Last updated: 2018-07-19

Code version: 83db76d

Goals

  1. Use cell times derived from fucci and dapi to predict cyclical gene expression patterns

  2. Here we consider that the PC1 and PC2 of fucci and dapi are not of equal variance. We fit an ellipse to the PC1 and PC2 space and project the PC1 and PC2 of each cell to the ellipse.

  3. Results showed that the prediction error is higher than when we predicting cyclical expression patterns just using fucci. However, the top detected cyclical genes are also enriched with cell cycle processes, similarly as the top detected cyclical genes based on cell times derived from fucci only.

  4. These results will be included in the supplemental information.

Top X cyclical genese

library(gplots)
inds <- c("NA19098", "NA18511", "NA18870", "NA19101", "NA18855", "NA19160")
ngenes <- c(1:9, seq(10, 11040, 10))
gene_names <- rownames(readRDS("../data/results/alge_data_training_cyclical_genes.fold.1.rds"))
genes_list <- lapply(1:length(ngenes), function(i) {
  ngene <- ngenes[i]
  tmp <- do.call(cbind, lapply(1:5, function(fold) {
      fl_name <- paste0("../data/results/alge_data_training_cyclical_genes.fold.",
                   fold,".rds")
      df <- readRDS(fl_name)
      top_list <- rownames(df)[order(df$pve,decreasing = T)[1:ngene]]
      rownames(df) %in% top_list
  }) )
  rownames(tmp) <- gene_names
  return(rownames(tmp)[rowSums(tmp)>=4])
})
names(genes_list) <- ngenes
saveRDS(genes_list, 
        file = paste0("../data/results/alge_results_topgenes.rds"))


# mixed individuals
library(gplots)
inds <- c("NA19098", "NA18511", "NA18870", "NA19101", "NA18855", "NA19160")
ngenes <- c(1:9, seq(10, 11040, 10))
for (j in 1:length(inds)) {
  ind <- inds[j]
  gene_names <- rownames(readRDS(paste0("../data/results/alge_ind_",ind,"_data_training_cyclical_genes.fold.", 1,".rds")))
  genes_list <- lapply(1:length(ngenes), function(i) {
    ngene <- ngenes[i]
    tmp <- do.call(cbind, lapply(1:5, function(fold) {
      fl_name <- paste0("../data/results/alge_ind_",ind,"_data_training_cyclical_genes.fold.",
                   fold,".rds")
      df <- readRDS(fl_name)
      top_list <- rownames(df)[order(df$pve,decreasing = T)[1:ngene]]
      rownames(df) %in% top_list
    }) )
    rownames(tmp) <- gene_names
    return(rownames(tmp)[rowSums(tmp)>=4])
  })
  names(genes_list) <- ngenes
  saveRDS(genes_list, 
          file = paste0("../data/results/alge_ind_",ind,"_results_topgenes.rds"))
}


ind_NA19098_topgenes <- readRDS("../data/results/alge_ind_NA19098_results_topgenes.rds")
ind_NA18511_topgenes <- readRDS("../data/results/alge_ind_NA18511_results_topgenes.rds")
ind_NA18870_topgenes <- readRDS("../data/results/alge_ind_NA18870_results_topgenes.rds")
ind_NA19101_topgenes <- readRDS("../data/results/alge_ind_NA19101_results_topgenes.rds")
ind_NA18855_topgenes <- readRDS("../data/results/alge_ind_NA18855_results_topgenes.rds")
ind_NA19160_topgenes <- readRDS("../data/results/alge_ind_NA19160_results_topgenes.rds")

topgenes_list <- list(ind_NA19098_topgenes, ind_NA18511_topgenes, ind_NA18870_topgenes,
                      ind_NA19101_topgenes, ind_NA18855_topgenes, ind_NA19160_topgenes)
library(dplyr)
topgenes_combined <- lapply(1:length(ind_NA19160_topgenes), function(i) {
  ll <- lapply(topgenes_list, "[[", i)
  ll <- unlist(ll)  
  tab <- table(ll)
  names(tab)[which(tab>=4)]
})
names(topgenes_combined) <- names(ind_NA19160_topgenes)

saveRDS(topgenes_combined, file="../data/results/alge_ind_results_topgenes.rds")

