Styler and spell check.

R/AllClasses.R

@@ -1,2 +1,2 @@
 ## Class definitions
-setClassUnion(name = 'AnyMatrix', members = c("matrix", "dgCMatrix"))
+setClassUnion(name = "AnyMatrix", members = c("matrix", "dgCMatrix"))

R/gs_score-methods.R

@@ -8,7 +8,6 @@ setMethod(
   ),
   function(data,
            features = NULL) {
-
     ## compute overall score
     score <- gs_score_init(score = data, features = features)
 
@@ -32,7 +31,7 @@ setMethod(
 
     ## compute overall score
     score <- sapply(names(features), \(i)
-                    gs_score(data = data, features = features[[i]])) |>
+    gs_score(data = data, features = features[[i]])) |>
       data.frame()
     ## set colnames
     colnames(score) <- paste(names(features), suffix, sep = ".")

R/plot.R

@@ -6,15 +6,17 @@ plot_mm <- function(mixmdl, dist = c("norm", "gamma")) {
 
   p <- ggplot(data.frame(x = mixmdl$x), aes(x = x)) +
     geom_histogram()
-  if(dist == "norm") {
+  if (dist == "norm") {
     p <- p +
-      mapply(function(mean, sd, lambda, col) {
-        stat_function(
-          fun = function(x) {
-            (dnorm(x, mean = mean, sd = sd)) * lambda
-          },
-          col = col
-        )},
+      mapply(
+        function(mean, sd, lambda, col) {
+          stat_function(
+            fun = function(x) {
+              (dnorm(x, mean = mean, sd = sd)) * lambda
+            },
+            col = col
+          )
+        },
         mean = mixmdl$mu,
         sd = mixmdl$sigma,
         lambda = mixmdl$lambda,
@@ -23,18 +25,20 @@ plot_mm <- function(mixmdl, dist = c("norm", "gamma")) {
       geom_vline(xintercept = mixmdl$mu, col = seq_along(mixmdl$mu) + 1) +
       labs(x = "score", y = "density") +
       theme_bw()
-  }else {
+  } else {
     ## compute mu
-    mixmdl$mu <- mixmdl$gamma.pars[1,] * mixmdl$gamma.pars[2,] # alpha * beta
+    mixmdl$mu <- mixmdl$gamma.pars[1, ] * mixmdl$gamma.pars[2, ] # alpha * beta
 
     p <- p +
-      mapply(function(mean, alpha, beta, lambda, col) {
-        stat_function(
-          fun = function(x) {
-            (dgamma(x, shape = alpha, rate = beta)) * lambda
-          },
-          col = col
-        )},
+      mapply(
+        function(mean, alpha, beta, lambda, col) {
+          stat_function(
+            fun = function(x) {
+              (dgamma(x, shape = alpha, rate = beta)) * lambda
+            },
+            col = col
+          )
+        },
         mean = mixmdl$mu,
         alpha = mixmdl$gamma.pars["alpha", ],
         beta = mixmdl$gamma.pars["beta", ],
@@ -86,8 +90,10 @@ plot_mm_clust <- function(score, clust) {
 sin_score_boxplot <- function(data, features = NULL,
                               ref.group, label,
                               method = "t.test") {
-  if(is.null(features)) features <- rownames(data)
-  data[features,] |> as.matrix() |> as.data.frame() |>
+  if (is.null(features)) features <- rownames(data)
+  data[features, ] |>
+    as.matrix() |>
+    as.data.frame() |>
     dplyr::add_rownames("Gene") |>
     setNames(c("Gene", as.character(label))) |>
     tidyr::pivot_longer(-"Gene", names_to = "Type", values_to = "Score") |>
@@ -97,8 +103,10 @@ sin_score_boxplot <- function(data, features = NULL,
     geom_violin() +
     stat_summary(fun = median, geom = "crossbar") +
     facet_wrap(~Gene, scales = "free") +
-    ggpubr::stat_compare_means(label = "p.signif", method = method,
-                               ref.group = ref.group, label.y.npc = 1) +
+    ggpubr::stat_compare_means(
+      label = "p.signif", method = method,
+      ref.group = ref.group, label.y.npc = 1
+    ) +
     theme_classic() +
     theme(axis.text.x = element_blank())
 }
@@ -116,13 +124,17 @@ sin_score_boxplot <- function(data, features = NULL,
 ova_score_boxplot <- function(data, features,
                               ref.group, label,
                               method = "t.test") {
-  data.frame(Score = gs_score_init(data, features = features),
-             Group = label) |>
+  data.frame(
+    Score = gs_score_init(data, features = features),
+    Group = label
+  ) |>
     ggplot(aes(x = Group, y = Score, col = Group)) +
     geom_boxplot() +
     # geom_jitter(aes(col = Group), alpha = 0.2) +
-    ggpubr::stat_compare_means(label = "p.signif", method = method,
-                               ref.group = ref.group, label.y.npc = 1) +
+    ggpubr::stat_compare_means(
+      label = "p.signif", method = method,
+      ref.group = ref.group, label.y.npc = 1
+    ) +
     theme_classic() +
     theme(axis.text.x = element_blank())
 }
@@ -143,8 +155,9 @@ ova_score_boxplot <- function(data, features,
 #' score_barplot(top_n)
 score_barplot <- function(top_markers, column = ".dot", f_list, n = 30) {
   ## set f_list as features label for color
-  if(missing(f_list))
+  if (missing(f_list)) {
     f_list <- list(Features = top_markers$Genes)
+  }
 
   # ## get top n markers
   # top_markers <- top_markers(
@@ -158,22 +171,28 @@ score_barplot <- function(top_markers, column = ".dot", f_list, n = 30) {
   top_markers <- dplyr::slice_max(top_markers, Scores, n = n)
 
