@@ -1,6 +1,6 @@

 Package: GRaNIE

 Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNA-seq data

-Version: 1.1.8

+Version: 1.1.9

 Encoding: UTF-8

 Authors@R: c(person("Christian", "Arnold", email =

         "chrarnold@web.de", role = c("cre","aut")),

@@ -618,7 +618,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

 #' @param maxNormalizedMean_peaks Numeric or \code{NULL}. Default \code{NULL}. Maximum mean across all samples for a peak to be retained for the normalized counts table. Set to \code{NULL} for not applying the filter.

 #' @param minNormalizedMeanRNA Numeric or \code{NULL}. Default 5. Minimum mean across all samples for a gene to be retained for the normalized counts table. Set to \code{NULL} for not applying the filter.

 #' @param maxNormalizedMeanRNA Numeric or \code{NULL}. Default \code{NULL}. Maximum mean across all samples for a gene to be retained for the normalized counts table. Set to \code{NULL} for not applying the filter.

-#' @param chrToKeep_peaks Character vector. Default \code{c(paste0("chr", 1:22), "chrX", "chrY")}. Vector of chromosomes that peaks are allowed to come from. This filter can be used to filter sex chromosomes from the peaks, for example.

+#' @param chrToKeep_peaks Character vector or \code{NULL}. Default \code{c(paste0("chr", 1:22), "chrX", "chrY")}. Vector of chromosomes that peaks are allowed to come from. This filter can be used to filter sex chromosomes from the peaks, for example.

 #' @param minSize_peaks Integer or \code{NULL}. Default \code{NULL}. Minimum peak size (width, end - start) for a peak to be retained. Set to \code{NULL} for not applying the filter.

 #' @param maxSize_peaks Integer or \code{NULL}. Default 10000. Maximum peak size (width, end - start) for a peak to be retained. Set to \code{NULL} for not applying the filter.

 #' @param minCV_peaks Numeric or \code{NULL}. Default \code{NULL}. Minimum CV (coefficient of variation, a unitless measure of variation) for a peak to be retained. Set to \code{NULL} for not applying the filter.

@@ -643,11 +643,11 @@ filterData <- function (GRN,

   GRN = .addFunctionLogToObject(GRN) 

   checkmate::assertClass(GRN, "GRN")

-  checkmate::assertNumber(minNormalizedMean_peaks, lower = 0)

-  checkmate::assertNumber(minNormalizedMeanRNA, lower = 0)

+  checkmate::assertNumber(minNormalizedMean_peaks, lower = 0, null.ok = TRUE)

+  checkmate::assertNumber(minNormalizedMeanRNA, lower = 0, null.ok = TRUE)

   checkmate::assertNumber(maxNormalizedMean_peaks, lower = minNormalizedMean_peaks , null.ok = TRUE)

   checkmate::assertNumber(maxNormalizedMeanRNA, lower = minNormalizedMeanRNA, null.ok = TRUE)

-  checkmate::assertCharacter(chrToKeep_peaks, min.len = 1, any.missing = FALSE)

+  checkmate::assertCharacter(chrToKeep_peaks, min.len = 1, any.missing = FALSE, null.ok = TRUE)

   checkmate::assertIntegerish(minSize_peaks, lower = 1, null.ok = TRUE)

   checkmate::assertIntegerish(maxSize_peaks, lower = dplyr::if_else(is.null(minSize_peaks), 1, minSize_peaks), null.ok = TRUE)

   checkmate::assertNumber(minCV_peaks, lower = 0, null.ok = TRUE)

@@ -729,7 +729,13 @@ filterData <- function (GRN,

     minSize_peaks = 1

   }

-  futile.logger::flog.info(paste0("Filter and sort peaks and remain only those on the following chromosomes: ", paste0(chrToKeep, collapse = ",")))

+  if (is.null(chrToKeep)) {

+    chrToKeep = GRN@data$peaks$consensusPeaks %>% dplyr::pull(chr) %>% unique()