Prediction error

summarize results

diff_time_wrapper <- function(results_list) {
  
  methods_list <- sapply(names(results_list),
                         function(x) strsplit(x, split=".", fixed=TRUE)[[1]][2])
  
  diff_time_list <- do.call(rbind, lapply(1:length(results_list), function(i) {
    diff_time <- results_list[[i]]$diff_time
    diff_mean <- mean(diff_time/2/pi)
#    diff_se <- sd(diff_time/2/pi)/sqrt(ncol(results_list[[1]]$Y))

    return(data.frame(diff_mean=diff_mean,
#                      diff_se=diff_se,
                      methods=methods_list[i]))
  }) )
  return(diff_time_list)  
}




library(gplots)
ngenes <- c(2:9, seq(10,200, by=10))
train_randos <- do.call(rbind, lapply(1:length(ngenes), function(i) {
    ngene <- ngenes[i]
    train_topX <- do.call(rbind, lapply(1:5, function(fold) {
    # print(ind)
    print(ngene)
    print(fold)
    fl_name <- paste0("../data/results/triple_alge_results_train.fold.",fold,
                      ".top",ngene,".rds")
    df <- readRDS(fl_name)
    out <- diff_time_wrapper(df$fit.test)
    out$fold <- fold
    return(out)
    }) )
    train_topX$ngenes <- ngene
    #return(train_topX)  
    agg_mn <- aggregate(diff_mean ~ methods,
                      data=train_topX, FUN=mean)
    agg_sd <- aggregate(diff_mean ~ methods,
                      data=train_topX, FUN=sd)
  
    obj <- data.frame(methods=agg_mn$methods, 
                      diff_mean=agg_mn$diff_mean,
                      diff_se=agg_sd$diff_mean/sqrt(5))
    obj$ngenes <- ngene
    return(obj)
  }) )
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saveRDS(train_randos, file = "../output/method-train-alge.Rmd/diff_time_all.rds")




# prediction for one individual
ngenes <- c(2:9, seq(10,200, by=10))
inds <- c("NA19098", "NA18511", "NA18870", "NA19101", "NA18855", "NA19160")
train_top <- lapply(1:length(inds), function(j) {
  ind <- inds[j]
  out <- do.call(rbind, lapply(1:length(ngenes), function(i) {
    ngene <- ngenes[i]
    train_topX <- do.call(rbind, lapply(1:5, function(fold) {
      print(ind)
      print(ngene)
      print(fold)
      fl_name <- paste0("../data/results/triple_alge_ind_",ind,"_results_train.fold.",fold,
                        ".top",ngene,".rds")
      df <- readRDS(fl_name)
      out <- diff_time_wrapper(df$fit.test)
      out$fold <- fold
      return(out)
    }) )
    train_topX$ngenes <- ngene
    #return(train_topX)  
    agg_mn <- aggregate(diff_mean ~ methods,
                      data=train_topX, FUN=mean)
    agg_sd <- aggregate(diff_mean ~ methods,
                      data=train_topX, FUN=sd)
  
