@@ -88,5 +88,5 @@ License: Artistic-2.0

 LazyData: false

 URL: https://grp-zaugg.embl-community.io/GRaNIE

 BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues

-RoxygenNote: 7.2.0

+RoxygenNote: 7.2.1

 Config/testthat/edition: 3

@@ -2,16 +2,15 @@

 ## Major changes

-- many Documentation and R help updates, Package Details Vignette is online

+- many Documentation and R help updates, the [Package Details Vignette](https://grp-zaugg.embl-community.io/GRaNIE/articles/GRaNIE_packageDetails.html#installation) is online

 - The workflow vignette is now improved: better figure resolution, figure aspect ratios are optimized, and a few other changes

 - the eGRN graph structure as built by `build_eGRN_graph()` in the `GRaNIE` object is now reset whenever the function `filterGRNAndConnectGenes()` is successfully executed to make sure that enrichment functions etc are not using an outdated graph structure. 

 - the landing page of the website has been extended and overhauled

-- removed some dependency packages and moved others into `Suggests` to lower the installation burden of the package. In addition, removed `topGO` from the `Depends` section (now in `Suggests`) and removed `tidyverse` altogether (before in `Depends`). Detailed explanations when and how the packages listed under `Suggests` are needed can now be found in the new [Package Details Vignette](https://grp-zaugg.embl-community.io/GRaNIE/articles/GRaNIE_packageDetails.html#installation).

-- new helper function `installSuggestedPackages()` to install all packages listed under `Suggests`

+- removed some dependency packages and moved others into `Suggests` to lower the installation burden of the package. In addition, removed `topGO` from the `Depends` section (now in `Suggests`) and removed `tidyverse` altogether (before in `Depends`). Detailed explanations when and how the packages listed under `Suggests` are needed can now be found in the new [Package Details Vignette](https://grp-zaugg.embl-community.io/GRaNIE/articles/GRaNIE_packageDetails.html#installation) and are clearly given to the user when executing the respective functions

 ## Minor changes

-- many small changes in the code, better argument checking, and preparing rigorous unit test inclusion

+- many small changes in the code, updated argument checking, and preparing rigorous unit test inclusion

 - internally renaming the (recently changed / renamed) gene type `lncRNA` from `biomaRt` to `lincRNA` to be compatible with older versions of `GRaNIE`

@@ -202,7 +202,7 @@ setMethod("show",

                 cat(" Communities (TF-gene):\n")

                 df = igraph::vertex.attributes(GRN@graph[["TF_gene"]]$graph)

                 if (!is.null(df) & "community" %in% names(df)) {

-                    communities = df %>% as.data.frame() %>% dplyr::count(community) %>% dplyr::arrange(dplyr::desc(.data$n))

+                    communities = df %>% as.data.frame() %>% dplyr::count(.data$community) %>% dplyr::arrange(dplyr::desc(.data$n))

                     cat("  Communities, sorted by size (n = Number of nodes): ", paste0(communities$community, " (n=", communities$n, collapse = "), "), ")\n", sep = "")

                 } else {

                     cat("  None found\n")

@@ -265,7 +265,7 @@ nPeaks <- function(GRN, filter = TRUE) {

   }

   if (filter) {

-    nPeaks = GRN@data$peaks$consensusPeaks %>% dplyr::filter(!isFiltered) %>% nrow()

+    nPeaks = GRN@data$peaks$consensusPeaks %>% dplyr::filter(!.data$isFiltered) %>% nrow()

   } else {

     nPeaks = GRN@data$peaks$consensusPeaks %>% nrow()

   }

@@ -301,7 +301,7 @@ nGenes <- function(GRN, filter = TRUE) {

   }

   if (filter) {

-    nGenes = GRN@data$RNA$counts_norm.l[["0"]] %>% dplyr::filter(!isFiltered) %>% nrow()

+    nGenes = GRN@data$RNA$counts_norm.l[["0"]] %>% dplyr::filter(!.data$isFiltered) %>% nrow()

   } else {

     nGenes = GRN@data$RNA$counts_norm.l[["0"]] %>% nrow()

   }

@@ -226,7 +226,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     if (nDuplicateRows > 0) {

       futile.logger::flog.warn(paste0(" Found ", nDuplicateRows, " duplicate rows in RNA-Seq data, consolidating them by summing them up."))

       counts_rna = counts_rna %>%

-        dplyr::group_by(ENSEMBL) %>%

+        dplyr::group_by(.data$ENSEMBL) %>%

         dplyr::summarise_if(is.numeric, sum) 

       # dplyr::summarise_if(is.numeric, sum, .groups = 'drop') # the .drop caused an error with dplyr 1.0.5

     }

@@ -243,8 +243,8 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     GRN@config$TF_peak_connectionTypes = "expression"

     # Make sure ENSEMBL is the first column

-    countsRNA.norm.df = dplyr::select(countsRNA.norm.df, ENSEMBL, tidyselect::everything())

-    countsPeaks.norm.df = dplyr::select(countsPeaks.norm.df, peakID, tidyselect::everything())

+    countsRNA.norm.df = dplyr::select(countsRNA.norm.df, .data$ENSEMBL, tidyselect::everything())

+    countsPeaks.norm.df = dplyr::select(countsPeaks.norm.df, .data$peakID, tidyselect::everything())

     samples_rna  = colnames(countsRNA.norm.df)

     samples_peaks =  colnames(countsPeaks.norm.df)

@@ -262,7 +262,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     if (!is.null(sampleMetadata)) {

       futile.logger::flog.info("Parsing provided metadata...")

-      GRN@data$metadata = sampleMetadata %>% dplyr::distinct() %>% tibble::set_tidy_names(syntactic = TRUE, quiet = TRUE)

+      GRN@data$metadata = sampleMetadata %>% dplyr::distinct() %>% tibble::tibble(.name_repair = "universal")

       # Force the first column to be the ID column

       if ("sampleID" %in% colnames(GRN@data$metadata)) {

@@ -288,12 +288,12 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     }

     GRN@data$metadata =  GRN@data$metadata %>%

-      dplyr::mutate(has_RNA = sampleID  %in% samples_rna,

-                    has_peaks = sampleID %in% samples_peaks,

-                    has_both = has_RNA & has_peaks

+      dplyr::mutate(has_RNA = .data$sampleID  %in% samples_rna,

+                    has_peaks = .data$sampleID %in% samples_peaks,

+                    has_both = .data$has_RNA & .data$has_peaks

       )

-    GRN@config$sharedSamples = dplyr::filter(GRN@data$metadata, has_both) %>% dplyr::pull(sampleID) %>% as.character()

+    GRN@config$sharedSamples = dplyr::filter(GRN@data$metadata, .data$has_both) %>% dplyr::pull(.data$sampleID) %>% as.character()

     counts_peaks = as.data.frame(counts_peaks)

     counts_rna = as.data.frame(counts_rna)

@@ -306,7 +306,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     if (isIntegerMatrix(counts_peaks[, GRN@config$sharedSamples])) {

       GRN@data$peaks$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_peaks[, GRN@config$sharedSamples], 

-                                                                  colData = dplyr::filter(GRN@data$metadata, has_both) %>% tibble::column_to_rownames("sampleID"),

+                                                                  colData = dplyr::filter(GRN@data$metadata, .data$has_both) %>% tibble::column_to_rownames("sampleID"),

                                                                   design = ~1)

     } else {

@@ -315,7 +315,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     if (isIntegerMatrix(counts_rna[, GRN@config$sharedSamples])) {

       GRN@data$RNA$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_rna[, GRN@config$sharedSamples], 

