@@ -2,7 +2,7 @@ Type: Package

 Package: CellaRepertorium

 Title: Data structures, clustering and testing for single 

     cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq)

-Version: 0.99.0

+Version: 0.99.1

 Authors@R: 

     c(person(given = "Andrew",

              family = "McDavid",

@@ -27,7 +27,7 @@ Description: Methods to cluster and analyze high-throughput

     hypergeometric models.

 License: GPL-3

 Depends: 

-    R (>= 3.5.0)

+    R (>= 4.0)

 Imports:

     dplyr,

     tibble,

@@ -36,7 +36,7 @@ Imports:

     Rcpp,

     reshape2,

     methods,

-    rlang,

+    rlang (>= 0.3),

     purrr,

     Matrix,

     S4Vectors,

@@ -62,6 +62,7 @@ importFrom(methods,new)

 importFrom(methods,slot)

 importFrom(methods,validObject)

 importFrom(rlang,":=")

+importFrom(rlang,.data)

 importFrom(rlang,sym)

 importFrom(rlang,syms)

 importFrom(stats,as.dist)

@@ -78,4 +79,5 @@ importFrom(tibble,as_tibble)

 importFrom(tibble,data_frame)

 importFrom(tibble,tibble)

 importFrom(utils,data)

+importFrom(utils,packageVersion)

 useDynLib(CellaRepertorium)

deleted file mode 100644

Binary files a/R/.DS_Store and /dev/null differ

@@ -7,6 +7,7 @@

 #'

 #' @return object representing the filtration (currently a list)

 #' @export

+#' @importFrom rlang .data

 #'

 #' @examples

 #' cluster_filterset(min_number = 1, min_freq = 0)

@@ -25,9 +26,11 @@ cluster_filterset = function(min_number = 0, min_freq = 0, white_list = NULL){

     canon = canonicalize_cell(ccdb,

                               contig_filter_args = !!rlang::enexpr(contig_filter_args),

                               tie_break_keys, overwrite = TRUE, contig_fields = contig_fields)

-    count = canon$cell_tbl %>% group_by(!!!syms(ccdb$cluster_pk)) %>% summarize(n = dplyr::n(), freq = n/nrow(canon$cell_tbl))

+    count = canon$cell_tbl

+    count = count %>% group_by(!!!syms(ccdb$cluster_pk))

+    count = count %>% summarize(n = dplyr::n(), freq = .data$n/nrow(canon$cell_tbl))

     if(!is.null(filterset$white_list)) count = semi_join(count, filterset$white_list, by = ccdb$cluster_pk)

-    filter(count, n >= filterset$min_number, freq >= filterset$min_freq)

+    filter(count, .data$n >= filterset$min_number, .data$freq >= filterset$min_freq)

 }

 #' @describeIn cluster_logistic_test split `ccdb` and conduct tests within strata

@@ -55,7 +58,7 @@ cluster_test_by = function(ccdb, fields  = 'chain', tbl = 'cluster_tbl', ...){

 #' @return table with one row per cluster/term.

 #' @export

 #' @importFrom stats as.formula

-#'

+#' @importFrom utils packageVersion

 #' @examples

 #' library(dplyr)

 #' data(ccdb_ex)

@@ -236,18 +236,6 @@ pairing_tables = function(ccdb,  canonicalize_fun = canonicalize_by_chain, table

 }

-plot_pairing = function(pairing_list, color_labels_by){

-    pl = pairing_list

-    pairs_plt = ggplot(pairing_list$cell_tbl, aes(x = cluster_idx.1_fct, y = cluster_idx.2_fct, color = sample, shape = pop)) + geom_jitter(width = .3, height = .3)

-

-    ylab = tibble(!!color_labels_by :=  ggplot_build(pairs_plt)$layout$panel_params[[1]]$y.label) %>% left_join(feature_tbl) %>% mutate(class_color = ifelse(is.na(class_color), '#E41A1C', class_color))

-

-    xlab = tibble(!!color_labels_by :=  ggplot_build(pairs_plt)$layout$panel_params[[1]]$x.label) %>% left_join(feature_tbl) %>% mutate(class_color = ifelse(is.na(class_color), '#E41A1C', class_color))

-

-    pairs_plt = pairs_plt + theme(axis.text.x = element_text(angle = 90, color = xlab$class_color, size = 8), axis.text.y = element_text(color = ylab$class_color, size = 8))

-

-

-}

 #' @export

 #' @describeIn enumerate_pairing Recode a table with IG chains