Version 1.1.8, see Changelog for details

VERSION

-1.1.7
+1.1.8

docs/projectInfo.rst

@@ -31,6 +31,9 @@ We also put the paper on *bioRxiv*, please read all methodological details here:
 Change log
 ============================
 
+Version 1.1.8 (2018-11-07)
+    - changed the call to the mlv function from the modeest package due to a breaking implementation change in version 2.3.2 that was published end of October 2018. ``diffTF`` now checks the package version for modeest and calls the functions in dependence of the specific version.
+
 Version 1.1.7 (2018-10-25)
     - the default value of the minimum number of data points for a CG bin to be included has been raised from 5 to 20 to make the variance calculation more reliable
     - various small updates to the ``summaryFinal.R`` script

src/R/analyzeTF.R

@@ -494,7 +494,21 @@ if (skipTF) {
     
           # d) Comparisons between peaks and binding sites
           
-          modeNum     = mlv(final.TF.df$l2FC, method = "mfv", na.rm = TRUE)
+          # Check the version of modeest, because version 2.3.2 introduced an implementation change that breaks things
+          
+          if (packageVersion("modeest") < "2.3.2") {
+              
+              modeNum     = mlv(final.TF.df$l2FC, method = "mfv", na.rm = TRUE)
+              
+              stopifnot(is.list(modeNum))
+              l2fc_mode = ifelse(is.null(modeNum$M), NA, modeNum$M)
+              l2fc_skewness = ifelse(is.null(modeNum$skewness), NA, modeNum$skewness)
+          } else {
+              
+              l2fc_mode = mlv(final.TF.df$l2FC, method = "mfv", na.rm = TRUE)[1]
+              l2fc_skewness = skewness(final.TF.df$l2FC, na.rm = TRUE)[1]
+          }
+          
           
           # We have to filter now by NAs because for some permutations, limma might have been unable to calculate the coefficients
           final.TF.filtered.df = dplyr::filter(final.TF.df, !is.na(l2FC))
@@ -518,10 +532,10 @@ if (skipTF) {
                                        Pos_l2FC        = nrow(final.TF.df[final.TF.df$l2FC > 0,]) / nrow(final.TF.df),
                                        Mean_l2FC       = mean(final.TF.df$l2FC, na.rm = TRUE),
                                        Median_l2FC     = median(final.TF.df$l2FC, na.rm = TRUE),
-                                       Mode_l2FC       = modeNum[[1]],
+                                       Mode_l2FC       = l2fc_mode,
                                        sd_l2FC         = sd(final.TF.df$l2FC, na.rm = TRUE),
                                        pvalue_raw      = tTest_pVal,
-                                       skewness_l2FC   = modeNum[[2]], 
+                                       skewness_l2FC   = l2fc_skewness, 
                                        T_statistic     = tTest_stat, 
                                        TFBS_num        = nrow(final.TF.df)
             )

src/R/summary1.R

@@ -146,16 +146,30 @@ if (nTFMissing == nrow(summary.df)) {
 # Replace p-values of 0 with the smallest p-value on the system
 summary.df$pvalue_raw[summary.df$pvalue_raw == 0] = .Machine$double.xmin
 
+# Check the version of modeest, because version 2.3.2 introduced an implementation change that breaks things
+
+if (packageVersion("modeest") < "2.3.2") {
+    
+    mode_peaks = mlv(peaks.df$l2FC, method = "mfv", na.rm = TRUE)
+    
+    stopifnot(is.list(mode_peaks))
+    l2fc_mode = ifelse(is.null(mode_peaks$M), NA, mode_peaks$M)
+    l2fc_skewness = ifelse(is.null(mode_peaks$skewness), NA, mode_peaks$skewness)
+} else {
+    
+    l2fc_mode = mlv(peaks.df$l2FC, method = "mfv", na.rm = TRUE)[1]
+    l2fc_skewness = skewness(peaks.df$l2FC, na.rm = TRUE)[1]
+}
+
 
-mode_peaks = mlv(peaks.df$l2FC, method = "mfv", na.rm = TRUE)
 
 summary.df = summary.df %>%
               dplyr::mutate(
                   adj_pvalue  = p.adjust(pvalue_raw, method = "fdr"),
                   Diff_mean   = Mean_l2FC    -   mean(peaks.df$l2FC, na.rm = TRUE), 
                   Diff_median = Median_l2FC  - median(peaks.df$l2FC, na.rm = TRUE),
-                  Diff_mode   = Mode_l2FC    - mode_peaks[[1]],    
-                  Diff_skew   = skewness_l2FC - mode_peaks[[2]])  %>%
+                  Diff_mode   = Mode_l2FC    - l2fc_mode,    
+                  Diff_skew   = skewness_l2FC - l2fc_skewness)  %>%
               na.omit(summary.df)
 
 summary.df = mutate_if(summary.df, is.numeric, as.character)

src/R/summaryFinal.R

@@ -471,6 +471,8 @@ for (fileCur in par.l$files_input_permResults) {
 output.global.TFs.orig$weighted_meanDifference = as.numeric(output.global.TFs.orig$weighted_meanDifference)
 output.global.TFs.orig$variance = as.numeric(output.global.TFs.orig$variance)
 output.global.TFs.orig$weighted_CD = as.numeric(output.global.TFs.orig$weighted_CD)
+output.global.TFs.orig$weighted_Tstat = as.numeric(output.global.TFs.orig$weighted_Tstat)
+
 
 
 # Remove rows with NA