-                                                                colData = dplyr::filter(GRN@data$metadata, has_both) %>% tibble::column_to_rownames("sampleID"),

+                                                                colData = dplyr::filter(GRN@data$metadata, .data$has_both) %>% tibble::column_to_rownames("sampleID"),

                                                                 design = ~1)   

     } else {

       GRN@data$RNA$counts_orig = as.matrix(counts_rna[, GRN@config$sharedSamples])

@@ -442,15 +442,15 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     }

     geneAnnotation %>%

-        as_tibble() %>%

-        dplyr::filter(stringr::str_length(chromosome_name) <= 5) %>%

-        dplyr::mutate(chromosome_name = paste0("chr", chromosome_name)) %>%

-        dplyr::rename(gene.chr = chromosome_name, gene.start = start_position, gene.end = end_position, 

-                      gene.strand = strand, gene.ENSEMBL = ensembl_gene_id, gene.type = gene_biotype, gene.name = external_gene_name) %>%

+        tibble::as_tibble() %>%

+        dplyr::filter(stringr::str_length(.data$chromosome_name) <= 5) %>%

+        dplyr::mutate(chromosome_name = paste0("chr", .data$chromosome_name)) %>%

+        dplyr::rename(gene.chr = .data$chromosome_name, gene.start = .data$start_position, gene.end = .data$end_position, 

+                      gene.strand = .data$strand, gene.ENSEMBL = .data$ensembl_gene_id, gene.type = .data$gene_biotype, gene.name = .data$external_gene_name) %>%

         tidyr::replace_na(list(gene.type = "unknown")) %>%

         dplyr::mutate_if(is.character, as.factor) %>%

-        dplyr::mutate(gene.type = dplyr::recode_factor(gene.type, lncRNA = "lincRNA")) %>%  # there seems to be a name change from lincRNA -> lncRNA, lets change it here 

-        dplyr::mutate(gene.strand = factor(gene.strand, levels = c(1,-1), labels = c("+", "-")))

+        dplyr::mutate(gene.type = dplyr::recode_factor(.data$gene.type, lncRNA = "lincRNA")) %>%  # there seems to be a name change from lincRNA -> lncRNA, lets change it here 

+        dplyr::mutate(gene.strand = factor(.data$gene.strand, levels = c(1,-1), labels = c("+", "-")))

 }

@@ -473,7 +473,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

   if (!is.null(gene.types)) {

     if (! "all" %in% gene.types) {

-      genes.filt.df = dplyr::filter(genes.filt.df, gene.type %in% gene.types)

+      genes.filt.df = dplyr::filter(genes.filt.df, .data$gene.type %in% gene.types)

     }

   }

@@ -488,7 +488,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

   futile.logger::flog.info(paste0(" Calculate statistics for each peak (mean and CV)"))

-  countsPeaks.m = as.matrix(dplyr::select(countsPeaks.clean, -peakID))

+  countsPeaks.m = as.matrix(dplyr::select(countsPeaks.clean, -.data$peakID))

   rowMeans_peaks   = rowMeans(countsPeaks.m)

   rowMedians_peaks = matrixStats::rowMedians(countsPeaks.m)

@@ -509,7 +509,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     futile.logger::flog.info(paste0(" Retrieve peak annotation using ChipSeeker. This may take a while"))

     genomeAssembly = GRN@config$parameters$genomeAssembly

-    consensusPeaks     = GRN@data$peaks$consensusPeaks %>% dplyr::filter(!isFiltered)

+    consensusPeaks     = GRN@data$peaks$consensusPeaks %>% dplyr::filter(!.data$isFiltered)

     consensusPeaks.gr  = .constructGRanges(consensusPeaks, seqlengths = .getChrLengths(genomeAssembly), genomeAssembly)

     # Add ChIPSeeker anotation

@@ -538,22 +538,22 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     GRN@annotation$consensusPeaks = dplyr::left_join(GRN@annotation$consensusPeaks, 

                                                      peaks.annotated.df  %>% 

-                                                       dplyr::select(peakID, annotation, tidyselect::starts_with("gene"), -geneId, distanceToTSS, ENSEMBL, SYMBOL, GENENAME) %>%

-                                                       dplyr::mutate(annotation  = as.factor(annotation), 

-                                                                     ENSEMBL = as.factor(ENSEMBL), 

-                                                                     GENENAME = as.factor(GENENAME),

-                                                                     SYMBOL = as.factor(SYMBOL)),

+                                                       dplyr::select(.data$peakID, .data$annotation, tidyselect::starts_with("gene"), -.data$geneId, .data$distanceToTSS, .data$ENSEMBL, .data$SYMBOL, .data$GENENAME) %>%

+                                                       dplyr::mutate(annotation  = as.factor(.data$annotation), 

+                                                                     ENSEMBL = as.factor(.data$ENSEMBL), 

+                                                                     GENENAME = as.factor(.data$GENENAME),

+                                                                     SYMBOL = as.factor(.data$SYMBOL)),

                                                      by = c("peak.ID" = "peakID")) %>%

-      dplyr::rename(peak.gene.chr = geneChr,

-                    peak.gene.start = geneStart, 

-                    peak.gene.end = geneEnd, 

-                    peak.gene.length = geneLength, 

-                    peak.gene.strand = geneStrand, 

-                    peak.gene.name = GENENAME,

-                    peak.gene.distanceToTSS = distanceToTSS,

-                    peak.gene.ENSEMBL = ENSEMBL,

-                    peak.gene.symbol = SYMBOL,

-                    peak.annotation = annotation

+      dplyr::rename(peak.gene.chr = .data$geneChr,

+                    peak.gene.start = .data$geneStart, 

+                    peak.gene.end = .data$geneEnd, 

+                    peak.gene.length = .data$geneLength, 

+                    peak.gene.strand = .data$geneStrand, 

+                    peak.gene.name = .data$GENENAME,

+                    peak.gene.distanceToTSS = .data$distanceToTSS,

+                    peak.gene.ENSEMBL = .data$ENSEMBL,

+                    peak.gene.symbol = .data$SYMBOL,

+                    peak.annotation = .data$annotation

       )

@@ -575,7 +575,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

   countsRNA.clean  = getCounts(GRN, type = "rna", norm = TRUE, permuted = FALSE)

-  countsRNA.m = as.matrix(dplyr::select(countsRNA.clean, -ENSEMBL))

+  countsRNA.m = as.matrix(dplyr::select(countsRNA.clean, -.data$ENSEMBL))

   rowMeans_rna = rowMeans(countsRNA.m)

   rowMedians_rna = matrixStats::rowMedians(countsRNA.m)

@@ -588,7 +588,7 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

                                 gene.median = rowMedians_rna, 

                                 gene.CV = CV_rna) %>%

     dplyr::full_join(genomeAnnotation.df, by = c("gene.ENSEMBL")) %>%

-    dplyr::mutate(gene.type = forcats::fct_explicit_na(gene.type, na_level = "unknown/missing"))

+    dplyr::mutate(gene.type = forcats::fct_explicit_na(.data$gene.type, na_level = "unknown/missing"))

   GRN@annotation$genes = metadata_rna

@@ -626,24 +626,24 @@ addData <- function(GRN, counts_peaks, normalization_peaks = "DESeq_sizeFactor",

     tibble::as_tibble() %>%

     dplyr::mutate(length = GenomicRanges::width(query),

                   peak.ID = query$peakID,

-                  GC_class = cut(`G|C`, breaks = seq(0,1,0.1), include.lowest = TRUE, ordered_result = TRUE))