    obj <- data.frame(methods=agg_mn$methods, 
                      diff_mean=agg_mn$diff_mean,
                      diff_se=agg_sd$diff_mean/sqrt(5))
    obj$ngenes <- ngene
    return(obj)
  }) )
  out$ind <- ind
  return(out)
})
[1] "NA19098"
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[1] 4
[1] "NA18511"
[1] 10
[1] 5
[1] "NA18511"
[1] 20
[1] 1
[1] "NA18511"
[1] 20
[1] 2
[1] "NA18511"
[1] 20
[1] 3
[1] "NA18511"
[1] 20
[1] 4
[1] "NA18511"
[1] 20
[1] 5
[1] "NA18511"
[1] 30
[1] 1
[1] "NA18511"
[1] 30
[1] 2
[1] "NA18511"
[1] 30
[1] 3
[1] "NA18511"
[1] 30
[1] 4
[1] "NA18511"
[1] 30
[1] 5
[1] "NA18511"
[1] 40
[1] 1
[1] "NA18511"
[1] 40
[1] 2
[1] "NA18511"
[1] 40
[1] 3
[1] "NA18511"
[1] 40
[1] 4
[1] "NA18511"
[1] 40
[1] 5
[1] "NA18511"
[1] 50
[1] 1
[1] "NA18511"
[1] 50
[1] 2
[1] "NA18511"
[1] 50
[1] 3
[1] "NA18511"
[1] 50
[1] 4
[1] "NA18511"
[1] 50
[1] 5
[1] "NA18511"
[1] 60
[1] 1
[1] "NA18511"
[1] 60
[1] 2
[1] "NA18511"
[1] 60
[1] 3
[1] "NA18511"
[1] 60
[1] 4
[1] "NA18511"
[1] 60
[1] 5
[1] "NA18511"
[1] 70
[1] 1
[1] "NA18511"
[1] 70
[1] 2
[1] "NA18511"
[1] 70
[1] 3
[1] "NA18511"
[1] 70
[1] 4
[1] "NA18511"
[1] 70
[1] 5
[1] "NA18511"
[1] 80
[1] 1
[1] "NA18511"
[1] 80
[1] 2
[1] "NA18511"
[1] 80
[1] 3
[1] "NA18511"
[1] 80
[1] 4
[1] "NA18511"
[1] 80
[1] 5
[1] "NA18511"
[1] 90
[1] 1
[1] "NA18511"
[1] 90
[1] 2
[1] "NA18511"
[1] 90
[1] 3
[1] "NA18511"
[1] 90
[1] 4
[1] "NA18511"
[1] 90
[1] 5
[1] "NA18511"
[1] 100
[1] 1
[1] "NA18511"
[1] 100
[1] 2
[1] "NA18511"
[1] 100
[1] 3
[1] "NA18511"
[1] 100
[1] 4
[1] "NA18511"
[1] 100
[1] 5
[1] "NA18511"
[1] 110
[1] 1
[1] "NA18511"
[1] 110
[1] 2
[1] "NA18511"
[1] 110
[1] 3
[1] "NA18511"
[1] 110
[1] 4
[1] "NA18511"
[1] 110
[1] 5
[1] "NA18511"
[1] 120
[1] 1
[1] "NA18511"
[1] 120
[1] 2
[1] "NA18511"
[1] 120
[1] 3
[1] "NA18511"
[1] 120
[1] 4
[1] "NA18511"
[1] 120
[1] 5
[1] "NA18511"
[1] 130
[1] 1
[1] "NA18511"
[1] 130
[1] 2
[1] "NA18511"
[1] 130
[1] 3
[1] "NA18511"
[1] 130
[1] 4
[1] "NA18511"
[1] 130
[1] 5
[1] "NA18511"
[1] 140
[1] 1
[1] "NA18511"
[1] 140
[1] 2
[1] "NA18511"
[1] 140
[1] 3
[1] "NA18511"
[1] 140
[1] 4
[1] "NA18511"
[1] 140
[1] 5
[1] "NA18511"
[1] 150
[1] 1
[1] "NA18511"
[1] 150
[1] 2
[1] "NA18511"
[1] 150
[1] 3
[1] "NA18511"
[1] 150
[1] 4
[1] "NA18511"
[1] 150
[1] 5
[1] "NA18511"
[1] 160
[1] 1
[1] "NA18511"
[1] 160
[1] 2
[1] "NA18511"
[1] 160
[1] 3
[1] "NA18511"
[1] 160
[1] 4
[1] "NA18511"
[1] 160
[1] 5
[1] "NA18511"
[1] 170
[1] 1
[1] "NA18511"
[1] 170
[1] 2
[1] "NA18511"
[1] 170
[1] 3
[1] "NA18511"
[1] 170
[1] 4
[1] "NA18511"
[1] 170
[1] 5
[1] "NA18511"
[1] 180
[1] 1
[1] "NA18511"
[1] 180
[1] 2
[1] "NA18511"
[1] 180
[1] 3
[1] "NA18511"
[1] 180
[1] 4
[1] "NA18511"
[1] 180
[1] 5
[1] "NA18511"
[1] 190
[1] 1
[1] "NA18511"
[1] 190
[1] 2
[1] "NA18511"
[1] 190
[1] 3
[1] "NA18511"
[1] 190
[1] 4
[1] "NA18511"
[1] 190
[1] 5
[1] "NA18511"
[1] 200
[1] 1
[1] "NA18511"
[1] 200
[1] 2
[1] "NA18511"
[1] 200
[1] 3
[1] "NA18511"
[1] 200
[1] 4
[1] "NA18511"
[1] 200
[1] 5
[1] "NA18870"
[1] 2
[1] 1
[1] "NA18870"
[1] 2
[1] 2
[1] "NA18870"
[1] 2
[1] 3
[1] "NA18870"
[1] 2
[1] 4
[1] "NA18870"
[1] 2
[1] 5
[1] "NA18870"
[1] 3
[1] 1
[1] "NA18870"
[1] 3
[1] 2
[1] "NA18870"
[1] 3
[1] 3
[1] "NA18870"
[1] 3
[1] 4
[1] "NA18870"
[1] 3
[1] 5
[1] "NA18870"
[1] 4