   ## add markers type
-  top_markers <- merge(top_markers, stack(f_list), by.x = "Genes",
-                       by.y = "values", all.x = TRUE)
+  top_markers <- merge(top_markers, stack(f_list),
+    by.x = "Genes",
+    by.y = "values", all.x = TRUE
+  )
 
   ## plot
-  ggplot(top_markers,
-         aes(
-           y = tidytext::reorder_within(Genes, Scores, !!ggplot2::sym(column)),
-           x = Scores,
-           fill = ind
-         )) +
+  ggplot(
+    top_markers,
+    aes(
+      y = tidytext::reorder_within(Genes, Scores, !!ggplot2::sym(column)),
+      x = Scores,
+      fill = ind
+    )
+  ) +
     geom_bar(stat = "identity") +
     facet_wrap(ggplot2::sym(column), scales = "free") +
     labs(y = "Genes", fill = "Markers Type") +
     tidytext::scale_y_reordered() +
     theme_classic()
 }
 
-utils::globalVariables(c("Group", "Score", "x", "Scores", "Comp", "Genes",
-                         "..density..", "stack", "values", "ind", "Type"))
+utils::globalVariables(c(
+  "Group", "Score", "x", "Scores", "Comp", "Genes",
+  "..density..", "stack", "values", "ind", "Type"
+))

R/scale_mgm.R

@@ -18,7 +18,7 @@ scale_mgm <- function(expr, label) {
 
   ## compute group means
   mgm <- sapply(unique(label), \(i)
-                sparseMatrixStats::rowMeans2(expr[, label == i], na.rm = TRUE)) |>  # get mean of each group
+  sparseMatrixStats::rowMeans2(expr[, label == i], na.rm = TRUE)) |> # get mean of each group
     rowMeans(na.rm = TRUE) # get mean of group mean
 
   ## scale

R/score-methods.R

@@ -12,7 +12,6 @@ setMethod(
            iae = "prob",
            par.idf = NULL,
            par.iae = NULL) {
-
     score <- cal_score_init(
       expr = data,
       tf = tf,
@@ -42,10 +41,10 @@ setMethod(
     ## get expr
     expr <- SummarizedExperiment::assay(data, i = slot)
     ## get label
-    if(!is.null(par.idf) & !is.null(par.idf$label)) {
+    if (!is.null(par.idf) & !is.null(par.idf$label)) {
       par.idf$label <- data@colData[[par.idf$label]]
     }
-    if(!is.null(par.iae) & !is.null(par.iae$label)) {
+    if (!is.null(par.iae) & !is.null(par.iae$label)) {
       par.iae$label <- data@colData[[par.iae$label]]
     }
 

R/score.R

@@ -13,12 +13,15 @@ NULL
 #' data <- matrix(rnorm(100), 10, dimnames = list(1:10))
 #' gs_score_init(data, 1:5)
 gs_score_init <- function(score, features = NULL) {
-  if(is.null(features)) features <- rownames(score)
+  if (is.null(features)) features <- rownames(score)
 
   ## check features
-  if(!all(features %in% rownames(score)))
-    warning(sprintf("Feature %s is not in score!\n",
-                    setdiff(features, rownames(score))))
+  if (!all(features %in% rownames(score))) {
+    warning(sprintf(
+      "Feature %s is not in score!\n",
+      setdiff(features, rownames(score))
+    ))
+  }
   features <- intersect(rownames(score), features)
   stopifnot("less than 2 features are in score rows!" = length(features) > 1)
 
@@ -36,10 +39,12 @@ gs_score_init <- function(score, features = NULL) {
 #' @examples
 #' idf_iae_methods()
 idf_iae_methods <- function() {
-  return(sort(c("label probability" = 'prob', "label relative frequency" = 'rf',
-                "label IGM" = 'igm', "null" = 'null',
-                "unlabel max" = 'm', "unlabel SD" = 'sd',
-                "unlabel HDBSCAN" = "hdb", "unlabel standard" = 'standard')))
+  return(sort(c(
+    "label probability" = "prob", "label relative frequency" = "rf",
+    "label IGM" = "igm", "null" = "null",
+    "unlabel max" = "m", "unlabel SD" = "sd",
+    "unlabel HDBSCAN" = "hdb", "unlabel standard" = "standard"
+  )))
 }
 
 #' Calculate score for each feature in each cell
@@ -58,8 +63,10 @@ idf_iae_methods <- function() {
 #' @examples
 #' data <- matrix(rpois(100, 2), 10, dimnames = list(1:10))
 #' label <- sample(c("A", "B"), 10, replace = TRUE)
-#' smartid:::cal_score_init(data, par.idf = list(label = label),
-#'                          par.iae = list(label = label))
+#' smartid:::cal_score_init(data,
+#'   par.idf = list(label = label),
+#'   par.iae = list(label = label)
+#' )
 cal_score_init <- function(expr, tf = c("logtf", "tf"),
                            idf = "prob", iae = "prob",
                            par.idf = NULL, par.iae = NULL) {
@@ -76,17 +83,17 @@ cal_score_init <- function(expr, tf = c("logtf", "tf"),
   tf <- tf(expr, log = (tf == "logtf"))
 
   ## compute idf
-  if(idf == "null") {
+  if (idf == "null") {
     idf <- 1
-  }else {
+  } else {
     idf <- ifelse(idf == "standard", "idf", paste0("idf_", idf))
     idf <- do.call(idf, c(list(expr = expr), par.idf))
   }
 
   ## compute iae
-  if(iae == "null") {
+  if (iae == "null") {
     iae <- 1
-  }else {
+  } else {
     iae <- ifelse(iae == "standard", "iae", paste0("iae_", iae))
     iae <- do.call(iae, c(list(expr = expr), par.iae))
   }