+                  GC_class = cut(.data$`G|C`, breaks = seq(0,1,0.1), include.lowest = TRUE, ordered_result = TRUE))

   GC_classes.df = GC_content.df %>%

-    dplyr::group_by(GC_class) %>%

+    dplyr::group_by(.data$GC_class) %>%

     dplyr::summarise(n= dplyr::n(), peak_width_mean = mean(length), peak_width_sd = sd(length)) %>%

     dplyr::ungroup() %>% 

-    tidyr::complete(GC_class, fill = list(n = 0)) %>%

+    tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%

     dplyr::mutate(n_rel = .data$n / length(query))

   # TODO: Put where

-  #ggplot(GC_content.df, aes(GC.class)) + geom_histogram(stat = "count") + theme_bw()

+  #ggplot2::ggplot(GC_content.df, ggplot2::aes(GC.class)) + geom_histogram(stat = "count") + ggplot2::theme_bw()

-  #ggplot(GC_classes.df , aes(GC.class, n_rel)) + geom_bar(stat = "identity") + theme_bw()

+  #ggplot2::ggplot(GC_classes.df , ggplot2::aes(GC.class, n_rel)) + geom_bar(stat = "identity") + ggplot2::theme_bw()

   GRN@annotation$consensusPeaks = dplyr::left_join(GRN@annotation$consensusPeaks, GC_content.df, by = "peak.ID") %>%

-    dplyr::rename( peak.GC.perc    = `G|C`,

-                   peak.width      = length,

-                   peak.GC.class   = GC_class)

+    dplyr::rename( peak.GC.perc    = .data$`G|C`,

+                   peak.width      = .data$length,

+                   peak.GC.class   = .data$GC_class)

   GRN@stats$peaks = list()

   GRN@stats$peaks$GC = GC_classes.df

@@ -749,7 +749,7 @@ filterData <- function (GRN,

     futile.logger::flog.info(paste0(" Number of rows in total: ", nrow(GRN@data$RNA$counts_norm.l[["0"]])))

     for (permutationCur in c(0)) {

       permIndex = as.character(permutationCur)

-      rowMeans = rowMeans(dplyr::select(GRN@data$RNA$counts_norm.l[[permIndex]], -ENSEMBL))

+      rowMeans = rowMeans(dplyr::select(GRN@data$RNA$counts_norm.l[[permIndex]], -.data$ENSEMBL))

       GRN@data$RNA$counts_norm.l[[permIndex]]$isFiltered = rowMeans < minNormalizedMeanRNA 

     }

     nRowsFlagged = length(which(GRN@data$RNA$counts_norm.l[["0"]]$isFiltered))

@@ -816,12 +816,12 @@ filterData <- function (GRN,

   if (is.null(maxCV)) {

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

-    peaksFiltered = dplyr::filter(GRN@annotation$consensusPeaks, peak.CV >= minCV)

+    peaksFiltered = dplyr::filter(GRN@annotation$consensusPeaks, .data$peak.CV >= minCV)

   } else {

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

-    peaksFiltered = dplyr::filter(GRN@annotation$consensusPeaks, peak.CV >= minCV, peak.CV <= maxCV)

+    peaksFiltered = dplyr::filter(GRN@annotation$consensusPeaks, .data$peak.CV >= minCV, .data$peak.CV <= maxCV)

   }

   futile.logger::flog.info(paste0(" Number of peaks after filtering : ", nrow(peaksFiltered)))

@@ -865,8 +865,8 @@ filterData <- function (GRN,

   peaksFiltered = dplyr::filter(GRN@annotation$consensusPeaks, 

-                                peak.CV >= minCV, peak.CV <= maxCV, 

-                                peak.mean >= minMean, peak.mean <= maxMean)

+                                .data$peak.CV >= minCV, .data$peak.CV <= maxCV, 

+                                .data$peak.mean >= minMean, .data$peak.mean <= maxMean)

   futile.logger::flog.info(paste0(" Number of peaks after filtering : ", nrow(peaksFiltered)))

@@ -912,8 +912,8 @@ filterData <- function (GRN,

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

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

-                                gene.mean >= minMean, gene.mean <= maxMean)

+                                .data$gene.CV >= minCV, .data$gene.CV <= maxCV, 

+                                .data$gene.mean >= minMean, .data$gene.mean <= maxMean)

   futile.logger::flog.info(paste0(" Number of genes after filtering : ", nrow(genesFiltered)))

@@ -962,7 +962,7 @@ addTFBS <- function(GRN, motifFolder, TFs = "all", nTFMax = NULL, filesTFBSPatte

     GRN@data$TFs$translationTable = GRN@data$TFs$translationTable %>%

       dplyr::select(c("HOCOID", "ENSEMBL")) %>%

-      dplyr::mutate(TF.name = HOCOID, TF.ENSEMBL = ENSEMBL) 

+      dplyr::mutate(TF.name = .data$HOCOID, TF.ENSEMBL = .data$ENSEMBL) 

     # TODO: Change here and make it more logical what to put where

     GRN@config$allTF = GRN@data$TFs$translationTable$TF.name

@@ -1013,14 +1013,16 @@ addTFBS <- function(GRN, motifFolder, TFs = "all", nTFMax = NULL, filesTFBSPatte

   file_input_HOCOMOCO = paste0(folder_input_TFBS, .Platform$file.sep, "translationTable.csv")

   HOCOMOCO_mapping.df = .readHOCOMOCOTable(file_input_HOCOMOCO, delim = " ")

-  TF_notExpressed = sort(dplyr::filter(HOCOMOCO_mapping.df, ! ENSEMBL %in% countsRNA$ENSEMBL, HOCOID %in% TFsWithTFBSPredictions) %>% dplyr::pull(HOCOID))

+  TF_notExpressed = sort(dplyr::filter(HOCOMOCO_mapping.df, ! .data$ENSEMBL %in% countsRNA$ENSEMBL, .data$HOCOID %in% TFsWithTFBSPredictions) %>% dplyr::pull(.data$HOCOID))

   if (length(TF_notExpressed) > 0) {

     futile.logger::flog.info(paste0("Filtering the following ", length(TF_notExpressed), " TFs as they are not present in the RNA-Seq data: ", paste0(TF_notExpressed, collapse = ",")))

   }

-  allTF = sort(dplyr::filter(HOCOMOCO_mapping.df, ENSEMBL %in% countsRNA$ENSEMBL, HOCOID %in% TFsWithTFBSPredictions) %>% dplyr::pull(HOCOID))

+  allTF = sort(dplyr::filter(HOCOMOCO_mapping.df, 

+                             .data$ENSEMBL %in% countsRNA$ENSEMBL, 

+                             .data$HOCOID %in% TFsWithTFBSPredictions) %>% dplyr::pull(.data$HOCOID))

   nTF = length(allTF)

   if (nTF == 0) {

@@ -1041,7 +1043,7 @@ addTFBS <- function(GRN, motifFolder, TFs = "all", nTFMax = NULL, filesTFBSPatte

   nTF = length(allTF)

   futile.logger::flog.info(paste0("Running the pipeline for ", nTF, " TF in total."))