[1] 1
[1] "NA18870"
[1] 4
[1] 2
[1] "NA18870"
[1] 4
[1] 3
[1] "NA18870"
[1] 4
[1] 4
[1] "NA18870"
[1] 4
[1] 5
[1] "NA18870"
[1] 5
[1] 1
[1] "NA18870"
[1] 5
[1] 2
[1] "NA18870"
[1] 5
[1] 3
[1] "NA18870"
[1] 5
[1] 4
[1] "NA18870"
[1] 5
[1] 5
[1] "NA18870"
[1] 6
[1] 1
[1] "NA18870"
[1] 6
[1] 2
[1] "NA18870"
[1] 6
[1] 3
[1] "NA18870"
[1] 6
[1] 4
[1] "NA18870"
[1] 6
[1] 5
[1] "NA18870"
[1] 7
[1] 1
[1] "NA18870"
[1] 7
[1] 2
[1] "NA18870"
[1] 7
[1] 3
[1] "NA18870"
[1] 7
[1] 4
[1] "NA18870"
[1] 7
[1] 5
[1] "NA18870"
[1] 8
[1] 1
[1] "NA18870"
[1] 8
[1] 2
[1] "NA18870"
[1] 8
[1] 3
[1] "NA18870"
[1] 8
[1] 4
[1] "NA18870"
[1] 8
[1] 5
[1] "NA18870"
[1] 9
[1] 1
[1] "NA18870"
[1] 9
[1] 2
[1] "NA18870"
[1] 9
[1] 3
[1] "NA18870"
[1] 9
[1] 4
[1] "NA18870"
[1] 9
[1] 5
[1] "NA18870"
[1] 10
[1] 1
[1] "NA18870"
[1] 10
[1] 2
[1] "NA18870"
[1] 10
[1] 3
[1] "NA18870"
[1] 10
[1] 4
[1] "NA18870"
[1] 10
[1] 5
[1] "NA18870"
[1] 20
[1] 1
[1] "NA18870"
[1] 20
[1] 2
[1] "NA18870"
[1] 20
[1] 3
[1] "NA18870"
[1] 20
[1] 4
[1] "NA18870"
[1] 20
[1] 5
[1] "NA18870"
[1] 30
[1] 1
[1] "NA18870"
[1] 30
[1] 2
[1] "NA18870"
[1] 30
[1] 3
[1] "NA18870"
[1] 30
[1] 4
[1] "NA18870"
[1] 30
[1] 5
[1] "NA18870"
[1] 40
[1] 1
[1] "NA18870"
[1] 40
[1] 2
[1] "NA18870"
[1] 40
[1] 3
[1] "NA18870"
[1] 40
[1] 4
[1] "NA18870"
[1] 40
[1] 5
[1] "NA18870"
[1] 50
[1] 1
[1] "NA18870"
[1] 50
[1] 2
[1] "NA18870"
[1] 50
[1] 3
[1] "NA18870"
[1] 50
[1] 4
[1] "NA18870"
[1] 50
[1] 5
[1] "NA18870"
[1] 60
[1] 1
[1] "NA18870"
[1] 60
[1] 2
[1] "NA18870"
[1] 60
[1] 3
[1] "NA18870"
[1] 60
[1] 4
[1] "NA18870"
[1] 60
[1] 5
[1] "NA18870"
[1] 70
[1] 1
[1] "NA18870"
[1] 70
[1] 2
[1] "NA18870"
[1] 70
[1] 3
[1] "NA18870"
[1] 70
[1] 4
[1] "NA18870"
[1] 70
[1] 5
[1] "NA18870"
[1] 80
[1] 1
[1] "NA18870"
[1] 80
[1] 2
[1] "NA18870"
[1] 80
[1] 3
[1] "NA18870"
[1] 80
[1] 4
[1] "NA18870"
[1] 80
[1] 5
[1] "NA18870"
[1] 90
[1] 1
[1] "NA18870"
[1] 90
[1] 2
[1] "NA18870"
[1] 90
[1] 3
[1] "NA18870"
[1] 90
[1] 4
[1] "NA18870"
[1] 90
[1] 5
[1] "NA18870"
[1] 100
[1] 1
[1] "NA18870"
[1] 100
[1] 2
[1] "NA18870"
[1] 100
[1] 3
[1] "NA18870"
[1] 100
[1] 4
[1] "NA18870"
[1] 100
[1] 5
[1] "NA18870"
[1] 110
[1] 1
[1] "NA18870"
[1] 110
[1] 2
[1] "NA18870"
[1] 110
[1] 3
[1] "NA18870"
[1] 110
[1] 4
[1] "NA18870"
[1] 110
[1] 5
[1] "NA18870"
[1] 120
[1] 1
[1] "NA18870"
[1] 120
[1] 2
[1] "NA18870"
[1] 120
[1] 3
[1] "NA18870"
[1] 120
[1] 4
[1] "NA18870"
[1] 120
[1] 5
[1] "NA18870"
[1] 130
[1] 1
[1] "NA18870"
[1] 130
[1] 2
[1] "NA18870"
[1] 130
[1] 3
[1] "NA18870"
[1] 130
[1] 4
[1] "NA18870"
[1] 130
[1] 5
[1] "NA18870"
[1] 140
[1] 1
[1] "NA18870"
[1] 140
[1] 2
[1] "NA18870"
[1] 140
[1] 3
[1] "NA18870"
[1] 140
[1] 4
[1] "NA18870"
[1] 140
[1] 5
[1] "NA18870"
[1] 150
[1] 1
[1] "NA18870"
[1] 150
[1] 2
[1] "NA18870"
[1] 150
[1] 3
[1] "NA18870"
[1] 150
[1] 4
[1] "NA18870"
[1] 150
[1] 5
[1] "NA18870"
[1] 160
[1] 1
[1] "NA18870"
[1] 160
[1] 2
[1] "NA18870"
[1] 160
[1] 3
[1] "NA18870"
[1] 160
[1] 4
[1] "NA18870"
[1] 160
[1] 5
[1] "NA18870"
[1] 170
[1] 1
[1] "NA18870"
[1] 170
[1] 2
[1] "NA18870"
[1] 170
[1] 3
[1] "NA18870"
[1] 170
[1] 4
[1] "NA18870"
[1] 170
[1] 5
[1] "NA18870"
[1] 180
[1] 1
[1] "NA18870"
[1] 180
[1] 2
[1] "NA18870"
[1] 180
[1] 3
[1] "NA18870"
[1] 180
[1] 4
[1] "NA18870"
[1] 180
[1] 5
