Use 4 spaces as indents, replace accessors @ with functions.

DESCRIPTION

@@ -23,9 +23,9 @@ Collate:
     'gs_score-methods.R'
     'plot.R'
     'scale_mgm.R'
+    'smartid-package.R'
     'score-methods.R'
     'select_markers.R'
-    'smartid-package.R'
     'top_markers.R'
     'top_markers-methods.R'
 Depends: 

NAMESPACE

@@ -22,5 +22,7 @@ import(stats)
 importClassesFrom(Matrix,dgCMatrix)
 importFrom(SummarizedExperiment,SummarizedExperiment)
 importFrom(SummarizedExperiment,assay)
+importFrom(SummarizedExperiment,colData)
+importFrom(SummarizedExperiment,metadata)
 importFrom(utils,globalVariables)
 importFrom(utils,stack)

R/AllGenerics.R

@@ -17,9 +17,9 @@
 #' @inheritParams cal_score_init
 #' @param data an expression object, can be matrix or SummarizedExperiment
 #' @param slot a character, specify which slot to use when data is se object,
-#'             optional, default 'counts'
+#'     optional, default 'counts'
 #' @param new.slot a character, specify the name of slot to save score in se object,
-#'                 optional, default 'score'
+#'     optional, default 'score'
 #'
 #' @return A list of matrices or se object containing combined score
 #'
@@ -57,7 +57,7 @@ setGeneric(
 #' @inheritParams top_markers_init
 #' @param data an expression object, can be matrix or SummarizedExperiment
 #' @param slot a character, specify which slot to use when data is se object,
-#'             optional, default 'score'
+#'     optional, default 'score'
 #'
 #' @return A tibble with top n feature names, group labels and ordered scores
 #'
@@ -93,9 +93,9 @@ setGeneric(
 #' @param data an expression object, can be matrix or SummarizedExperiment
 #' @param features vector or named list, feature names to compute score
 #' @param slot a character, specify which slot to use when data is se object,
-#'             optional, default 'score'
+#'     optional, default 'score'
 #' @param suffix a character, specify the name suffix to save score when
-#'               features is a named list
+#'     features is a named list
 #'
 #' @return A vector of overall score for each sample
 #'

R/plot.R

@@ -79,7 +79,7 @@ plot_mm_clust <- function(score, clust) {
 #' @param ref.group character, reference group name
 #' @param label vector, group labels
 #' @param method character, statistical test to use,
-#'               details in [ggpubr::stat_compare_means()]
+#'     details in [ggpubr::stat_compare_means()]
 #'
 #' @return faceted ggplot object
 #' @export

R/score-methods.R

@@ -1,4 +1,4 @@
-#' @include score.R
+#' @include score.R smartid-package.R
 NULL
 
 #' @rdname cal_score
@@ -42,10 +42,10 @@ setMethod(
     expr <- SummarizedExperiment::assay(data, i = slot)
     ## get label
     if (!is.null(par.idf) & !is.null(par.idf$label)) {
-      par.idf$label <- data@colData[[par.idf$label]]
+      par.idf$label <- colData(data)[[par.idf$label]]
     }
     if (!is.null(par.iae) & !is.null(par.iae$label)) {
-      par.iae$label <- data@colData[[par.iae$label]]
+      par.iae$label <- colData(data)[[par.iae$label]]
     }
 
     res <- cal_score(
@@ -58,9 +58,9 @@ setMethod(
     )
 
     SummarizedExperiment::assay(data, i = new.slot) <- res$score
-    data@metadata$tf <- res$tf
-    data@metadata$idf <- res$idf
-    data@metadata$iae <- res$iae
+    metadata(data)$tf <- res$tf
+    metadata(data)$idf <- res$idf
+    metadata(data)$iae <- res$iae
 
     return(data)
   }

R/score.R

@@ -52,9 +52,9 @@ idf_iae_methods <- function() {
 #' @param expr a count matrix, features in row and cells in column
 #' @param tf a character, specify the TF method to use, can be "tf" or "logtf"
 #' @param idf a character, specify the IDF method to use. Available methods can
-#'            be accessed using [idf_iae_methods()]
+#'     be accessed using [idf_iae_methods()]
 #' @param iae a character, specify the IAE method to use. Available methods can
-#'            be accessed using [idf_iae_methods()]
+#'     be accessed using [idf_iae_methods()]
 #' @param par.idf other parameters for specified IDF methods
 #' @param par.iae other parameters for specified IAE methods
 #'