-  HOCOMOCO_mapping.df.exp = dplyr::filter(HOCOMOCO_mapping.df, HOCOID %in% allTF)

+  HOCOMOCO_mapping.df.exp = dplyr::filter(HOCOMOCO_mapping.df, .data$HOCOID %in% allTF)

   if (nrow(HOCOMOCO_mapping.df.exp) == 0) {

     message = paste0("Number of rows of HOCOMOCO_mapping.df.exp is 0. Something is wrong with the mapping table or the filtering")

     .checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)

@@ -1125,13 +1127,13 @@ overlapPeaksAndTFBS <- function(GRN, nCores = 2, forceRerun = FALSE) {

     }

     # new 

-    filteredPeaks = dplyr::filter(GRN@data$peaks$counts_norm, isFiltered) %>% dplyr::pull(peakID)

+    filteredPeaks = dplyr::filter(GRN@data$peaks$counts_norm, .data$isFiltered) %>% dplyr::pull(.data$peakID)

     # Collect binary 0/1 binding matrix from all TF and concatenate

     GRN@data$TFs$TF_peak_overlap = TFBS_bindingMatrix.df %>%

       dplyr::mutate(peakID = GRN@data$peaks$consensusPeaks$peakID,

-                    isFiltered = peakID %in% filteredPeaks) %>%

+                    isFiltered = .data$peakID %in% filteredPeaks) %>%

       dplyr::mutate_if(is.logical, as.numeric) %>%

-      dplyr::select(tidyselect::all_of(sort(GRN@config$allTF)), isFiltered)

+      dplyr::select(tidyselect::all_of(sort(GRN@config$allTF)), .data$isFiltered)

     GRN@data$TFs$TF_peak_overlap = .asSparseMatrix(as.matrix(GRN@data$TFs$TF_peak_overlap), 

                                                    convertNA_to_zero = FALSE, 

@@ -1186,13 +1188,13 @@ overlapPeaksAndTFBS <- function(GRN, nCores = 2, forceRerun = FALSE) {

   query_overlap_df$peak_end    = end(query.gr)  [query_row_ids]

   final.df = cbind.data.frame(query_overlap_df, subject_overlap_df) %>%

-    dplyr::select(-score, -annotation) %>%

-    dplyr::mutate(tfbsID = paste0(tfbs_chr, ":", tfbs_start, "-", tfbs_end),

-                  coordCentTfbs = round((tfbs_start + tfbs_end)/2, 0),

-                  coordSummit   = round((peak_start + peak_end)/2, 0),

-                  distance = abs(coordSummit - coordCentTfbs))  %>%

-    dplyr::group_by(peakID) %>%

-    dplyr::slice(which.min(distance)) %>%

+    dplyr::select(-.data$score, -.data$annotation) %>%

+    dplyr::mutate(tfbsID = paste0(.data$tfbs_chr, ":", .data$tfbs_start, "-", .data$tfbs_end),

+                  coordCentTfbs = round((.data$tfbs_start + .data$tfbs_end)/2, 0),

+                  coordSummit   = round((.data$peak_start + .data$peak_end)/2, 0),

+                  distance = abs(.data$coordSummit - .data$coordCentTfbs))  %>%

+    dplyr::group_by(.data$peakID) %>%

+    dplyr::slice(which.min(.data$distance)) %>%

     #arrange(distance, .by_group = TRUE) %>%

     # top_n(n = 2, dplyr::desc(distance)) %>%

     dplyr::ungroup()

@@ -1236,10 +1238,10 @@ overlapPeaksAndTFBS <- function(GRN, nCores = 2, forceRerun = FALSE) {

   # Filter to only the TFs

   # In addition, the no of TF because multiple TFs can map to the same gene/ ENSEMBL ID

   # Also filter 0 count genes because they otherwise cause errors downstream

-  rowSums = rowSums(dplyr::select(matrix1, -ENSEMBL))

+  rowSums = rowSums(dplyr::select(matrix1, -.data$ENSEMBL))

   # Keep only Ensembl IDs from TFs we have data from

-  matrix1.norm.TFs.df = dplyr::filter(matrix1, ENSEMBL %in% HOCOMOCO_mapping$ENSEMBL, rowSums != 0)

+  matrix1.norm.TFs.df = dplyr::filter(matrix1, .data$ENSEMBL %in% HOCOMOCO_mapping$ENSEMBL, rowSums != 0)

   diff = nrow(matrix1) - nrow(matrix1.norm.TFs.df)

   if (diff > 0) {

@@ -1252,7 +1254,7 @@ overlapPeaksAndTFBS <- function(GRN, nCores = 2, forceRerun = FALSE) {

     .checkAndLogWarningsAndErrors(NULL, message, isWarning = FALSE)

   }

-  HOCOMOCO_mapping.exp = dplyr::filter(HOCOMOCO_mapping, ENSEMBL %in% matrix1.norm.TFs.df$ENSEMBL)

+  HOCOMOCO_mapping.exp = dplyr::filter(HOCOMOCO_mapping, .data$ENSEMBL %in% matrix1.norm.TFs.df$ENSEMBL)

   futile.logger::flog.info(paste0(whitespacePrefix, "Correlate TF/gene data for ", nrow(matrix1.norm.TFs.df), " unique Ensembl IDs (TFs) and peak counts for ", nrow(matrix_peaks), " peaks."))

   futile.logger::flog.info(paste0(whitespacePrefix, "Note: For subsequent steps, the same gene may be associated with multiple TF, depending on the translation table."))

   # Correlate TF gene counts with peak counts 

@@ -1263,7 +1265,7 @@ overlapPeaksAndTFBS <- function(GRN, nCores = 2, forceRerun = FALSE) {

   # counts for peaks may be 0 throughout, then a warning is thrown

   #If the sd is zero, a warning is issued. We suppress it here to not confuse users as this is being dealt with later by ignoring the NA entries

-  cor.m = suppressWarnings(t(cor(t(dplyr::select(matrix1.norm.TFs.df, -ENSEMBL)), t(dplyr::select(matrix_peaks, -peakID)), method = corMethod)))

+  cor.m = suppressWarnings(t(cor(t(dplyr::select(matrix1.norm.TFs.df, -.data$ENSEMBL)), t(dplyr::select(matrix_peaks, -.data$peakID)), method = corMethod)))

   colnames(cor.m) = matrix1.norm.TFs.df$ENSEMBL

   rownames(cor.m) = matrix_peaks$peakID

@@ -1384,7 +1386,7 @@ addData_TFActivity <- function(GRN, normalization = "cyclicLoess", name = "TF_ac

       tibble::rownames_to_column("TF.name") %>%

       tibble::as_tibble() %>%

       dplyr::left_join(GRN@data$TFs$translationTable, by = "TF.name") %>%

-      dplyr::select(ENSEMBL, TF.name, tidyselect::all_of(GRN@config$sharedSamples))

+      dplyr::select(.data$ENSEMBL, .data$TF.name, tidyselect::all_of(GRN@config$sharedSamples))

     # Update available connection types

     GRN@config$TF_peak_connectionTypes = unique(c(GRN@config$TF_peak_connectionTypes, name))

@@ -1542,7 +1544,7 @@ importTFData <- function(GRN, data, name, idColumn = "ENSEMBL", nameColumn = "TF

     # data = dplyr::select(data, -tidyselect::one_of(nameColumn))

     # Only TF.names are unique

-    countsNorm = .normalizeNewDataWrapper(data %>% dplyr::select(-ENSEMBL), normalization = normalization, idColumn = "TF.name")

+    countsNorm = .normalizeNewDataWrapper(data %>% dplyr::select(-.data$ENSEMBL), normalization = normalization, idColumn = "TF.name")

     # Check overlap of ENSEMBL IDs

     countsNorm$ENSEMBL = data$ENSEMBL

@@ -1578,7 +1580,7 @@ importTFData <- function(GRN, data, name, idColumn = "ENSEMBL", nameColumn = "TF

     # Therefore, use TF names as row names, same as with the TF Activity matrix

     GRN@data$TFs[[name]] = countsNorm.subset %>%

-      dplyr::select(ENSEMBL, TF.name, tidyselect::all_of(GRN@config$sharedSamples)) %>%