[1] "NA18870"
[1] 190
[1] 1
[1] "NA18870"
[1] 190
[1] 2
[1] "NA18870"
[1] 190
[1] 3
[1] "NA18870"
[1] 190
[1] 4
[1] "NA18870"
[1] 190
[1] 5
[1] "NA18870"
[1] 200
[1] 1
[1] "NA18870"
[1] 200
[1] 2
[1] "NA18870"
[1] 200
[1] 3
[1] "NA18870"
[1] 200
[1] 4
[1] "NA18870"
[1] 200
[1] 5
[1] "NA19101"
[1] 2
[1] 1
[1] "NA19101"
[1] 2
[1] 2
[1] "NA19101"
[1] 2
[1] 3
[1] "NA19101"
[1] 2
[1] 4
[1] "NA19101"
[1] 2
[1] 5
[1] "NA19101"
[1] 3
[1] 1
[1] "NA19101"
[1] 3
[1] 2
[1] "NA19101"
[1] 3
[1] 3
[1] "NA19101"
[1] 3
[1] 4
[1] "NA19101"
[1] 3
[1] 5
[1] "NA19101"
[1] 4
[1] 1
[1] "NA19101"
[1] 4
[1] 2
[1] "NA19101"
[1] 4
[1] 3
[1] "NA19101"
[1] 4
[1] 4
[1] "NA19101"
[1] 4
[1] 5
[1] "NA19101"
[1] 5
[1] 1
[1] "NA19101"
[1] 5
[1] 2
[1] "NA19101"
[1] 5
[1] 3
[1] "NA19101"
[1] 5
[1] 4
[1] "NA19101"
[1] 5
[1] 5
[1] "NA19101"
[1] 6
[1] 1
[1] "NA19101"
[1] 6
[1] 2
[1] "NA19101"
[1] 6
[1] 3
[1] "NA19101"
[1] 6
[1] 4
[1] "NA19101"
[1] 6
[1] 5
[1] "NA19101"
[1] 7
[1] 1
[1] "NA19101"
[1] 7
[1] 2
[1] "NA19101"
[1] 7
[1] 3
[1] "NA19101"
[1] 7
[1] 4
[1] "NA19101"
[1] 7
[1] 5
[1] "NA19101"
[1] 8
[1] 1
[1] "NA19101"
[1] 8
[1] 2
[1] "NA19101"
[1] 8
[1] 3
[1] "NA19101"
[1] 8
[1] 4
[1] "NA19101"
[1] 8
[1] 5
[1] "NA19101"
[1] 9
[1] 1
[1] "NA19101"
[1] 9
[1] 2
[1] "NA19101"
[1] 9
[1] 3
[1] "NA19101"
[1] 9
[1] 4
[1] "NA19101"
[1] 9
[1] 5
[1] "NA19101"
[1] 10
[1] 1
[1] "NA19101"
[1] 10
[1] 2
[1] "NA19101"
[1] 10
[1] 3
[1] "NA19101"
[1] 10
[1] 4
[1] "NA19101"
[1] 10
[1] 5
[1] "NA19101"
[1] 20
[1] 1
[1] "NA19101"
[1] 20
[1] 2
[1] "NA19101"
[1] 20
[1] 3
[1] "NA19101"
[1] 20
[1] 4
[1] "NA19101"
[1] 20
[1] 5
[1] "NA19101"
[1] 30
[1] 1
[1] "NA19101"
[1] 30
[1] 2
[1] "NA19101"
[1] 30
[1] 3
[1] "NA19101"
[1] 30
[1] 4
[1] "NA19101"
[1] 30
[1] 5
[1] "NA19101"
[1] 40
[1] 1
[1] "NA19101"
[1] 40
[1] 2
[1] "NA19101"
[1] 40
[1] 3
[1] "NA19101"
[1] 40
[1] 4
[1] "NA19101"
[1] 40
[1] 5
[1] "NA19101"
[1] 50
[1] 1
[1] "NA19101"
[1] 50
[1] 2
[1] "NA19101"
[1] 50
[1] 3
[1] "NA19101"
[1] 50
[1] 4
[1] "NA19101"
[1] 50
[1] 5
[1] "NA19101"
[1] 60
[1] 1
[1] "NA19101"
[1] 60
[1] 2
[1] "NA19101"
[1] 60
[1] 3
[1] "NA19101"
[1] 60
[1] 4
[1] "NA19101"
[1] 60
[1] 5
[1] "NA19101"
[1] 70
[1] 1
[1] "NA19101"
[1] 70
[1] 2
[1] "NA19101"
[1] 70
[1] 3
[1] "NA19101"
[1] 70
[1] 4
[1] "NA19101"
[1] 70
[1] 5
[1] "NA19101"
[1] 80
[1] 1
[1] "NA19101"
[1] 80
[1] 2
[1] "NA19101"
[1] 80
[1] 3
[1] "NA19101"
[1] 80
[1] 4
[1] "NA19101"
[1] 80
[1] 5
[1] "NA19101"
[1] 90
[1] 1
[1] "NA19101"
[1] 90
[1] 2
[1] "NA19101"
[1] 90
[1] 3
[1] "NA19101"
[1] 90
[1] 4
[1] "NA19101"
[1] 90
[1] 5
[1] "NA19101"
[1] 100
[1] 1
[1] "NA19101"
[1] 100
[1] 2
[1] "NA19101"
[1] 100
[1] 3
[1] "NA19101"
[1] 100
[1] 4
[1] "NA19101"
[1] 100
[1] 5
[1] "NA19101"
[1] 110
[1] 1
[1] "NA19101"
[1] 110
[1] 2
[1] "NA19101"
[1] 110
[1] 3
[1] "NA19101"
[1] 110
[1] 4
[1] "NA19101"
[1] 110
[1] 5
[1] "NA19101"
[1] 120
[1] 1
[1] "NA19101"
[1] 120
[1] 2
[1] "NA19101"
[1] 120
[1] 3
[1] "NA19101"
[1] 120
[1] 4
[1] "NA19101"
[1] 120
[1] 5
[1] "NA19101"
[1] 130
[1] 1
[1] "NA19101"
[1] 130
[1] 2
[1] "NA19101"
[1] 130
[1] 3
[1] "NA19101"
[1] 130
[1] 4
[1] "NA19101"
[1] 130
[1] 5
[1] "NA19101"
[1] 140
[1] 1
[1] "NA19101"
[1] 140
[1] 2
[1] "NA19101"
[1] 140
[1] 3
[1] "NA19101"
[1] 140
[1] 4
[1] "NA19101"
[1] 140
[1] 5