R/select_markers.R

@@ -8,9 +8,9 @@ NULL
 #' @param prob numeric, probability cutoff for 1st component classification
 #' @param k integer, number of components of mixtures
 #' @param ratio numeric, ratio cutoff of 1st component mu to 2nd component mu,
-#'              only when ratio > cutoff will return markers for the group
+#'     only when ratio > cutoff will return markers for the group
 #' @param dist can be one of "norm" and "gamma", specify if to use
-#'             [mixtools::normalmixEM()] or [mixtools::gammamixEM()]
+#'     [mixtools::normalmixEM()] or [mixtools::gammamixEM()]
 #' @param maxit integer, maximum number of iterations for EM
 #' @param plot logical, if to plot mixture density and hist
 #' @param ... other params for [mixtools::normalmixEM()] or [mixtools::gammamixEM()]
@@ -76,9 +76,9 @@ markers_mixmdl <- function(top_markers, column = ".dot",
 #'
 #' @inheritParams markers_mixmdl
 #' @param s_thres NULL or numeric, only features with score > threshold will be
-#'                returned, if NULL will use 2 * average probability as threshold
-#' @param method can be "max.one" or "remove.min", if to only keep features in 1st
-#'               component or return features not in the last component
+#'     returned, if NULL will use 2 * average probability as threshold
+#' @param method can be "max.one" or "remove.min", if to only keep features in
+#'     1st component or return features not in the last component
 #' @param ... other params for [mclust::densityMclust()]
 #'
 #' @return a list of markers for each group

R/smartid-package.R

@@ -2,18 +2,18 @@
 #' @import methods
 #' @import stats
 #' @import graphics
-#' @importFrom SummarizedExperiment SummarizedExperiment assay
+#' @importFrom SummarizedExperiment SummarizedExperiment assay colData metadata
 #' @importFrom utils globalVariables stack
 #' @importClassesFrom Matrix dgCMatrix
 NULL
 
 #' Scoring and Marker Selection method based on modified TF-IDF
 #'
 #' `smartid` This package enables automated selection of group specific signature,
-#'   especially for rare population. The package is developed for generating
-#'   specific lists of signature genes based on TF-IDF modified methods. It can
-#'   also be used as a new gene-set scoring method or data transformation method.
-#'   Multiple visualization functions are implemented in this package.
+#'     especially for rare population. The package is developed for generating
+#'     specific lists of signature genes based on TF-IDF modified methods. It
+#'     can also be used as a new gene-set scoring method or data transformation
+#'     method. Multiple visualization functions are implemented in this package.
 #'
 #' @author Jinjin Chen \email{chen.j@@wehi.edu.au}
 #' @name smartid_Package

R/tf_idf_iae_wrappers.R

@@ -131,7 +131,7 @@ idf_sd <- function(expr, features = NULL, thres = 0) {
 #'
 #' @inheritParams idf_rf
 #' @param minPts integer, minimum size of clusters, default 2.
-#'               Details in [dbscan::hdbscan()].
+#'     Details in [dbscan::hdbscan()].
 #' @param ... parameters for [dbscan::hdbscan()]
 #'
 #' @return a matrix of inverse cell frequency score
@@ -414,7 +414,7 @@ iae_sd <- function(expr, features = NULL, thres = 0) {
 #'
 #' @inheritParams idf_rf
 #' @param minPts integer, minimum size of clusters, default 2.
-#'               Details in [dbscan::hdbscan()].
+#'     Details in [dbscan::hdbscan()].
 #' @param ... parameters for [dbscan::hdbscan()]
 #'
 #' @return a matrix of inverse average expression score

R/top_markers-methods.R

@@ -63,7 +63,7 @@ setMethod(
     ## get expr
     expr <- SummarizedExperiment::assay(data, i = slot)
     ## get label
-    label <- data@colData[[label]]
+    label <- colData(data)[[label]]
 
     top_m <- top_markers(
       data = expr,

R/top_markers.R

@@ -3,7 +3,7 @@
 #' @inheritParams top_markers_abs
 #' @inheritParams top_markers_glm
 #' @param use.glm logical, if to use [stats::glm()] to compute group mean score,
-#'                if TRUE, also compute mean score difference as output
+#'     if TRUE, also compute mean score difference as output
 #' @param ... params for [top_markers_abs()] or [top_markers_glm()]
 #'
 #' @return a tibble with feature names, group labels and ordered processed scores
@@ -49,7 +49,7 @@ top_markers_init <- function(data, label, n = 10,
 #' @param label vector, group labels
 #' @param n integer, number of returned top genes for each group
 #' @param method character, specify metric to compute, can be one of "median",
-#'               "mad", "mean"
+#'     "mad", "mean"
 #' @param scale logical, if to scale data by row
 #' @param use.mgm logical, if to scale data using [scale_mgm()]
 #' @param softmax logical, if to apply softmax transformation on output