+      dplyr::select(.data$ENSEMBL, .data$TF.name, tidyselect::all_of(GRN@config$sharedSamples)) %>%

       tibble::as_tibble()

     # Update available connection types

@@ -1636,7 +1638,7 @@ AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05

   outputFolder = .checkOutputFolder(GRN, outputFolder)

   GRN@data$TFs$classification$TF.translation.orig = GRN@data$TFs$translationTable %>%

-    dplyr::mutate(TF.name = HOCOID)

+    dplyr::mutate(TF.name = .data$HOCOID)

   if (is.null(GRN@data$TFs$TF_peak_overlap)) {

     message = paste0("Could not find peak - TF matrix. Run the function overlapPeaksAndTFBS first / again")

@@ -1683,7 +1685,7 @@ AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05

           # TF activity data

           counts1 = GRN@data$TFs[[connectionTypeCur]] %>% 

-            dplyr::select(-TF.name)

+            dplyr::select(-.data$TF.name)

         } 

@@ -1716,7 +1718,7 @@ AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05

         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification = 

           .finalizeClassificationAndAppend(

-            output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = TF.name), 

+            output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = .data$TF.name), 

             median.cor.tfs = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground, 

             act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l, 

             par.l = GRN@config$parameters, 

@@ -1764,7 +1766,7 @@ AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05

           TF_peak_cor = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor

           peak_TF_overlapCur.df = .filterSortAndShuffle_peakTF_overlapTable(GRN, permutationCur, TF_peak_cor)

           .plot_heatmapAR(TF.peakMatrix.df = peak_TF_overlapCur.df, 

-                          HOCOMOCO_mapping.df.exp = GRN@data$TFs$translationTable %>% dplyr::mutate(TF = TF.name), 

+                          HOCOMOCO_mapping.df.exp = GRN@data$TFs$translationTable %>% dplyr::mutate(TF = .data$TF.name), 

                           sort.cor.m = TF_peak_cor, 

                           par.l = GRN@config$parameters, 

                           corMethod = corMethod,

@@ -1968,7 +1970,7 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

       # Keep only Ensembl ID here

       counts_connectionTypeCur = GRN@data$TFs[[connectionTypeCur]] %>% 

-        dplyr::select(-TF.name)

+        dplyr::select(-.data$TF.name)

     } 

@@ -2040,18 +2042,18 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

         # Get GC info from those peaks from the foreground

         GC_classes_foreground.df = peaksForeground %>%

-          dplyr::group_by(GC_class) %>%

-          dplyr::summarise(n= dplyr::n(), peak_width_mean = mean(peak_width), peak_width_sd = sd(peak_width)) %>%

+          dplyr::group_by(.data$GC_class) %>%

+          dplyr::summarise(n= dplyr::n(), peak_width_mean = mean(.data$peak_width), peak_width_sd = sd(.data$peak_width)) %>%

           dplyr::ungroup() %>% 

-          tidyr::complete(GC_class, fill = list(n = 0)) %>%

+          tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%

           dplyr::mutate(n_rel = .data$n / nPeaksForeground, type = "foreground") %>%

-          dplyr::arrange(dplyr::desc(n_rel))

+          dplyr::arrange(dplyr::desc(.data$n_rel))

         GC_classes_background.df = peaksBackground %>%

-          dplyr::group_by(GC_class) %>%

-          dplyr::summarise(n= dplyr::n(), peak_width_mean = mean(peak_width), peak_width_sd = sd(peak_width)) %>%

+          dplyr::group_by(.data$GC_class) %>%

+          dplyr::summarise(n= dplyr::n(), peak_width_mean = mean(.data$peak_width), peak_width_sd = sd(.data$peak_width)) %>%

           dplyr::ungroup() %>% 

-          tidyr::complete(GC_class, fill = list(n = 0)) %>%

+          tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%

           dplyr::mutate(n_rel = .data$n / nPeaksBackground, type = "background_orig")

@@ -2062,9 +2064,6 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

           minPerc = percBackground_size

         } else {

-          # Find a matching background of maximal size

-          minPerc= min(GC_class.all$maxSizeBackground) / nPeaksBackground

-          

           threshold_percentage = 0.05

           minPerc = .findMaxBackgroundSize(GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage =  threshold_percentage)

@@ -2099,9 +2098,9 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

         # Add sel. minPerc to table and calculate required frequencies

         GC_classes_all.df = dplyr::full_join(GC_classes_foreground.df, GC_classes_background.df, suffix = c(".fg",".bg"), by = "GC_class") %>%

-          dplyr::mutate(maxSizeBackground = n.bg / n_rel.fg,

-                        n.bg.needed = floor(n_rel.fg * targetNoPeaks), 

-                        n.bg.needed.perc = n.bg / n.bg.needed) 

+          dplyr::mutate(maxSizeBackground = .data$n.bg / .data$n_rel.fg,

+                        n.bg.needed = floor(.data$n_rel.fg * targetNoPeaks), 

+                        n.bg.needed.perc = .data$n.bg / .data$n.bg.needed) 

         #futile.logger::flog.info(paste0( " GC-adjustment: Randomly select a total of ", round(targetNoPeaks,0), 

@@ -2127,9 +2126,9 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

           }

           if (GC_classes_all.df$n.bg.needed[i] > nrow(peaksBackgroundGCCur)) {

-            peakIDsSel = c(peakIDsSel, peaksBackgroundGCCur %>% dplyr::sample_n(nPeaksCur, replace = percBackground_resample) %>% dplyr::pull(peakID))  

+            peakIDsSel = c(peakIDsSel, peaksBackgroundGCCur %>% dplyr::sample_n(nPeaksCur, replace = percBackground_resample) %>% dplyr::pull(.data$peakID))  

           } else {

-            peakIDsSel = c(peakIDsSel, peaksBackgroundGCCur %>% dplyr::sample_n(nPeaksCur, replace = FALSE) %>% dplyr::pull(peakID))

+            peakIDsSel = c(peakIDsSel, peaksBackgroundGCCur %>% dplyr::sample_n(nPeaksCur, replace = FALSE) %>% dplyr::pull(.data$peakID))

           }

           # Take a sample from the background, and record the row numbers

@@ -2246,7 +2245,7 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

           # 

           TF_peak.fdr_direction     = directionCur,

           TF_peak.r_bin      = 

-            as.character(cut(TF_peak.r_bin2, breaks = stepsCur, right = rightOpen, include.lowest = TRUE))

+            as.character(cut(.data$TF_peak.r_bin2, breaks = stepsCur, right = rightOpen, include.lowest = TRUE))

         ) 

         # Derive connection summaries for all TF for both directions

@@ -2262,7 +2261,7 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

         }

         connectionStats_all.l[[indexStr]] =  cor.peak.tf %>%

-          dplyr::group_by(TF_peak.r_bin) %>%

+          dplyr::group_by(.data$TF_peak.r_bin) %>%

           dplyr::summarise(n = dplyr::n()) %>%

           dplyr::ungroup() %>%

           dplyr::right_join(fdr.curve, by = "TF_peak.r_bin") %>%

@@ -2270,7 +2269,7 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

                         TF.name = as.factor(TFCur), 

                         TF_peak.connectionType = factor(connectionTypeCur, levels = connectionTypes),

                         TF_peak.fdr_direction  = factor(directionCur, levels = c("pos", "neg")),

-                        TF_peak.r_bin = factor(TF_peak.r_bin, levels = levelsBins),