[1] "NA19101"
[1] 150
[1] 1
[1] "NA19101"
[1] 150
[1] 2
[1] "NA19101"
[1] 150
[1] 3
[1] "NA19101"
[1] 150
[1] 4
[1] "NA19101"
[1] 150
[1] 5
[1] "NA19101"
[1] 160
[1] 1
[1] "NA19101"
[1] 160
[1] 2
[1] "NA19101"
[1] 160
[1] 3
[1] "NA19101"
[1] 160
[1] 4
[1] "NA19101"
[1] 160
[1] 5
[1] "NA19101"
[1] 170
[1] 1
[1] "NA19101"
[1] 170
[1] 2
[1] "NA19101"
[1] 170
[1] 3
[1] "NA19101"
[1] 170
[1] 4
[1] "NA19101"
[1] 170
[1] 5
[1] "NA19101"
[1] 180
[1] 1
[1] "NA19101"
[1] 180
[1] 2
[1] "NA19101"
[1] 180
[1] 3
[1] "NA19101"
[1] 180
[1] 4
[1] "NA19101"
[1] 180
[1] 5
[1] "NA19101"
[1] 190
[1] 1
[1] "NA19101"
[1] 190
[1] 2
[1] "NA19101"
[1] 190
[1] 3
[1] "NA19101"
[1] 190
[1] 4
[1] "NA19101"
[1] 190
[1] 5
[1] "NA19101"
[1] 200
[1] 1
[1] "NA19101"
[1] 200
[1] 2
[1] "NA19101"
[1] 200
[1] 3
[1] "NA19101"
[1] 200
[1] 4
[1] "NA19101"
[1] 200
[1] 5
[1] "NA18855"
[1] 2
[1] 1
[1] "NA18855"
[1] 2
[1] 2
[1] "NA18855"
[1] 2
[1] 3
[1] "NA18855"
[1] 2
[1] 4
[1] "NA18855"
[1] 2
[1] 5
[1] "NA18855"
[1] 3
[1] 1
[1] "NA18855"
[1] 3
[1] 2
[1] "NA18855"
[1] 3
[1] 3
[1] "NA18855"
[1] 3
[1] 4
[1] "NA18855"
[1] 3
[1] 5
[1] "NA18855"
[1] 4
[1] 1
[1] "NA18855"
[1] 4
[1] 2
[1] "NA18855"
[1] 4
[1] 3
[1] "NA18855"
[1] 4
[1] 4
[1] "NA18855"
[1] 4
[1] 5
[1] "NA18855"
[1] 5
[1] 1
[1] "NA18855"
[1] 5
[1] 2
[1] "NA18855"
[1] 5
[1] 3
[1] "NA18855"
[1] 5
[1] 4
[1] "NA18855"
[1] 5
[1] 5
[1] "NA18855"
[1] 6
[1] 1
[1] "NA18855"
[1] 6
[1] 2
[1] "NA18855"
[1] 6
[1] 3
[1] "NA18855"
[1] 6
[1] 4
[1] "NA18855"
[1] 6
[1] 5
[1] "NA18855"
[1] 7
[1] 1
[1] "NA18855"
[1] 7
[1] 2
[1] "NA18855"
[1] 7
[1] 3
[1] "NA18855"
[1] 7
[1] 4
[1] "NA18855"
[1] 7
[1] 5
[1] "NA18855"
[1] 8
[1] 1
[1] "NA18855"
[1] 8
[1] 2
[1] "NA18855"
[1] 8
[1] 3
[1] "NA18855"
[1] 8
[1] 4
[1] "NA18855"
[1] 8
[1] 5
[1] "NA18855"
[1] 9
[1] 1
[1] "NA18855"
[1] 9
[1] 2
[1] "NA18855"
[1] 9
[1] 3
[1] "NA18855"
[1] 9
[1] 4
[1] "NA18855"
[1] 9
[1] 5
[1] "NA18855"
[1] 10
[1] 1
[1] "NA18855"
[1] 10
[1] 2
[1] "NA18855"
[1] 10
[1] 3
[1] "NA18855"
[1] 10
[1] 4
[1] "NA18855"
[1] 10
[1] 5
[1] "NA18855"
[1] 20
[1] 1
[1] "NA18855"
[1] 20
[1] 2
[1] "NA18855"
[1] 20
[1] 3
[1] "NA18855"
[1] 20
[1] 4
[1] "NA18855"
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[1] "NA18855"
[1] 30
[1] 1
[1] "NA18855"
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[1] 2
[1] "NA18855"
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[1] 3
[1] "NA18855"
[1] 30
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 40
[1] 2
[1] "NA18855"
[1] 40
[1] 3
[1] "NA18855"
[1] 40
[1] 4
[1] "NA18855"
[1] 40
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 50
[1] 2
[1] "NA18855"
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[1] 3
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] 2
[1] "NA18855"
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[1] 3
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 80
[1] 2
[1] "NA18855"
[1] 80
[1] 3
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
[1] 100
[1] 1
[1] "NA18855"
[1] 100
[1] 2
[1] "NA18855"
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[1] 3
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 120
[1] 2
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
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[1] 2
[1] "NA18855"