+  } else {

+    futile.logger::flog.info(paste0("Filter and sort peaks and remain only those on the following chromosomes: ", paste0(chrToKeep, collapse = ",")))

+  }

+  

+

   futile.logger::flog.info(paste0("Filter and sort peaks by size and remain only those smaller than : ", maxSize_peaks))

   futile.logger::flog.info(paste0(" Number of peaks before filtering: ", nrow(GRN@data$peaks$consensusPeaks)))

   ids = strsplit(GRN@data$peaks$consensusPeaks %>% dplyr::pull(!!(idColumn)), split = ":", fixed = TRUE)

@@ -798,7 +804,9 @@ filterData <- function (GRN,

   if (is.null(minMean)) {

-    minMean = 0

+    

+    # As data can be pre-normalized, set the minimum to a very small value so the filter is effectively off

+    minMean = -9e+99

   }

   if (is.null(maxMean)) {

@@ -838,19 +846,23 @@ filterData <- function (GRN,

     futile.logger::flog.info(paste0("  Filter genes by CV: Min = ", minCV, ", Max = ", maxCV))

   }

+  messageMean = paste0("  Filter genes by mean:")

   if (is.null(minMean)) {

-    minMean = 0

+    minMean = -9e+99

+  } else {

+    messageMean = paste0(messageMean, " Min = ", minMean)

   }

-  

+

   if (is.null(maxMean)) {

-    futile.logger::flog.info(paste0("  Filter genes by mean: Min = ", minMean))

     maxMean = 9e+99

   } else {

-    futile.logger::flog.info(paste0("  Filter genes by mean: Min = ", minMean, ", Max = ", maxMean))  

+    messageMean = paste0(messageMean, " Max = ", maxMean)

   }   

+  futile.logger::flog.info(messageMean)

+  

   genesFiltered = dplyr::filter(GRN@annotation$genes, 

                                 gene.CV >= minCV, gene.CV <= maxCV, 

@@ -2507,7 +2507,7 @@ plotCommunitiesStats <- function(GRN, outputFolder = NULL, basenameOutput = NULL

 #' Similarly to \code{\link{plotGeneralEnrichment}}, the results of the community-based enrichment analysis are plotted.. By default, the results for the 10 largest communities are displayed. Additionally, if a general enrichment analysis was previously generated, this function plots an additional heatmap to compare the general enrichment with the community based enrichment. A reduced version of this heatmap is also produced where terms are filtered out to improve visibility and display and highlight the most significant terms.

 #' 

 #' @inheritParams plotGeneralEnrichment

-#' @param display Character. Default \code{"byRank"}. One of: \code{"byRank"}, \code{"byLabel"}. Specify whether the communities will by displayed based on their rank, where the largest community (with most vertices) would have a rank of 1, or by their label. Note that the label is independent of the rank.

+#' @param display Character. Default \code{"byRank"}. One of: \code{"byRank"}, \code{"byLabel"}. Specify whether the communities will be displayed based on their rank, where the largest community (with most vertices) would have a rank of 1, or by their label. Note that the label is independent of the rank.

 #' @param communities \code{NULL} or numeric vector. Default \code{NULL}. If set to \code{NULL}, the default, all communities enrichments that have been calculated before are plotted. If a numeric vector is specified: Depending on what was specified in the \code{display} parameter, this parameter indicates either the rank or the label of the communities to be plotted. i.e. for \code{communities = c(1,4)}, if \code{display = "byRank"} the results for the first and fourth largest communities are plotted. if \code{display = "byLabel"}, the results for the communities labeled \code{"1"}, and \code{"4"} are plotted. 

 #' @param nSignificant Numeric. Default 3. Threshold to filter out an ontology term with less than \code{nSignificant} overlapping genes. 

 #' @param nID Numeric. Default 10. For the reduced heatmap, number of top terms to select per community.