Update top_markers function, add hyper parameter for softmax.

R/top_markers.R

@@ -46,13 +46,9 @@ top_markers_init <- function(data, label, n = 10,
 #' calculate group median, MAD or mean score and order genes based on scores
 #'
 #' @inheritParams scale_mgm
-#' @param data matrix, features in row and samples in column
-#' @param n integer, number of returned top genes for each group
+#' @inheritParams top_markers_glm
 #' @param method character, specify metric to compute, can be one of "median",
 #'     "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
 #'
 #' @return a tibble with feature names, group labels and ordered processed scores
 #' @export
@@ -64,7 +60,8 @@ top_markers_abs <- function(data, label, n = 10,
                             pooled.sd = FALSE,
                             method = c("median", "mad", "mean"),
                             scale = TRUE, use.mgm = TRUE,
-                            softmax = TRUE) {
+                            softmax = TRUE,
+                            tau = 1) {
   method <- match.arg(method)
   if (scale && use.mgm) {
     data <- scale_mgm(expr = data, label = label, pooled.sd = pooled.sd)
@@ -91,14 +88,14 @@ top_markers_abs <- function(data, label, n = 10,
       # dplyr::mutate(Scores = Scores / sd(Scores, na.rm = TRUE)) |> # norm by sd
       # dplyr::mutate(Scores = sigmoid(Scores)) |> # sigmoid
       # dplyr::mutate(Scores = tanh(Scores)) |> # tanh
-      dplyr::mutate(Scores = softmax(Scores)) # softmax
+      dplyr::mutate(Scores = softmax(Scores, tau = tau)) # softmax
   }
 
   data <- dplyr::slice_max(data, Scores, n = n) ## extract top n markers
 
   # ## softmax
   # if(softmax == TRUE)
-  #   data <- dplyr::mutate(data, Scores = softmax(Scores))
+  #   data <- dplyr::mutate(data, Scores = softmax(Scores, tau = tau))
 
   return(data)
 }
@@ -112,6 +109,7 @@ top_markers_abs <- function(data, label, n = 10,
 #' @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
+#' @param tau numeric, hyper parameter for softmax
 #'
 #' @return a tibble with feature names, group labels and ordered processed scores
 #' @export
@@ -124,7 +122,8 @@ top_markers_glm <- function(data, label, n = 10,
                             scale = TRUE, use.mgm = TRUE,
                             pooled.sd = FALSE,
                             # log = TRUE,
-                            softmax = TRUE) {
+                            softmax = TRUE,
+                            tau = 1) {
   label <- factor(label) # factorize label
 
   ## scale
@@ -170,14 +169,14 @@ top_markers_glm <- function(data, label, n = 10,
       # dplyr::mutate(Scores = Scores / sd(Scores, na.rm = TRUE)) |> # norm by sd
       # dplyr::mutate(Scores = sigmoid(Scores)) |> # sigmoid
       # dplyr::mutate(Scores = tanh(Scores)) |> # tanh
-      dplyr::mutate(Scores = softmax(Scores)) # softmax
+      dplyr::mutate(Scores = softmax(Scores, tau = tau)) # softmax
   }
 
   data <- dplyr::slice_max(data, Scores, n = n) ## extract top n markers
 
   # ## softmax
   # if(softmax == TRUE)
-  #   data <- dplyr::mutate(data, Scores = softmax(Scores))
+  #   data <- dplyr::mutate(data, Scores = softmax(Scores, tau = tau))
 
   return(data)
 }

vignettes/smartid_Demo.Rmd

@@ -117,7 +117,7 @@ $\mathbf{TF_{i,j}}=\frac{N_{i,j}}{\sum_j{N_{i,j}}},$
 $\mathbf{IDF_i} = \log(1+\frac{n}{n_i+1}),$
 $\mathbf{IAE_i} = \log(1+\frac{n}{\sum_j^n\hat N_{i,j}+1})$
 
-Where $N_{i,j}$ is the counts of feature $i$ in cell $j$; $\hat N_{i,j}$ is $\max(0,N_{i,j}-threshold)$;
+Where $N_{i,j}$ is the counts of feature $i$ in cell $j$; $\hat N_{i,j}$ is $\max(0,N_{i,j}-\mathrm{threshold})$;
 $n$ is the total number of documents(cells); $n_i$ is $\sum_{j = 1}^{n} \mathrm{sign}(N_{i,j} > \mathrm{threshold})$.
 
 Here for labeled data, we can choose logTF * IDF_prob * IAE_prob for marker identification: