Merge pull request #15 from jhsiao999/dev

Dev merge

.gitignore

@@ -1,5 +1,4 @@
 .Rhistory
-data-raw/
 .Rproj.user
 peco.Rcheck/
 peco*tar.gz

DESCRIPTION

@@ -14,6 +14,7 @@ Authors@R: c(person("Chiaowen Joyce","Hsiao",email="joyce.hsiao1@gmail.com",
               role="ctb", comment = c(ORCID = "0000-0003-2634-9879")),
        person("Peter","Carbonetto", email = "peter.carbonetto@gmail.com",
               role="ctb", comment = c(ORCID = "0000-0003-1144-6780")))
+Maintainer: Chiaowen Joyce Hsiao <joyce.hsiao1@gmail.com>
 Description: Our approach provides a way to assign continuous cell cycle phase 
     using scRNA-seq data, and consequently, allows to identify cyclic trend 
     of gene expression levels along the cell cycle. This package provides method
@@ -23,16 +24,18 @@ Description: Our approach provides a way to assign continuous cell cycle phase
 URL: https://github.com/jhsiao999/peco
 BugReports: https://github.com/jhsiao999/peco/issues
 License: GPL (>= 3)
-Depends: R (>= 3.2)
+Depends: R (>= 3.5)
 Imports:
     assertthat,
     Biobase,
     circular,
+    conicfit,
     genlasso,
     ggplot2,
     graphics,
     MASS,
     Matrix,
+    parallel,
     stats
 Suggests: 
     knitr,

NAMESPACE

@@ -2,17 +2,15 @@
 
 export(circ_dist)
 export(cycle_npreg_insample)
-export(cycle_npreg_loglik)
-export(cycle_npreg_mstep)
 export(cycle_npreg_outsample)
 export(data_transform_quantile)
 export(fit_bspline)
 export(fit_cyclical_many)
 export(fit_loess)
 export(fit_trendfilter_generic)
-export(initialize_grids)
 export(intensity2circle)
 export(rotation)
+export(shift_origin)
 importFrom(assertthat,assert_that)
 importFrom(circular,coord2rad)
 importFrom(conicfit,AtoG)

R/circ_dist.R

@@ -4,17 +4,29 @@
 #' the differences in both clockwise and counterclockwise directions.
 #'
 #' @param y1 A vector of angles.
-#' 
 #' @param y2 A vector of angles.
 #'
 #' @return A vector of distances between angles.
 #'
-#' @author Joyce Hsiao, Matthew Stephens
+#' @examples
+#' # a vector of angles
+#' theta_ref <- seq(0,2*pi, length.out=100)
+#'
+#' # shift the origin of theta_ref to pi
+#' theta_compare <- shift_origin(theta_ref, origin = pi)
+#' mean(circ_dist(theta_ref, theta_compare))
+#'
+#' # after rotation of angles, difference is 0 between the original
+#' # and the shifted angles
+#' theta_compare_rotated <- rotation(ref_var=theta_ref, shift_var=theta_compare)
+#' mean(circ_dist(theta_ref, theta_compare_rotated))
 #'
+#'
+#' @author Joyce Hsiao, Matthew Stephens
 #' @export
-#' 
+#'
 circ_dist <- function(y1,y2) {
-  pmin(abs(y2-y1), abs(2*pi-(abs(y2-y1))))
+    pmin(abs(y2-y1), abs(2*pi-(abs(y2-y1))))
 }
 
 #' @title Rotate circular variable shift_var to minimize distance
@@ -27,29 +39,42 @@ circ_dist <- function(y1,y2) {
 #' distance between the output value and ref_var.
 #'
 #' @param ref_var A vector of reference angles.
-#' 
 #' @param shift_var A vector of angles to be compared to ref_var.
 #'
 #' @return The transformed values of shift_var after rotation and
 #' shifting.
 #'
-#' @author Matthew Stephens
+#' @examples
+#' # a vector of angles
+#' theta_ref <- seq(0,2*pi, length.out=100)
+#'
+#' # shift the origin of theta_ref to pi
+#' theta_compare <- shift_origin(theta_ref, origin = pi)
+#'
+#' # rotate theta_compare in a such a way that the distance
+#' # between theta_ref and thet_compare is minimized
+#' theta_compare_rotated <- rotation(ref_var=theta_ref, shift_var=theta_compare)
 #'
+#' par(mofrow=c(1,2))
+#' plot(x=theta_ref, y = theta_compare)
+#' plot(x=theta_ref, y = theta_compare_rotated)
+#'
+#' @author Matthew Stephens
 #' @export
-#' 
+#'
 rotation <- function(ref_var,shift_var) {
 
-  df <- data.frame(flip=rep(c(1,-1), each=length(shift_var)),
-                   shift = rep(shift_var, 2))
+    df <- data.frame(flip=rep(c(1,-1), each=length(shift_var)),
+                        shift = rep(shift_var, 2))
 
-  for (i in 1:nrow(df)) {
-    shift_var_tmp <- df$flip[i]*((shift_var+df$shift[i])%%(2*pi))
-    df$dis[i] <- mean(circ_dist(ref_var, shift_var_tmp))
-  }
-  which_cutoff <- which.min(df$dis)
+    for (i in seq_len(nrow(df))) {
+        shift_var_tmp <- df$flip[i]*((shift_var+df$shift[i])%%(2*pi))
+        df$dis[i] <- mean(circ_dist(ref_var, shift_var_tmp))
+    }
+    which_cutoff <- which.min(df$dis)
 
-  shift_var_new <-
-    df$flip[which_cutoff]*((shift_var+df$shift[which_cutoff])%%(2*pi))
+    shift_var_new <-
+      df$flip[which_cutoff]*((shift_var+df$shift[which_cutoff])%%(2*pi))
 
-  return(shift_var_new)
+    return(shift_var_new)
 }