Create README.R

Author: huayingfang

README.R

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+################################################################################
+# File: README.R
+# Aim : A brief introduction about the usage for classo and SparCC
+#-------------------------------------------------------------------------------
+# Author: Fang Huaying (Peking University)
+# Email : [email protected]
+# Date  : 2015-01-08
+#-------------------------------------------------------------------------------
+# Package required: 
+#                   gtools for SparCC
+# Files needed: 
+#               cclasso.R for cclasso (including cclasso)
+#               SparCC.R for SparCC (including SparCC.count and SparCC.frac)
+#-------------------------------------------------------------------------------
+# Function parameter description:
+# function: cclasso
+#   Input:
+#           x ------ n x p data matrix (row/column is sample/variable)
+#                    n samples & p compositional variables
+#      counts ------ Is the compositional data matrix a count matrix? 
+#                    Default: FALSE
+#      pseudo ------ pseudo count if counts = TRUE
+#                    Default: 0.5
+#        k_cv ------ folds of cross validation
+#                    Default: 3     
+#     lam_int ------ tuning parameter interval
+#                    Default: [1e-4, 1]
+#       k_max ------ maximum iterations for golden section method
+#                    Default: 20
+#      n_boot ------ Bootstrap times
+#                    Default: 20
+#   Output: 
+#      A list structure contains:
+#       var_w ------ variance estimation
+#       cor_w ------ correlation estimation
+#      p_vals ------ p-values for elements of cor_w equal 0 or not
+#      lambda ------ final tuning parameter
+#     info_cv ------ information for cross validation
+#-------------------------------------------------------------------------------
+# function: SparCC.count
+#   input:
+#          x ------ nxp count data matrix, row is sample, col is variable
+#       imax ------ resampling times from posterior distribution 
+#                   default: 20
+#       kmax ------ max iteration steps for SparCC 
+#                   default: 10
+#      alpha ------ the threshold for strong correlation
+#                   default: 0.1
+#       Vmin ------ minimal variance if negative variance appears
+#                   default: 1e-4
+#   output: a list structure
+#      cov.w ------ covariance estimation
+#      cor.w ------ correlation estimation
+#
+# function: SparCC.frac
+#   input:
+#          x ------ nxp fraction data matrix, row is sample, col is variable
+#       kmax ------ max iteration steps for SparCC
+#                   default: 10
+#      alpha ------ the threshold for strong correlation
+#                   default: 0.1
+#       Vmin ------ minimal variance if negative variance appears
+#                   default: 1e-4
+#   output: a list structure
+#      cov.w ------ covariance estimation
+#      cor.w ------ correlation estimation
+#-------------------------------------------------------------------------------
+# Basic example
+source("R/cclasso.R");
+source("R/SparCC.R");
+# 1. generate logistic normal variables
+n <- 100;
+p <- 20;
+x <- matrix(rnorm(n * p), nrow = n); 
+x.frac <- exp(x) / rowSums(exp((x)));
+totCount <- round(runif(n = n,  min = 1000, max = 2000));
+x.count <- x.frac * totCount;
+# 2. run cclasso 
+# using fraction
+res_ccl_frac <- cclasso(x = x.frac, counts = F);
+# using counts
+res_ccl_count <- cclasso(x = x.count, counts = T);
+# 3. run SparCC.count and SparCC.frac
+res_spa_count <- SparCC.count(x = x.count);
+res_spa_frac <- SparCC.frac(x = x.frac);
+# 4. get the correlation matrix
+{
+  cat("CCLasso using fraction data:\n");
+  print(round(res_ccl_frac$cor_w, 2));
+  cat("CCLasso using count data:\n");
+  print(round(res_ccl_count$cor_w, 2));
+  cat("SparCC using fraction data:\n");
+  print(round(res_spa_frac$cor.w, 2));
+  cat("SparCC using count data:\n");
+  print(round(res_spa_count$cor.w, 2));
+}
+#-------------------------------------------------------------------------------