Use latest tidyselect interface

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: clustermole
 Title: Unbiased Single-Cell Transcriptomic Data Cell Type Identification
-Version: 1.1.0.9000
+Version: 1.1.0.9001
 Authors@R: 
     person(given = "Igor",
            family = "Dolgalev",
@@ -19,18 +19,17 @@ Imports:
     GSVA (>= 1.26.0),
     magrittr,
     methods,
-    rlang (>= 0.1.2),
+    rlang,
     singscore,
     tibble,
     tidyr,
     utils
 Suggests: 
     covr,
     knitr,
-    prettydoc,
     rmarkdown,
     roxygen2,
-    testthat (>= 2.1.0)
+    testthat
 biocViews:
 Encoding: UTF-8
 RoxygenNote: 7.2.3

R/enrichment.R

@@ -22,7 +22,6 @@
 #' @examples
 #' # my_enrichment <- clustermole_enrichment(expr_mat = my_expr_mat, species = "hs")
 clustermole_enrichment <- function(expr_mat, species, method = "gsva") {
-
   # check that the expression matrix seems reasonable
   if (!is(expr_mat, "matrix")) {
     stop("expression matrix is not a matrix")
@@ -53,7 +52,7 @@ clustermole_enrichment <- function(expr_mat, species, method = "gsva") {
   # create a table of cell types (without genes)
   celltypes_tbl <-
     markers_tbl %>%
-    dplyr::select(-dplyr::starts_with("gene")) %>%
+    dplyr::select(!dplyr::starts_with("gene")) %>%
     dplyr::distinct()
 
   # run the actual enrichment analysis
@@ -80,7 +79,7 @@ get_scores <- function(expr_mat, markers_list, method = c("gsva", "ssgsea", "sin
       method = "gsva", kcdf = "Gaussian", parallel.sz = 1, verbose = FALSE
     )
     scores_tbl <- lengthen_scores(scores_mat)
-    scores_gsva_tbl <- dplyr::select(scores_tbl, .data$cluster, .data$celltype_full, score_rank_gsva = .data$score_rank)
+    scores_gsva_tbl <- dplyr::select(scores_tbl, "cluster", "celltype_full", score_rank_gsva = "score_rank")
   }
 
   if (method == "ssgsea" || method == "all") {
@@ -89,7 +88,7 @@ get_scores <- function(expr_mat, markers_list, method = c("gsva", "ssgsea", "sin
       method = "ssgsea", kcdf = "Gaussian", parallel.sz = 1, verbose = FALSE
     )
     scores_tbl <- lengthen_scores(scores_mat)
-    scores_ssgsea_tbl <- dplyr::select(scores_tbl, .data$cluster, .data$celltype_full, score_rank_ssgsea = .data$score_rank)
+    scores_ssgsea_tbl <- dplyr::select(scores_tbl, "cluster", "celltype_full", score_rank_ssgsea = "score_rank")
   }
 
   if (method == "singscore" || method == "all") {
@@ -98,7 +97,7 @@ get_scores <- function(expr_mat, markers_list, method = c("gsva", "ssgsea", "sin
     scores_mat <- singscore::multiScore(rankData = rankGenes(expr_mat), upSetColc = markers_gsc)
     scores_mat <- scores_mat$Scores
     scores_tbl <- lengthen_scores(scores_mat)
-    scores_singscore_tbl <- dplyr::select(scores_tbl, .data$cluster, .data$celltype_full, score_rank_singscore = .data$score_rank)
+    scores_singscore_tbl <- dplyr::select(scores_tbl, "cluster", "celltype_full", score_rank_singscore = "score_rank")
   }
 
   if (method == "all") {
@@ -128,8 +127,8 @@ lengthen_scores <- function(scores_mat) {
   scores_mat %>%
     round(10) %>%
     tibble::as_tibble(rownames = "celltype_full") %>%
-    tidyr::gather(key = "cluster", value = "score", -.data$celltype_full) %>%
-    dplyr::select(.data$cluster, .data$celltype_full, .data$score) %>%
+    tidyr::gather(key = "cluster", value = "score", -"celltype_full") %>%
+    dplyr::select("cluster", "celltype_full", "score") %>%
     dplyr::group_by(.data$cluster) %>%
     dplyr::mutate(score_rank = rank(desc(.data$score), ties.method = "first")) %>%
     dplyr::ungroup()

R/markers.R

@@ -6,7 +6,6 @@
 #'
 #' @return A data frame of cell type markers (one gene per row).
 #'
-#' @import dplyr
 #' @export
 #'
 #' @examples
@@ -16,12 +15,10 @@ clustermole_markers <- function(species = c("hs", "mm")) {
   species <- match.arg(species)
   m_tbl <- clustermole_markers_tbl
   if (species == "hs") {
-    m_tbl %>%
-      dplyr::select(-.data$gene_mm) %>%
-      dplyr::rename(gene = .data$gene_hs)
+    names(m_tbl)[names(m_tbl) == "gene_hs"] <- "gene"
+    m_tbl[, !(names(m_tbl) %in% "gene_mm")]
   } else if (species == "mm") {
-    m_tbl %>%
-      dplyr::select(-.data$gene_hs) %>%
-      dplyr::rename(gene = .data$gene_mm)
+    names(m_tbl)[names(m_tbl) == "gene_mm"] <- "gene"
+    m_tbl[, !(names(m_tbl) %in% "gene_hs")]
   }
 }

R/utils.R

@@ -20,6 +20,6 @@ read_gmt <- function(file, geneset_label = "celltype", gene_label = "gene") {
   gmt_list <- lapply(gmt_split, tail, -2)
   names(gmt_list) <- sapply(gmt_split, head, 1)
   gmt_df <- tibble::enframe(gmt_list, name = geneset_label, value = gene_label)
-  gmt_df <- tidyr::unnest(gmt_df, gene_label)
+  gmt_df <- tidyr::unnest(gmt_df, all_of(gene_label))
   gmt_df
 }