+                        TF_peak.r_bin = factor(.data$TF_peak.r_bin, levels = levelsBins),

                         # Collect extra information, currently however a bit repetitively stored

                         nForeground              = nPeaksForeground,

@@ -2278,15 +2277,15 @@ addConnections_TF_peak <- function (GRN, plotDiagnosticPlots = TRUE, plotDetails

                         nBackground_orig         = nPeaksBackground,

                         percBackgroundUsed       = minPerc,

                         background_match_success = background_match_success) %>%

-          dplyr::select(TF.name, TF_peak.r_bin, 

-                        .data$n, tpvalue, fpvalue, fpvalue_norm, 

-                        TF_peak.fdr, 

-                        TF_peak.fdr_orig, TF_peak.fdr_direction, 

-                        TF_peak.connectionType,

+          dplyr::select(.data$TF.name, .data$TF_peak.r_bin, 

+                        .data$n, .data$tpvalue, .data$fpvalue, .data$fpvalue_norm, 

+                        .data$TF_peak.fdr, 

+                        .data$TF_peak.fdr_orig, .data$TF_peak.fdr_direction, 

+                        .data$TF_peak.connectionType,

                         tidyselect::contains("ground")) %>%

-          dplyr::rename(n_tp = tpvalue, n_fp = fpvalue, n_fp_norm = fpvalue_norm) %>%

+          dplyr::rename(n_tp = .data$tpvalue, n_fp = .data$fpvalue, n_fp_norm = .data$fpvalue_norm) %>%

           dplyr::distinct() %>%

-          dplyr::arrange(TF_peak.r_bin)

+          dplyr::arrange(.data$TF_peak.r_bin)

         # Collect data for additional QC plots before they are filtered

@@ -2498,7 +2497,7 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

 }

-.calculatePeakGeneOverlaps <- function(GRN, allPeaks, peak_TAD_mapping = NULL, par.l, neighborhoodSize = 250000, genomeAssembly, 

+.calculatePeakGeneOverlaps <- function(GRN, allPeaks, peak_TAD_mapping = NULL, neighborhoodSize = 250000, genomeAssembly, 

                                        gene.types, overlapTypeGene = "TSS", removeEnsemblNA = TRUE) {

   start = Sys.time()

@@ -2514,10 +2513,10 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

     futile.logger::flog.info(paste0(" For peaks overlapping multiple TADs, use the union of all to define the neighborhood"))

     peak_TAD_mapping = peak_TAD_mapping %>%

-      dplyr::group_by(peakID) %>%

-      dplyr::mutate(tadStart2 = min(tadStart), # If a peak overlaps multiple TADs, merge them

-                    tadEnd2   = max(tadEnd),  # If a peak overlaps multiple TADs, merge them

-                    tad.ID_all = paste0(tad.ID, collapse = "|")) %>%

+      dplyr::group_by(.data$peakID) %>%

+      dplyr::mutate(tadStart2 = min(.data$tadStart), # If a peak overlaps multiple TADs, merge them

+                    tadEnd2   = max(.data$tadEnd),  # If a peak overlaps multiple TADs, merge them

+                    tad.ID_all = paste0(.data$tad.ID, collapse = "|")) %>%

       dplyr::slice(1) %>%

       dplyr::ungroup()

@@ -2722,7 +2721,7 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

     nPeaksWithOutTAD = length(which(is.na(peak.TADs.df$tad.ID)))

     futile.logger::flog.info(paste0(" Out of the ", nPeaks, " peaks, ", nPeaksWithOutTAD, " peaks are not within a TAD domain. These will be ignored for subsequent overlaps"))   

-    nPeaksWithMultipleTADs = peak.TADs.df %>% dplyr::group_by(peakID) %>% dplyr::summarize(n = dplyr::n()) %>% dplyr::filter(.data$n > 1) %>% nrow()

+    nPeaksWithMultipleTADs = peak.TADs.df %>% dplyr::group_by(.data$peakID) %>% dplyr::summarize(n = dplyr::n()) %>% dplyr::filter(.data$n > 1) %>% nrow()

     if (nPeaksWithMultipleTADs > 0) {

       futile.logger::flog.info(paste0(" Out of the ", nPeaks, " peaks, ", nPeaksWithMultipleTADs, " overlap with more than one TAD. This usually means they are crossing TAD borders.")) 

@@ -2736,12 +2735,14 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

   # if a peak overlaps with a gene, should the same gene be reported as the connection?

   # OVERLAP OF PEAKS AND EXTENDED GENES

-  overlaps.sub.df = .calculatePeakGeneOverlaps(GRN, allPeaks = consensusPeaks, peak.TADs.df, neighborhoodSize = neighborhoodSize, 

-                                               genomeAssembly = genomeAssembly, gene.types = gene.types, overlapTypeGene = overlapTypeGene) 

+  overlaps.sub.df = .calculatePeakGeneOverlaps(GRN, allPeaks = consensusPeaks, peak.TADs.df, 

+                                               neighborhoodSize = neighborhoodSize, 

+                                               genomeAssembly = genomeAssembly, 

+                                               gene.types = gene.types, overlapTypeGene = overlapTypeGene) 

   overlaps.sub.filt.df = overlaps.sub.df %>%

-    dplyr::mutate(gene.ENSEMBL = gsub("\\..+", "", gene.ENSEMBL, perl = TRUE)) # Clean ENSEMBL IDs

+    dplyr::mutate(gene.ENSEMBL = gsub("\\..+", "", .data$gene.ENSEMBL, perl = TRUE)) # Clean ENSEMBL IDs

   # Only now we shuffle to make sure the background of possible connections is the same as in the foreground, as opposed to completely random

   # which would also include peak-gene connections that are not in the foreground at all

@@ -2762,8 +2763,8 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

   permIndex = as.character(perm)

-  countsPeaks.clean = dplyr::select(getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE), -peakID)

-  countsRNA.clean  = dplyr::select(getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(perm)), -ENSEMBL)

+  countsPeaks.clean = dplyr::select(getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE), -.data$peakID)

+  countsRNA.clean  = dplyr::select(getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(perm)), -.data$ENSEMBL)

   # Cleverly construct the count matrices so we do the correlations in one go

   map_peaks = match(overlaps.sub.filt.df$peak.ID,  getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE)$peakID)

@@ -2812,29 +2813,29 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

   res.df = suppressMessages(tibble::as_tibble(res.m) %>%

                               dplyr::mutate(peak.ID = getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE)$peakID[map_peaks],

                                             gene.ENSEMBL = getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(perm))$ENSEMBL[map_rna]) %>%

-                              dplyr::filter(!is.na(gene.ENSEMBL)) %>%  # For some peak-gene combinations, no RNA-Seq data was available, these NAs are filtered

+                              dplyr::filter(!is.na(.data$gene.ENSEMBL)) %>%  # For some peak-gene combinations, no RNA-Seq data was available, these NAs are filtered

                               # Add gene annotation and distance

                               dplyr::left_join(overlaps.sub.filt.df, by = c("gene.ENSEMBL", "peak.ID")) %>%

                               # Integrate TAD IDs also

-                              dplyr::left_join(dplyr::select(peak.TADs.df, peak.ID, tad.ID), by = c("peak.ID")) %>%

+                              dplyr::left_join(dplyr::select(peak.TADs.df, .data$peak.ID, .data$tad.ID), by = c("peak.ID")) %>%

                               dplyr::select(tidyselect::all_of(selectColumns))) %>%

-    dplyr::mutate(peak.ID = as.factor(peak.ID),

-                  gene.ENSEMBL = as.factor(gene.ENSEMBL), 

-                  tad.ID = as.factor(tad.ID)) %>%

-    dplyr::rename(peak_gene.r = r, 

-                  peak_gene.p_raw = p.raw)

