Add documentation

R/KNN.graph.R

@@ -6,10 +6,9 @@
 #' @param knn.outlier.th 
 #' @param outlier.frac.th 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 get_knn_graph <- function(rd.dat, cl, ref.cells=row.names(rd.dat),method="Annoy.Cosine", k=15, knn.outlier.th=2, outlier.frac.th=0.5,clean.cells=row.names(rd.dat), knn.result=NULL,mc.cores=10)
 {
   if(is.null(knn.result)){
@@ -78,9 +77,6 @@ get_knn_graph <- function(rd.dat, cl, ref.cells=row.names(rd.dat),method="Annoy.
 #' @param bg.alpha alpha to use for edges that are more faint. Default = 0.1
 #' @param coord_fixed Cartesian coordinates with fixed "aspect ratio". See ggplot::coord_fixed. Default is TRUE
 #'  
-#' 
-#' @example_data:
-#'  
 #' knn.cl.df <- read.csv("data/Constellation_example/knn.cl.df.csv")
 #' cl.center.df <- read.csv("data/Constellation_example/cl.center.df.csv", row.names=1)
 #' 
@@ -747,10 +743,9 @@ plot_constellation <- function (knn.cl.df,
 #' @param line.segments 
 #' @param curved 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 edgeMaker <- function(whichRow, len=100, line.segments, curved=FALSE){
 
   fromC <- unlist(line.segments[whichRow,c(3,4)])# Origin
@@ -788,10 +783,9 @@ edgeMaker <- function(whichRow, len=100, line.segments, curved=FALSE){
 #' @param y 
 #' @param len 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 perpStart <- function(x, y, len) {
     perp(x, y, len, angle(x, y), 1)
         }
@@ -801,10 +795,9 @@ perpStart <- function(x, y, len) {
 #' @param x 
 #' @param y 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 avgangle <- function(x, y) {
     a1 <- angle(x[1:2], y[1:2])
     a2 <- angle(x[2:3], y[2:3])
@@ -819,10 +812,9 @@ avgangle <- function(x, y) {
 #' @param a 
 #' @param mid 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 perp <- function(x, y, len, a, mid) {
     dx <- len*cos(a + pi/2)
     dy <- len*sin(a + pi/2)
@@ -837,10 +829,9 @@ perp <- function(x, y, len, a, mid) {
 #' @param y 
 #' @param len 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 perpMid <- function(x, y, len) {
     ## Now determine angle at midpoint
     perp(x, y, len, avgangle(x, y), 2)
@@ -852,10 +843,9 @@ perpMid <- function(x, y, len) {
 #' @param y 
 #' @param len 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 perpEnd <- function(x, y, len) {
     perp(x, y, len, angle(x, y), 2)
 }
@@ -867,10 +857,9 @@ perpEnd <- function(x, y, len) {
 #' @param x vector
 #' @param y vector
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
+
 angle <- function(x, y) {
     atan2(y[2] - y[1], x[2] - x[1])
         }

R/annotate.R

@@ -524,11 +524,8 @@ compare_annotate <- function(cl,
 #' @return a character vector of hex colors with duplicated colors replaced
 #' 
 #' @export
-#'
 #' @examples
-#' 
 #' original_colors <- c("#00FF00","#00FF00","#FF0000","#00FF00")
-#' 
 #' new_colors <- adjust_color(original_colors)
 #' 
 adjust_color <- function(colorset) {

R/big_util.R

@@ -429,12 +429,12 @@ convert_h5ad_big.dat_parquet <- function(fn, adata=NULL, dir=getwd(),parquet.dir
 ##' .. content for \description{} (no empty lines) ..
 ##'
 ##' .. content for \details{} ..
-##' @title 
+##' @title append_big.dat_parquet
 ##' @param mat 
 ##' @param big.dat 
 ##' @param col.bin.size 
 ##' @param row.bin.size 
-##' @return 
+##' @return value
 ##' @author Zizhen Yao
 append_big.dat_parquet <- function(mat, big.dat, col.bin.size=50000, row.bin.size=500, mc.cores=10)
   {

R/consensusCluster.R

@@ -311,7 +311,7 @@ compile_cl_mat <- function(cl.list, select.cells)
 #' @param niter maxmimal mumber of refinement iterations. 
 #' @param tol.th If improvement is smaller than this threshold, terminate refinement step.  
 #' @param verbose If true, print out step-by-step improvement. 
-#' @return 
+#' @return value
 #' @author Zizhen Yao
 refine_cl <- function(cl, 
                       co.ratio=NULL, 

