution is developed together with a convenient regression parameterization. A simulation study indicates that, even when somewhat misspecified, regression models based on the Dirichlet negative ...
VARIABLE SELECTION FOR SPARSE DIRICHLET-MULTINOMIAL REGRESSION WITH AN APPLICATION TO MICROBIOME DATA ANALYSIS With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, ... J Chen,H Li - 《Other》 被引量:...
Therefore, we can think of a TF-related topic as representing a more general model of the TF’s binding preferences, with the highest probability k-mers in the multinomial corresponding to the preferred binding signals. In Fig. 1a, the latent dimension topic 1 (red color), parameterized by ...
Previously, a Dirichlet–multinomial regression framework has been suggested to model this relationship, but it did not account for any underlying latent group structure. An underlying group structure of guts (such as enterotypes) has been observed across gut microbiome samples in which guts in the ...
Dirichlet negative multinomial regression for overdispersed correlated count datadoi:10.1093/biostatistics/kxs050Daniel FarewellV T FarewellFarewell, DM and Farewell, VT (2012). Dirichlet negative multinomial regression for overdispersed correlated count data. Biostatistics 14, 395-404....
An integrative Bayesian Dirichletmultinomial regression model for the analysis of taxonomic abundances in microbiome data. BMC Bioinform. 2017;18:94. doi:10.1186/s12859-017-1516-0.Wadsworth WD, Argiento R, Guindani M, Galloway-Pena J, Shelbourne SA, Vannucci M. An integrative Bayesian ...