+    dplyr::mutate(peak.ID = as.factor(.data$peak.ID),

+                  gene.ENSEMBL = as.factor(.data$gene.ENSEMBL), 

+                  tad.ID = as.factor(.data$tad.ID)) %>%

+    dplyr::rename(peak_gene.r = .data$r, 

+                  peak_gene.p_raw = .data$p.raw)

   if (addRobustRegression) {

     res.df = dplyr::rename(res.df, 

-                           peak_gene.p_raw.robust = p_raw.robust, 

-                           peak_gene.r_robust = r_robust,

-                           peak_gene.bias_M_p.raw = bias_M_p.raw,

-                           peak_gene.bias_LS_p.raw = bias_LS_p.raw)

+                           peak_gene.p_raw.robust = .data$p_raw.robust, 

+                           peak_gene.r_robust = .data$r_robust,

+                           peak_gene.bias_M_p.raw = .data$bias_M_p.raw,

+                           peak_gene.bias_LS_p.raw = .data$bias_LS_p.raw)

   }

   if (is.null(TADs)) {

-    res.df = dplyr::select(res.df, -tad.ID)

+    res.df = dplyr::select(res.df, -.data$tad.ID)

   }

   futile.logger::flog.info(paste0(" Finished. Final number of rows: ", nrow(res.df)))

@@ -2905,17 +2906,17 @@ addConnections_peak_gene <- function(GRN, overlapTypeGene = "TSS", corMethod = "

       dataCur.df = tibble::tibble(data_Peaks =unlist(counts1 [map1[i],]), 

                                   data_RNA = unlist(counts2 [map2[i],]) )

-      # TODO: perm not known here

+      # TODO: perm and GRN not known here

       stop("Not implemeneted yet")

-      g = ggplot(dataCur.df, aes(data1, data2)) + geom_point() + geom_smooth(method ="lm") + 

-        ggtitle(paste0(getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE)$peakID[map1[i]], 

-                       ", ", 

-                       getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(perm))$ENSEMBL[map2 [i]], 

-                       ", p = ", round(res$p.value, 3))) + 

-        theme_bw()

-      plot(g)

-      

-      nPlotted = nPlotted + 1

+      # g = ggplot2::ggplot(dataCur.df, ggplot2::aes(data1, data2)) + ggplot2::geom_point() + ggplot2::geom_smooth(method ="lm") + 

+      #   ggplot2::ggtitle(paste0(getCounts(GRN, type = "peaks", norm = TRUE, permuted = FALSE)$peakID[map1[i]], 

+      #                  ", ", 

+      #                  getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(perm))$ENSEMBL[map2 [i]], 

+      #                  ", p = ", round(res$p.value, 3))) + 

+      #   ggplot2::theme_bw()

+      # plot(g)

+      # 

+      # nPlotted = nPlotted + 1

     }

     if (debugMode_nPlots > 0 & nPlotted >=  debugMode_nPlots){

@@ -3064,8 +3065,8 @@ filterGRNAndConnectGenes <- function(GRN,

     grn.filt = GRN@connections$TF_peaks[[permIndex]]$main  %>% 

       tibble::as_tibble() %>%

       dplyr::left_join(GRN@data$TFs$translationTable, by = c("TF.name")) %>%

-      dplyr::select(-HOCOID, -ENSEMBL) %>%

-      dplyr::mutate(TF.ENSEMBL = as.factor(TF.ENSEMBL))

+      dplyr::select(-.data$HOCOID, -.data$ENSEMBL) %>%

+      dplyr::mutate(TF.ENSEMBL = as.factor(.data$TF.ENSEMBL))

     if (is.null(grn.filt)) {

       message = "No TF-peak connections found. Run the function addConnections_TF_peak first"

@@ -3189,7 +3190,7 @@ filterGRNAndConnectGenes <- function(GRN,

       checkmate::assertIntegerish(peak_gene.maxDistance, lower = 0)

       futile.logger::flog.info(paste0(" Filter peak-gene connections for their distance and keep only connections with a maximum distance of  ", peak_gene.maxDistance))

       futile.logger::flog.info(paste0("  Number of peak-gene rows before filtering connection types: ", nrow(peakGeneCorrelations)))

-      peakGeneCorrelations = dplyr::filter(peakGeneCorrelations, peak_gene.distance < peak_gene.maxDistance)

+      peakGeneCorrelations = dplyr::filter(peakGeneCorrelations, .data$peak_gene.distance < peak_gene.maxDistance)

       futile.logger::flog.info(paste0("  Number of peak-gene rows after filtering connection types: ", nrow(peakGeneCorrelations)))

     }

@@ -3365,9 +3366,9 @@ filterGRNAndConnectGenes <- function(GRN,

                     dplyr::starts_with("peak_gene."),

                     dplyr::starts_with("gene."),

                     -dplyr::starts_with("peak.gene.")) %>% # these are the annotation columns by ChipSeeker that are confusing

-      dplyr::mutate(peak.ID      = as.factor(peak.ID),

-                    gene.ENSEMBL = as.factor(gene.ENSEMBL),

-                    TF.name      = as.factor(TF.name))

+      dplyr::mutate(peak.ID      = as.factor(.data$peak.ID),

+                    gene.ENSEMBL = as.factor(.data$gene.ENSEMBL),

+                    TF.name      = as.factor(.data$TF.name))

     GRN@connections$all.filtered[[permIndex]] = grn.filt

@@ -3482,27 +3483,27 @@ filterGRNAndConnectGenes <- function(GRN,

     # Plot No. 1

     res.l$ihwPlots$estimatedWeights  = 

       IHW::plot(ihw_res, what = "weights") + 

-      ggtitle("Estimated weights")

+      ggplot2::ggtitle("Estimated weights")

     # Plot No. 2

     res.l$ihwPlots$decisionBoundary  = 

       IHW::plot(ihw_res, what = "decisionboundary") + 

-      ggtitle("Decision boundary")

+      ggplot2::ggtitle("Decision boundary")

     # Plot No. 3

     res.l$ihwPlots$rawVsAdjPVal_all <- 

       as.data.frame(ihw_res) %>%

-      ggplot(aes(x = .data$pvalue, y = adj_pvalue, col = group)) + 

-      geom_point(size = 0.25) + scale_colour_hue(l = 70, c = 150, drop = FALSE) + 

-      ggtitle("Raw versus adjusted p-values")

+      ggplot2::ggplot(ggplot2::aes(x = .data$pvalue, y = .data$adj_pvalue, col = .data$group)) + 

+      ggplot2::geom_point(size = 0.25) + ggplot2::scale_colour_hue(l = 70, c = 150, drop = FALSE) + 

+      ggplot2::ggtitle("Raw versus adjusted p-values")

     # TODO why hard-coded

     pValThreshold = 0.2

     # Plot No. 4

     res.l$ihwPlots$rawVsAdjPVal_subset = 

-      res.l$ihwPlots$rawVsAdjPVal_all %+% subset(IHW::as.data.frame(ihw_res), adj_pvalue <= pValThreshold) + 

-      ggtitle("raw versus adjusted p-values (zoom)")

+      res.l$ihwPlots$rawVsAdjPVal_all %+% subset(IHW::as.data.frame(ihw_res), .data$adj_pvalue <= pValThreshold) + 

+      ggplot2::ggtitle("raw versus adjusted p-values (zoom)")

