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slrvv · Apr 17, 2023 · 8dcfbe3 · 8dcfbe3
1 parent 823ecaf
commit 8dcfbe3
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Showing 6 changed files with 20 additions and 17 deletions.
diff --git a/Images/Figure1-export.png b/Images/Figure1-export.png
diff --git a/R/computeCellTypeFeatures.R b/R/computeCellTypeFeatures.R
@@ -118,20 +118,20 @@ computeCellTypeFeatures <- function(metaData,
   list_enh <- as.data.frame(unique(featuresGeneric$enhancer_id))
   colnames(list_enh) <- c("enhancer_id")
 
-
+  print(head(regions_enhancer))
   cat("Getting the CRUP-EP scores for enhancer, promoter and the regulatory
       distance")
 
   #Crup enhancer scores for enhancer
   crup_EP_enh <- compute_crup_enhancer(regions_enhancer,
                                        list_enh,
                                        crupScores)
-
   crup_features <- merge(featuresGeneric,
                         crup_EP_enh,
                         by.x = "enhancer_id",
                         by.y = "cres_name",
                         all.x = TRUE)
+  print(head(crup_features))
   #CRUP enhancer scores for promoter
   crup_EP_prom <- compute_crup_promoter(regions_prom,
                                         list_prom,
@@ -142,9 +142,10 @@ computeCellTypeFeatures <- function(metaData,
                         by.x = "gene_id2",
                         by.y = "gene_name",
                         all.x = TRUE)
+  print(head(crup_features))
   ##crup enhancer scores for distance
   crup_features <- compute_crup_reg_distance_enh(crup_features, crupScores)
-
+  print(head(crup_features))
 
   ##Get CRUP promoter probabilities
 

diff --git a/R/createPairs.R b/R/createPairs.R
@@ -41,7 +41,7 @@ createPairs <- function(gene) {
   RSQLite::dbDisconnect(conn)
   gene$startTts <- integer(nrow(gene))
   gene$endTts <- integer(nrow(gene))
-  for (i in seq_along(nrow(gene)))
+  for (i in seq_len(nrow(gene)))
  {
     ##extend 500 kb to the left of tts
     if (gene$transcription_start[i] <= 500000) {

diff --git a/R/functions.R b/R/functions.R
@@ -247,23 +247,23 @@ compute_crup_reg_distance_enh <- function(input, prediction) {
   bins <- as.data.frame(table(cres_EP$between))
 
   cres_EP1 <- cres_EP[cres_EP$EP_reg_distance > 0.5, ]
-
+  print(cres_EP1) 
   if (nrow(cres_EP1) == 0) {
 
-    bins_pos <- as.data.frame(matrix(c(c(seq(1:nrow(input)),
+    bins_pos <- as.data.frame(matrix(c(seq(1:nrow(input)),
                                          rep(0, times = nrow(input))),
                                        nrow = nrow(input),
-                                       ncol =  2)))
+                                       ncol =  2))
     colnames(bins_pos) <- c("Var1", "Freq")
   } else {
     bins_pos <- as.data.frame(table(cres_EP1$between))
   }
-
+  print(head(bins_pos))
 
   all_bins <- merge(bins, bins_pos, by.x = "Var1", by.y = "Var1", all.x = TRUE)
   all_bins[is.na(all_bins)] <- 0
   colnames(all_bins) <- c("pair", "bins", "bins_pos")
-
+  print(head(all_bins))
 
   input$reg_dist_enh <- all_bins$bins_pos
   input$norm_reg_dist_enh <- all_bins$bins_pos / all_bins$bins
@@ -305,10 +305,10 @@ compute_crup_reg_distance_prom <- function(input, prediction) {
 
   if (nrow(cres_EP1) == 0) {
 
-    bins_pos <- as.data.frame(matrix(c(c(seq(1:nrow(input)),
+    bins_pos <- as.data.frame(matrix(c(seq(1:nrow(input)),
                                          rep(0, times = nrow(input))),
                                        nrow = nrow(input),
-                                       ncol =  2)))
+                                       ncol =  2))
     colnames(bins_pos) <- c("Var1", "Freq")
   } else {
     bins_pos <- as.data.frame(table(cres_EP1$between))

diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@ ChIP-seq data for three histone modifications for the cell type of interest.
  CENTRE uses various available datasets and extracts cell-type agnostic 
 statistics to complement the cell-type specific information.
 
-![title](Images/Figure1-export.tiff)
+![title](Images/Figure1-export.png)
 
 ### Contact
 
@@ -69,7 +69,8 @@ Note: If the installation of any of the dependencies of CENTRE fails
 try running the script CENTRE/install/install_CENTRE.R
 
 ## References
-Andersson,R. et al. (2014) An atlas of active enhancers across human cell types and tissues. Nature, 507, 455–461.
-Thurman,R.E. et al. (2012) The accessible chromatin landscape of the human genome. Nature, 489, 75–82.
-Sheffield,N.C. et al. (2013) Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res., 23, 777–788.
+
+- Andersson,R. et al. (2014) An atlas of active enhancers across human cell types and tissues. Nature, 507, 455–461.
+- Thurman,R.E. et al. (2012) The accessible chromatin landscape of the human genome. Nature, 489, 75–82.
+- Sheffield,N.C. et al. (2013) Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res., 23, 777–788.
 
diff --git a/vignettes/CENTRE_vignette.Rmd b/vignettes/CENTRE_vignette.Rmd
@@ -12,7 +12,7 @@ output:
   rmarkdown::html_document
 abstract: 
 vignette: |
-  %\VignetteIndexEntry{crupR-vignette}
+  %\VignetteIndexEntry{Centre-vignette}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
   
@@ -51,7 +51,8 @@ and Annotation.db. To do this run the following:
 CENTRE::downloadPrecomputedData(method = "curl")
 # Make sure whatever method you use to download is available on your system
 ```
-
+Or download the data from http://owww.molgen.mpg.de/~CENTRE_data/PrecomputedData.db
+and http://owww.molgen.mpg.de/~CENTRE_data/Annotation.db add it to the /inst/extdata folder.
 
 # Run the *CENTRE* pipeline