[1] 140
[1] 3
[1] "NA18855"
[1] 140
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 150
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[1] "NA18855"
[1] 150
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[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
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[1] 1
[1] "NA18855"
[1] 160
[1] 2
[1] "NA18855"
[1] 160
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[1] "NA18855"
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[1] 4
[1] "NA18855"
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[1] "NA18855"
[1] 170
[1] 1
[1] "NA18855"
[1] 170
[1] 2
[1] "NA18855"
[1] 170
[1] 3
[1] "NA18855"
[1] 170
[1] 4
[1] "NA18855"
[1] 170
[1] 5
[1] "NA18855"
[1] 180
[1] 1
[1] "NA18855"
[1] 180
[1] 2
[1] "NA18855"
[1] 180
[1] 3
[1] "NA18855"
[1] 180
[1] 4
[1] "NA18855"
[1] 180
[1] 5
[1] "NA18855"
[1] 190
[1] 1
[1] "NA18855"
[1] 190
[1] 2
[1] "NA18855"
[1] 190
[1] 3
[1] "NA18855"
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[1] "NA18855"
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[1] "NA18855"
[1] 200
[1] 1
[1] "NA18855"
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[1] 2
[1] "NA18855"
[1] 200
[1] 3
[1] "NA18855"
[1] 200
[1] 4
[1] "NA18855"
[1] 200
[1] 5
[1] "NA19160"
[1] 2
[1] 1
[1] "NA19160"
[1] 2
[1] 2
[1] "NA19160"
[1] 2
[1] 3
[1] "NA19160"
[1] 2
[1] 4
[1] "NA19160"
[1] 2
[1] 5
[1] "NA19160"
[1] 3
[1] 1
[1] "NA19160"
[1] 3
[1] 2
[1] "NA19160"
[1] 3
[1] 3
[1] "NA19160"
[1] 3
[1] 4
[1] "NA19160"
[1] 3
[1] 5
[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] 5
[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] 5
[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
[1] 8
[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] 5
[1] "NA19160"
[1] 9
[1] 1
[1] "NA19160"
[1] 9
[1] 2
[1] "NA19160"
[1] 9
[1] 3
[1] "NA19160"
[1] 9
[1] 4
[1] "NA19160"
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[1] 5
[1] "NA19160"
[1] 10
[1] 1
[1] "NA19160"
[1] 10
[1] 2
[1] "NA19160"
[1] 10
[1] 3
[1] "NA19160"
[1] 10
[1] 4
[1] "NA19160"
[1] 10
[1] 5
[1] "NA19160"
[1] 20
[1] 1
[1] "NA19160"
[1] 20
[1] 2
[1] "NA19160"
[1] 20
[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] "NA19160"
[1] 30
[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
[1] 30
[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
[1] 40
[1] 5
[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
[1] 80
[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
[1] 80
[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
[1] 100
[1] 1
[1] "NA19160"
[1] 100
[1] 2
[1] "NA19160"
[1] 100
[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
[1] 120
[1] 1
[1] "NA19160"
[1] 120
[1] 2
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] 2
[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] "NA19160"
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[1] 1
[1] "NA19160"
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[1] "NA19160"
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[1] 3
[1] "NA19160"
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[1] 4
[1] "NA19160"
[1] 200
[1] 5
names(train_top) <- inds
saveRDS(train_top, file = "../output/method-train-alge.Rmd/diff_time_all_indprediction.rds")