     ##                       ##

     ## 2. p-value histograms ##

@@ -3515,8 +3516,8 @@ filterGRNAndConnectGenes <- function(GRN,

     # Plot No. 5

     res.l$ihwPlots$pValHistogram = 

-      ggplot(data.df, aes(x = pValues)) + geom_histogram(binwidth = 0.025, boundary = 0) + 

-      ggtitle("p-Value histogram independent of the covariate")

+      ggplot2::ggplot(data.df, ggplot2::aes(x = .data$pValues)) + ggplot2::geom_histogram(binwidth = 0.025, boundary = 0) + 

+      ggplot2::ggtitle("p-Value histogram independent of the covariate")

     # Stratified p-value histograms by covariate

     # Plot No. 6

@@ -3525,14 +3526,14 @@ filterGRNAndConnectGenes <- function(GRN,

     data.df$covariate_group <- IHW::groups_by_filter(data.df$covariate, nGroups)

     res.l$ihwPlots$pValHistogramGroupedByCovariate = 

-      ggplot(data.df, aes(x=pValues)) + geom_histogram(binwidth = 0.025, boundary = 0) + 

-      facet_wrap( ~covariate_group, nrow = 2) + 

-      ggtitle("p-Value histogram grouped by the covariate")

+      ggplot2::ggplot(data.df, ggplot2::aes(x=.data$pValues)) + ggplot2::geom_histogram(binwidth = 0.025, boundary = 0) + 

+        ggplot2::facet_wrap( ~covariate_group, nrow = 2) + 

+      ggplot2::ggtitle("p-Value histogram grouped by the covariate")

     # Plot No. 7

     res.l$ihwPlots$pValHistogramGroupedByCovariateECDF = 

-      ggplot(data.df, aes(x = pValues, col = covariate_group)) + stat_ecdf(geom = "step")  + 

-      ggtitle("p-Value histogram grouped by the covariate (ECDF)")

+      ggplot2::ggplot(data.df, ggplot2::aes(x = .data$pValues, col = .data$covariate_group)) + ggplot2::stat_ecdf(geom = "step")  + 

+      ggplot2::ggtitle("p-Value histogram grouped by the covariate (ECDF)")

     # Check whether the covariate is informative about power under the alternative (property 1), 

@@ -3542,8 +3543,8 @@ filterGRNAndConnectGenes <- function(GRN,

     # Plot No. 8

     res.l$ihwPlots$pValAgainstRankCovariate = 

-      ggplot(data.df, aes(x= covariateRank, y = -log10(pValues))) + geom_hex(bins = 100) + 

-      ggtitle("p-Value against rank of the covariate")

+      ggplot2::ggplot(data.df, ggplot2::aes(x= .data$covariateRank, y = -log10(.data$pValues))) + ggplot2::geom_hex(bins = 100) + 

+      ggplot2::ggtitle("p-Value against rank of the covariate")

     if (!is.null(pdfFile)) {

@@ -3580,9 +3581,9 @@ add_TF_gene_correlation <- function(GRN, corMethod = "pearson", addRobustRegress

   checkmate::assertClass(GRN, "GRN")  

   GRN = .addFunctionLogToObject(GRN)    

   checkmate::assertChoice(corMethod, c("pearson", "spearman"))

-  assertFlag(addRobustRegression)

+  checkmate::assertFlag(addRobustRegression)

   checkmate::assertIntegerish(nCores, lower = 1)

-  assertFlag(forceRerun)

+  checkmate::assertFlag(forceRerun)

   if (addRobustRegression) {

       packageMessage = paste0("The package robust is not installed, but needed here due to addRobustRegression = TRUE. Please install it and re-run this function or change addRobustRegression to FALSE.")

@@ -3607,8 +3608,8 @@ add_TF_gene_correlation <- function(GRN, corMethod = "pearson", addRobustRegress

       permIndex = as.character(permutationCur)

       TF_genePairs = GRN@connections$all.filtered[[permIndex]] %>%

-        dplyr::filter(!is.na(gene.ENSEMBL)) %>%

-        dplyr::select(TF.name, gene.ENSEMBL) %>%

+        dplyr::filter(!is.na(.data$gene.ENSEMBL)) %>%

+        dplyr::select(.data$TF.name, .data$gene.ENSEMBL) %>%

         dplyr::distinct() %>%

         dplyr::left_join(GRN@data$TFs$translationTable, by = c("TF.name"), suffix = c("", ".transl")) # %>%

       # dplyr::distinct(ENSEMBL, ENSEMBL.transl)

@@ -3619,7 +3620,7 @@ add_TF_gene_correlation <- function(GRN, corMethod = "pearson", addRobustRegress

         futile.logger::flog.info(paste0("  Iterate through ", maxRow, " TF-gene combinations and (if possible) calculate correlations using ", nCores, " cores. This may take a few minutes."))

-        countsRNA.clean  = dplyr::select(getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur)), -ENSEMBL)

+        countsRNA.clean  = dplyr::select(getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur)), -.data$ENSEMBL)

         map_TF =   match(TF_genePairs$TF.ENSEMBL, getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur))$ENSEMBL)

         map_gene = match(TF_genePairs$gene.ENSEMBL, getCounts(GRN, type = "rna", norm = TRUE, permuted = as.logical(permutationCur))$ENSEMBL)

@@ -3664,7 +3665,7 @@ add_TF_gene_correlation <- function(GRN, corMethod = "pearson", addRobustRegress

                         TF.name           = as.factor(.data$TF.name)) %>%

           dplyr::rename(TF_gene.r     = .data$r, 

                         TF_gene.p_raw = .data$p.raw) %>%

-          dplyr::select(TF.name, TF.ENSEMBL, gene.ENSEMBL, tidyselect::everything())

+          dplyr::select(.data$TF.name, .data$TF.ENSEMBL, .data$gene.ENSEMBL, tidyselect::everything())

         if (addRobustRegression) {

@@ -3770,7 +3771,7 @@ addSNPOverlap <- function(grn, SNPData, col_chr = "chr", col_pos = "pos", col_pe

   query_overlap_df     = as.data.frame(S4Vectors::elementMetadata(peaks.gr)[query_row_ids, col_peakID])

   subject_overlap_df   = as.data.frame( SNPs.gr)[subject_row_ids, c("seqnames", "start")]

-  overlaps.df = cbind.data.frame(query_overlap_df,subject_overlap_df) %>% dplyr::mutate(seqnames = as.character(seqnames))

+  overlaps.df = cbind.data.frame(query_overlap_df,subject_overlap_df) %>% dplyr::mutate(seqnames = as.character(.data$seqnames))

   colnames(overlaps.df) = c("peakID", "SNP_chr", "SNP_start")

   if (addAllColumns) {

@@ -3785,13 +3786,13 @@ addSNPOverlap <- function(grn, SNPData, col_chr = "chr", col_pos = "pos", col_pe

   colnamesNew = setdiff(colnames(overlaps.df), col_peakID)

   overlaps.summarized.df =  overlaps.df %>%

-    dplyr::group_by(peak) %>%

+    dplyr::group_by(.data$peak) %>%

     dplyr::summarise_at(colnamesNew, myfun) %>%

     dplyr::ungroup()

   # Add no of SNPs also as column

   overlaps.summarized.df = overlaps.summarized.df %>%

-    dplyr::mutate(SNP_nOverlap = stringr::str_count(SNP_chr, stringr::fixed("|")) + 1)

+    dplyr::mutate(SNP_nOverlap = stringr::str_count(.data$SNP_chr, stringr::fixed("|")) + 1)

   grn.SNPs = dplyr::left_join(grn, overlaps.summarized.df, by = col_peakID)