R/de.genes.R

@@ -94,7 +94,7 @@
 #' \code{min.genes} is the minimum number of differentially expressed genes (passing the \code{padj.th} and \code{lfc.th} thresholds, above) 
 #' required to consider two clusters separate.
 #' 
-#' @return returns a list of parameters for reuse
+#' @return value.  returns a list of parameters for reuse
 #' @export
 #' 
 #' @examples 
@@ -202,7 +202,7 @@ de_param <- function(low.th = 1,
 #' @param y an integer vector with the number of cells in group \emph{y} with detection of each gene.
 #' @param y.total an integer value with the total number of cells in group \emph{y}.
 #' 
-#' @return a data.frame with the following result for each gene:
+#' @return value.  a data.frame with the following result for each gene:
 #' \itemize{
 #' \item{stats: The value of the chi-squared test statistic}
 #' \item{pval: The p-value as reported by pchisq}
@@ -252,7 +252,7 @@ vec_chisq_test <- function(x,
 #' @param fit a limma fit object
 #' @param genes the genes to use for pairwise comparisons
 #' 
-#' @return a data.frame with DE statistics:
+#' @return value.  a data.frame with DE statistics:
 #' \itemize{
 #' \item{padj} P-values adjusted using the Holm (1979) method (\code{p.adjust()} default).
 #' \item{pval} P-values reported by the \code{limma::eBayes()} function.
@@ -317,7 +317,7 @@ de_pair_limma <- function(pair,
 #' @param cl.size a named numeric vector of cluster sizes
 #' @param genes the genes to use for pairwise comparisons
 #'
-#' @return a data.frame with DE statistics:
+#' @return value.  a data.frame with DE statistics:
 #' \itemize{
 #' \item{padj} P-values adjusted using the Holm (1979) method (\code{p.adjust()} default).
 #' \item{pval} P-values reported by the \code{vec_chisq_test()} function.
@@ -366,7 +366,7 @@ de_pair_chisq <- function(pair,
 #' @param cl.size a named numeric vector of cluster sizes
 #' @param genes the genes to use for pairwise comparisons
 #'
-#' @return a data.frame with DE statistics:
+#' @return value.  a data.frame with DE statistics:
 #' \itemize{
 #' \item{padj} P-values adjusted using the Holm (1979) method (\code{p.adjust()} default).
 #' \item{pval} P-values reported by the \code{vec_chisq_test()} function.
@@ -544,10 +544,8 @@ de_pair_fast_limma <- function(pair,
 #' @param direction 
 #' @param include.self 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
 create_pairs <- function(cn1, cn2=cn1,direction="nondirectional", include.self = FALSE)
   {
     cn1=as.character(cn1)
@@ -592,7 +590,7 @@ get_de_truncate_score_sum <- function(gene.score, th=20)
 #' @param cl.size1 Optional: The number of samples in the first/high cluster
 #' @param cl.size2 Optional: The number of samples in the second/low cluster
 #' 
-#' @results A list of filtered differential expression results containing:
+#' @return A list of filtered differential expression results containing:
 #' \itemize{
 #' \item{score} The deScore value, equal to the sum of the -log10(p-values) of differentially expressed genes, with a cap of 20 per gene.
 #' \item{up.score} The deScore value for up-regulated genes.
@@ -705,7 +703,7 @@ de_stats_pair <- function(df,
 #' @param de.df Optional. Pre-computed results from \code{de_all_pairs()} or \code{de_selected_pairs}. Default = NULL.
 #' @param ... Additional parameters passed to \code{de_selected_pairs()}
 #'
-#' @return a character vector of all differentially expressed genes. 
+#' @return value.  a character vector of all differentially expressed genes. 
 #' @export
 #'
 de_all_pairs <- function(norm.dat, 
@@ -752,7 +750,7 @@ de_all_pairs <- function(norm.dat,
 #' @details When directed = TRUE and field = "num", the minimum value from up or down-regulated genes is returned for each pair. When field = "score", the 
 #' minimum deScore is returned.
 #'
-#' @return a matrix with clusters as rows and columns, and pairwise DE results as values.
+#' @return value.  a matrix with clusters as rows and columns, and pairwise DE results as values.
 #' @export
 #'
 get_de_matrix <- function(de.genes, 
@@ -801,10 +799,8 @@ get_de_matrix <- function(de.genes,
 #' @param file 
 #' @param ... 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
 plot_de_num <- function(de.genes, 
                         dend, 
                         cl.label = NULL, 
@@ -850,10 +846,8 @@ plot_de_num <- function(de.genes,
 #' @param select.pair 
 #' @param cl.label 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
 plot_de_lfc_num <- function(de.genes, 
                             top.n = 100, 
                             select.pair = NULL, 
@@ -967,10 +961,8 @@ plot_de_lfc_num <- function(de.genes,
 #' @param cl.label 
 #' @param ... 
 #'
-#' @return
+#' @return value. 
 #' @export
-#'
-#' @examples
 plot_pair_matrix <- function(pair.num, file, directed=FALSE, dend=NULL, col=jet.colors(100), cl.label=NULL,...)
   {
     pair.matrix <- convert_pair_matrix(pair.num, directed = directed)