print results

train_randos <- readRDS("../output/method-train-alge.Rmd/diff_time_all.rds")
train_randos$methods <- as.character(train_randos$methods)
train_randos <- subset(train_randos, methods=="supervised")

train_ind <- readRDS("../output/method-train-alge.Rmd/diff_time_all_indprediction.rds")
train_ind <- do.call(rbind, train_ind)
train_ind$methods <- as.character(train_ind$methods)
train_ind <- subset(train_ind, methods=="supervised")
train_ind$ind <- as.factor(train_ind$ind)


# head(a$fit.test$fit.supervised$cell_times_est)
# head(b$fit.test$fit.supervised$cell_times_est)
# 
# a=readRDS("../data/results/triple_alge_ind_NA18511_data_training_cyclical_genes.fold.1.rds")
# b=readRDS("../data/results/triple_ind_NA18511_data_training_cyclical_genes.fold.1.rds")
# 
# head(a$pve)
# head(b$pve)
# 
# plot(a$pve,b$pve)
# 
# a <- readRDS("../data/results/triple_data_training_alge.rds")
# b <- readRDS("../data/results/triple_data_training.rds")
# plot(a$theta.nonvalid,
#      b$theta.nonvalid)
library(ggplot2)
ggplot(train_randos, 
       aes(x=ngenes, y=diff_mean)) +
  geom_errorbar(train_randos,
                mapping=aes(ymin=diff_mean-diff_se, ymax=diff_mean+diff_se)) +
  geom_point() + geom_line(lty=3) +
  ylab("Error margin (% arc length)") + xlab("Top X cyclical genes") +
  ggtitle("Predict cell times from mixed individuals") 

ggplot(train_ind, 
       aes(x=ngenes, y=diff_mean, col=ind)) +
  geom_errorbar(train_ind,
                mapping=aes(ymin=diff_mean-diff_se, ymax=diff_mean+diff_se,
                            col=ind)) +
  geom_point() + geom_line(lty=3) +
  ylab("Error margin (% arc length)") + xlab("Top X cyclical genes") +
  ggtitle("Predict cell times for one individual")

Output genes

topgenes_randos <- readRDS("../data/results/alge_results_topgenes.rds")
topgenes_ind <- readRDS("../data/results/alge_ind_results_topgenes.rds")

write.table(topgenes_randos[[21]], 
            file = "../output/method-train-alge.Rmd/gene_list.txt",
            row.names = F, quote=F, col.names = F)

write.table(topgenes_ind[[21]], 
            file = "../output/method-train-alge.Rmd/gene_list_ind.txt",
            row.names = F, quote=F, col.names = F)

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] ggplot2_2.2.1 gplots_3.0.1 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17       knitr_1.20         magrittr_1.5      
 [4] munsell_0.4.3      colorspace_1.3-2   rlang_0.1.6       
 [7] stringr_1.2.0      plyr_1.8.4         caTools_1.17.1    
[10] tools_3.4.3        grid_3.4.3         gtable_0.2.0      
[13] KernSmooth_2.23-15 git2r_0.21.0       htmltools_0.3.6   
[16] gtools_3.5.0       lazyeval_0.2.1     yaml_2.1.16       
[19] rprojroot_1.3-2    digest_0.6.15      tibble_1.4.2      
[22] bitops_1.0-6       evaluate_0.10.1    rmarkdown_1.10    
[25] labeling_0.3       gdata_2.18.0       stringi_1.1.6     
[28] pillar_1.1.0       compiler_3.4.3     scales_0.5.0      
[31] backports_1.1.2

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