version 0.2.5

DESCRIPTION

@@ -0,0 +1,16 @@
+Package: DPBBM
+Type: Package
+Title: Dirichlet Process Beta-Binomial Mixture
+Version: 0.2.5
+Date: 2016-09-21
+Author: Lin Zhang
+Maintainer: Lin Zhang <lin.zhang@cumt.edu.cn>
+Depends: R (>= 3.1.0)
+Imports: tmvtnorm, VGAM, gplots, CEoptim
+Description: Beta-binomial Mixture Model is used to infer the pattern from count data.
+		It can be used for clustering of RNA methylation sequencing data. 
+License: GPL (>= 2)
+NeedsCompilation: no
+Packaged: 2016-09-28 15:21:06 UTC; lin.zhang
+Repository: CRAN
+Date/Publication: 2016-09-29 08:02:30

MD5

@@ -0,0 +1,19 @@
+85deb92e49f8e80d11b68243156f69c3 *DESCRIPTION
+fcd59f34600633e5b452a9bf4e4cb3d4 *NAMESPACE
+788cd67fd78de3aafe4948565b0ff337 *NEWS
+78f496dd38ec9c7aabeeb080122867d9 *R/BCubed_metric.r
+04011008f379c8feaf9e067a109832d3 *R/DPBBM-internal.R
+3a3617deef264facbf8f95f85a994d62 *R/bbm_data_generate.r
+f5ff34ab69bca1f5b44a9a867ef977cc *R/choose_cluster_id_by_mode.r
+fc08705e0767e837fa027a32304390d3 *R/dpbbm_mc_iterations.r
+7957a39ac67fb28b507269aedd6eafaa *R/dpbbm_mc_single_iter.r
+357b514d059c7822b73c99c3d4a3ccf0 *R/dpbbm_update_likelihood.r
+6e29979330db426d7edd337475475716 *R/dpbbm_update_tau.r
+92fe9c86d6e3d8a4ec99e895dcfaf640 *R/generate_u_param.r
+1f2d26caa65c2917c620f1ad90244b06 *inst/doc/DPBBM_example.pdf
+bee6206963313d57ffe574bc8a3390f7 *inst/extdata/DPBBM_example.Rmd
+d5ef12392295c2229a4688452167ac99 *inst/extdata/DPBBM_example.html
+bbcc89bad9adb4ebd99a0f5255cd3a30 *man/BCubed_metric.Rd
+df26148160ed7c2c26598927518d999b *man/DPBBM-package.Rd
+9e0994ac718f4cc6dea60772189ebe95 *man/bbm_data_generate.Rd
+20c8fa19eb4a6e3d6cbe1d6b19fc1ff6 *man/dpbbm_mc_iterations.Rd

NAMESPACE

@@ -0,0 +1,10 @@
+exportPattern("^[[:alpha:]]+")
+export(dpbbm_mc_iterations)
+export(bbm_data_generate)
+export(BCubed_metric)
+import(tmvtnorm)
+import(VGAM)
+import(gplots)
+importFrom(CEoptim,dirichletrnd)
+importFrom("graphics", "par")
+importFrom("stats", "cor", "rbeta", "rgamma", "rnbinom", "runif")

NEWS

@@ -0,0 +1,10 @@
+
+version 0.0.13 (2016-09-12)
+- bug fix
+
+version 0.1.0 (2016-09-18)
+- pre-release
+
+version 0.2.0 (2016-09-21)
+- added "Getting started with DPBBM package", including HTML, Rmd and PDF.
+

R/BCubed_metric.r

@@ -0,0 +1,45 @@
+###########################################################################
+# calculate BCubed metric for clustering evaluation
+# input : L, C, alpha
+#       L - real label of classes
+#       C - classification label of samples
+#       alpha - F metric parameter
+# output: F metric score
+
+BCubed_metric <- function(L, C, alpha){
+  snL <- length(L)
+  snC <- length(C)
+
+  if(snL != snC){
+    stop("length of category does not comply with length of cluster")
+    geterrmessage()
+  }
+
+  # define the correctness of the relation between i and j
+  Correctness <- matrix(nrow = snL, ncol = snC)
+  ncluster <- c()
+  ncategory <- c()
+  for( i in 1:snL ){
+    for( j in 1:snC ){
+
+      if((L[i] == L[j]) & (C[i] == C[j])) Correctness[i,j] <- 1
+      else Correctness[i,j] <- 0
+    }
+  }
+
+  # define the num of each cluster
+  for(i in 1:snC){
+    ncluster[i] <- length(which(C == C[i]))
+  }
+
+  # define the num of each category
+  for(i in 1:snL){
+    ncategory[i] <- length(which(L[i] == L))
+  }
+
+  Precision <- mean(rowSums(Correctness) / ncluster)
+  Recall <- mean(colSums(Correctness) / ncategory)
+  F <- 1/(alpha/Precision+(1-alpha)/Recall)
+
+  return(F)
+}

R/DPBBM-internal.R

@@ -0,0 +1,126 @@
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R/bbm_data_generate.r

@@ -0,0 +1,68 @@
+##########################################################################
+# generate data based on beta binomial mixture model
+bbm_data_generate<- function(S=3, G=50, K=3, prob=rep(1,times=3),
+                              alpha_band=c(2,6),
+                              beta_band=c(2,6),
+                              nb_mu=100,nb_size=0.2, plotf = FALSE, 
+                              max_cor=0.5){
+
+  prob = prob/sum(prob)
+  ff <- "repeat"
+  ct <- 1 # iteration limitation
+  while (ff=="repeat") {
+    # generate the starting point
+    # generate cluster ID
+    gamma <- sample.int(K, size = G, replace = TRUE, prob = prob)  
+    # generate alpha
+    alpha <- exp(matrix(runif(K*S, min = alpha_band[1], max = alpha_band[2]),nrow=K))
+    # generate_u_param(runif(S, min = alpha_band[1], max = alpha_band[2]), rep(4, S), n = K)
+    # alpha <- exp(abs(alpha))
+    #   
+
+    # generate beta
+    beta <- exp(matrix(runif(K*S, min = beta_band[1], max = beta_band[2]),nrow=K)) 
+    # generate_u_param(runif(S, min = beta_band[1], max = beta_band[2]),  rep(4, S), n = K)
+    # beta <- exp(abs(beta))
+
+    mu <- alpha/(alpha+beta) # parameter for betabinomial
+    a <- abs(cor(t(mu)))
+    ID <- upper.tri(a,diag=F)
+    m <- a[ID]
+    # min_c <- min(m)
+    max_c = max(m) # keep the sampled data relatively correlated, not too high, not too low
+    if (max_c < max_cor) {ff = "go"; 
+    # if (min_c > max_cor) {ff = "go"; 
+    print(paste("after tried",ct,"times"))
+    print(paste("sample data generated with correlation smaller than:",max_cor))}
+    # print(paste("sample data generated with correlation larger than:",max_cor))}
+    if (ct > 1000) {ff = "go"; 
+    print(paste("Fail to generate data with correlation smaller than:",max_cor))
+    print("clusters can be highly correlated ...")
+    }
+    ct <- ct + 1
+  }
+
+  # generate the number of reads (total number of reads for samples)
+  n <- matrix(rnbinom(G*S, mu = nb_mu, size=nb_size),nrow=G) # generate the number of reads
+  alpha_g <- alpha[gamma,]
+  beta_g <- beta[gamma,]
+
+  # generate the random number (number of reads for IP sample)
+  k <- matrix(rbetabinom.ab(n=G*S, size=n, shape1=alpha_g, shape2=beta_g, .dontuse.prob = NULL),nrow=G)
+
+  if (plotf == TRUE) {
+    p <- k/n
+    par(mfrow= c(K,1))
+    for (m in 1:K) {
+      plot( rep(1:S,sum(gamma==m)),t(p[gamma==m,]), main=m)
+    }
+  }
+
+
+  # save the data
+  mat=list(S=S, G=G, K=K, alpha_band=alpha_band,
+            beta_band=beta_band, nb_mu=nb_mu, nb_size=nb_size, 
+            gamma=gamma, alpha=alpha, beta=beta,
+            k=k, n=n, c=n-k)
+  return(mat)
+}

R/choose_cluster_id_by_mode.r

@@ -0,0 +1,19 @@
+# function used to get the mode for each site
+#
+.getmode <- function(v) {
+  uniqv <- unique(v)
+  uniqv[which.max(tabulate(match(v, uniqv)))]
+}
+
+.choose_cluster_id_by_mode <- function(param){
+
+  num_iter <- length(param)
+  num_site <- length(param[[num_iter]]$c)
+  c_mat <- matrix(NA, nrow= num_iter-1000, ncol = num_site)
+  for (i in 1:(num_iter-1000)){
+    c_mat[i,] <- param[[i+1000]]$c
+  }
+
+  c_final <- apply(c_mat, 2, .getmode)
+  return(c_final)
+}