update docs

.Rbuildignore

@@ -13,3 +13,5 @@
 ^pkgdown$
 ^man/figures/README-*
 ^dev$
+^\.git/
+\.Rproj\.user/

R/clustering.R

@@ -330,7 +330,7 @@ tof_cluster_kmeans <-
 #' the healthy subpopulation with the minimum distance to the cell represented
 #' by that row.
 #'
-#' If `return_distances = FALSE`, a tibble with one column named `.{distance_function}_cluster`.
+#' If `return_distances = FALSE`, a tibble with one column named `.\{distance_function\}_cluster`.
 #' This column will contain an integer vector of length `nrow(tof_tibble)` indicating the id of
 #' the developmental cluster to which each cell (i.e. each row) in `tof_tibble` was assigned.
 #'
@@ -436,7 +436,7 @@ tof_cluster_ddpr <-
 
 #' Cluster high-dimensional cytometry data.
 #'
-#' This function is a wrapper around {tidytof}'s tof_cluster_* function family.
+#' This function is a wrapper around tidytof's tof_cluster_* function family.
 #' It performs clustering on high-dimensional cytometry data using a user-specified method (of 5 choices)
 #' and each method's corresponding input parameters.
 #'
@@ -527,7 +527,7 @@ tof_cluster <-
 
 #' Cluster (ungrouped) high-dimensional cytometry data.
 #'
-#' This function is a wrapper around {tidytof}'s tof_cluster_* function family and
+#' This function is a wrapper around tidytof's tof_cluster_* function family and
 #' provides a low-level API for clustering ungrouped data frames. It is a subroutine
 #' of tof_cluster and shouldn't be called directly by users.
 #'
@@ -591,7 +591,7 @@ tof_cluster_tibble <-
 
 #' Cluster (grouped) high-dimensional cytometry data.
 #'
-#' This function is a wrapper around {tidytof}'s tof_cluster_* function family and
+#' This function is a wrapper around tidytof's tof_cluster_* function family and
 #' provides a low-level API for clustering grouped data frames. It is a subroutine
 #' of tof_cluster and shouldn't be called directly by users.
 #'

R/dimensionality_reduction.R

@@ -351,7 +351,7 @@ tof_reduce_umap <-
 
 #' Apply dimensionality reduction to a single-cell dataset.
 #'
-#' This function is a wrapper around {tidytof}'s tof_reduce_* function family.
+#' This function is a wrapper around tidytof's tof_reduce_* function family.
 #' It performs dimensionality reduction on single-cell data using a user-specified method
 #' (of 3 choices) and each method's corresponding input parameters
 #'

R/metaclustering.R

@@ -690,7 +690,7 @@ tof_metacluster_flowsom <-
 
 #' Metacluster clustered CyTOF data.
 #'
-#' This function is a wrapper around {tidytof}'s tof_metacluster_* function family.
+#' This function is a wrapper around tidytof's tof_metacluster_* function family.
 #' It performs metaclustering on CyTOF data using a user-specified method (of 5 choices)
 #' and each method's corresponding input parameters.
 #'

R/patient-level_modeling.R

@@ -173,7 +173,7 @@ tof_split_data <-
 #'
 #' This function creates a regular hyperparameter search grid (in the form of a
 #' \code{\link[dplyr]{tibble}}) specifying the search space for the two
-#' hyperparameters of a generalized linear model using the {glmnet} package:
+#' hyperparameters of a generalized linear model using the glmnet package:
 #' the regularization penalty term
 #' and the lasso/ridge regression mixture term.
 #'

R/quality_control.R

@@ -172,7 +172,7 @@ tof_calculate_flow_rate <-
 #' a tibble including only the computed flags should be returned (FALSE, the default).
 #'
 #' @return A tibble with the same number of rows as `tof_tibble`. If augment = FALSE
-#' (the default), it will have 3 columns: "{time_col}" (the same column as `time_col`),
+#' (the default), it will have 3 columns: "\{time_col\}" (the same column as `time_col`),
 #' "timestep" (the numeric timestep to which each cell was assigned based on its
 #' value for `time_col`), and "flagged_window" (a boolean vector indicating if
 #' each cell was collecting during a timestep flagged for having a high or low
@@ -306,7 +306,7 @@ tof_assess_flow_rate_tibble <-
 #' a tibble including only the computed flags should be returned (FALSE, the default).
 #'
 #' @return A tibble with the same number of rows as `tof_tibble`. If augment = FALSE
-#' (the default), it will have 3 columns: "{time_col}" (the same column as `time_col`),
+#' (the default), it will have 3 columns: "\{time_col\}" (the same column as `time_col`),
 #' "timestep" (the numeric timestep to which each cell was assigned based on its
 #' value for `time_col`), and "flagged_window" (a boolean vector indicating if
 #' each cell was collecting during a timestep flagged for having a high or low
@@ -652,7 +652,7 @@ tof_sum_rescale <- function(vec) {
 #' Two of the columns will be "entropy" (the entropy value for each cell) and "flagged_cell"
 #' (a boolean value indicating if each cell had an entropy value above entropy_threshold).
 #' The other NUM_CLUSTERS columns will contain the mahalanobis distances from each cell
-#' to each of the clusters in cluster_col (named ".mahalanobis_{cluster_name}").
+#' to each of the clusters in cluster_col (named ".mahalanobis_\{cluster_name\}").
 #' If augment = TRUE, the same 2 + NUM_CLUSTERS columns will be column-bound to
 #' tof_tibble, and the resulting tibble will be returned.
 #'

R/utils.R

@@ -651,7 +651,7 @@ pull_unless_null <- function(tib, uq_colname) {
 
 #' Estimate the local densities for all cells in a high-dimensional cytometry dataset.
 #'
-#' This function is a wrapper around {tidytof}'s tof_*_density() function family.
+#' This function is a wrapper around tidytof's tof_*_density() function family.
 #' It performs local density estimation on high-dimensional cytometry data using a user-specified
 #' method (of 3 choices) and each method's corresponding input parameters.
 #'

inst/CITATION

@@ -0,0 +1,46 @@
+pkgVer <- function(pkg) {
+    if (!exists("meta") || is.null(meta)) meta <- packageDescription(pkg)
+    ver <- meta$Version
+    paste0('https://github.com/keyes-timothy/tidytof/', pkg, ' - R package version ', ver)
+}
+
+c(
+
+    bibentry(bibtype="manual",
+        title = "tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis",
+        author = c(person(given = "Timothy",
+             family = "Keyes",
+             role = c("cre"),
+             email = "[email protected]",
+             comment = c(ORCID = "0000-0003-0423-9679")),
+      person(given = "Kara",
+             family = "Davis",
+             role = c("rth", "own"),
+             email = "[email protected]"),
+      person(given = "Garry",
+             family = "Nolan",
+             role = c("rth", "own"),
+             email = "[email protected]")),
+        year = format(Sys.Date(), "%Y"),
+        url = "http://www.bioconductor.org/packages/tidytof",
+        note = pkgVer('tidytof'),
+        doi = '10.18129/B9.bioc.tidytof'
+    ),
+    bibentry(bibtype = "article",
+         title = "tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis",
+         author = personList(
+             as.person("Keyes, Timothy J."),
+             as.person("Koladiya, Abhishek"),
+             as.person("Lo, Yu-Chen"),
+             as.person("Nolan, Garry P."),
+             as.person("Davis, Kara L.")
+         ),
+         year = "2023",
+         journal = "Bioinformatics Advances",
+         volume = "3",
+         number = "1",
+         doi = "10.1093/bioadv/vbad071",
+         url = "https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad071/7192984"
+)
+
+)