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library(Seurat)
library(monocle)
library(pheatmap)
library(knitr)
library(gtools)
library(dplyr)
library(kableExtra)
source('code/colors.R')
seurobj <- readRDS('output/seurat_objects/180831/10x-180831')
cds <- readRDS('output/monocle/180831/10x-180831-monocle-monocle_genelist_T1T2T3_T4T5_res.1.5')

Trajectory plots

fig <- plot_grid(ncol=1,
  plot_cell_trajectory(cds, color_by='timepoint') + scale_color_manual(values=colors.timepoints, name = "Timepoint"),
  plot_cell_trajectory(cds, color_by='Pseudotime'),
  plot_cell_trajectory(cds, color_by='State'),
  plot_cell_trajectory(cds, color_by = "State") + scale_color_manual(values=colors.states, name = "State"))

fig

Version Author Date
0abf4f1 Pytrik Folkertsma 2019-05-13
221a47f Pytrik Folkertsma 2019-01-04 
plot_cell_trajectory(cds, color_by='depot') + scale_color_manual(values=colors.depots, name = 'Depots')

Version Author Date
0abf4f1 Pytrik Folkertsma 2019-05-13
221a47f Pytrik Folkertsma 2019-01-04 
plot_cell_trajectory(cds, color_by='type') + scale_color_manual(values=colors.type, name = "Type")

Version Author Date
0abf4f1 Pytrik Folkertsma 2019-05-13
221a47f Pytrik Folkertsma 2019-01-04

Predicted labels by Monocle vs fat type.

#seurobj <- AddMetaData(seurobj, pData(cds)['State'])
p <- plot_grid(
  TSNEPlot(seurobj, group.by='State', pt.size=0.1, colors.use=colors.states, return.plot=T),
  TSNEPlot(seurobj, group.by='type', pt.size=0.1, colors.use=colors.type, return.plot=T),
  labels=c('Predicted by Monocle', 'Type')
)
p

Version Author Date
0abf4f1 Pytrik Folkertsma 2019-05-13
221a47f Pytrik Folkertsma 2019-01-04

Depots and timepoints in Seurat tSNE.

p <- plot_grid(
  TSNEPlot(seurobj, group.by='depot', pt.size=0.1, colors.use=colors.depots, return.plot=T),
  TSNEPlot(seurobj, group.by='timepoint', pt.size=0.1, colors.use=colors.timepoints, return.plot=T),
  ncol=2
)
p

Version Author Date
b064e18 Pytrik Folkertsma 2019-04-03
221a47f Pytrik Folkertsma 2019-01-04

Trajectory transparency.

plot_grid(
  plot_cell_trajectory(cds, color_by = "timepoint") + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.1) + scale_color_manual(values=colors.timepoints),
  plot_cell_trajectory(cds, color_by = "timepoint") + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.01) + scale_color_manual(values=colors.timepoints),
  ncol=2
)

Version Author Date
b064e18 Pytrik Folkertsma 2019-04-03

Pseudotime deciles

TSNEPlot(seurobj, group.by='branch_high_res', colors.use=colors.deciles.gradient, pt.size=0.5)

Version Author Date
b064e18 Pytrik Folkertsma 2019-04-03 
#save_plot('../figures/figures_paper/supplementary_figures/10x-180831_tsne-deciles-pseudotime.pdf', p, base_height=5, base_width=8)

Ratio’s brown/white in branches

Ratio’s white/brown and depots per branch.

get_ratios <- function(col1, col2){
  states <- unique(seurobj@meta.data[,col1])
  values <- unique(seurobj@meta.data[,col2])
  df <- as.data.frame(matrix(ncol=length(values)+1, nrow=length(states)))
  colnames(df) <- c('n', values)
  rownames(df) <- states
  for (state in states){
    n_state = length(which(seurobj@meta.data[col1] == state))
    df[state, 'n'] <- n_state
    #print(paste('N cells', col1, state, ':', n_state))
    for (value in values){
        n_state_value <- length(which(seurobj@meta.data[col1] == state & seurobj@meta.data[col2] == value))
        perc_state_value <- n_state_value / n_state
        df[state, value] <- round(perc_state_value, 2)
        #print(paste('Ratio', value, 'in', state, ': ', round(perc_state_value, 2)))
    }
  }
  return(df)
}
states.depots <- get_ratios('State.labels', 'depot')
depots.states <- get_ratios('depot', 'State.labels')

#write.table(states.depots, '../tables/tables_paper/supplementary_tables/10x-180831-ratios-branches-depots.tsv', sep='\t', quote=F, col.names=NA)
#write.table(depots.states, '../tables/tables_paper/supplementary_tables/10x-180831-ratios-depots-branches.tsv', sep='\t', quote=F, col.names=NA)

states.depots
      n Peri Subq Visce Supra
P 14353 0.23 0.26  0.25  0.26
L  5476 0.17 0.32  0.36  0.16
U  3599 0.39 0.23  0.11  0.27
depots.states
         n    P    L    U
Peri  5599 0.59 0.16 0.25
Subq  6269 0.59 0.28 0.13
Visce 5986 0.60 0.33 0.07
Supra 5574 0.67 0.16 0.18
states.types <- get_ratios('State.labels', 'type')
types.states <- get_ratios('type', 'State.labels')

#write.table(states.types, '../tables/tables_paper/supplementary_tables/10x-180831-ratios-branches-types.tsv', sep='\t', quote=F, col.names=NA)
#write.table(types.states, '../tables/tables_paper/supplementary_tables/10x-180831-ratios-types-branches.tsv', sep='\t', quote=F, col.names=NA)

states.types
      n brown white
P 14353  0.49  0.51
L  5476  0.33  0.67
U  3599  0.66  0.34
types.states
          n    P    L    U
brown 11173 0.63 0.16 0.21
white 12255 0.60 0.30 0.10

BEAM

BEAM takes as input a CellDataSet that’s been ordered with orderCells and the name of a branch point in the trajectory. It returns a table of significance scores for each gene. Genes that score significant are said to be branch-dependent in their expression.

#BEAM_res <- BEAM(cds, branch_point = 1, cores = 10)
load('output/monocle/180831/BEAM_new')
BEAM_res <- BEAM_res[order(BEAM_res$qval),]
BEAM_res <- BEAM_res[,c("gene_short_name", "pval", "qval")]
paste('Significant genes with q-val < 0.05:', length(BEAM_res$qval[BEAM_res$qval < 0.05]))
[1] "Significant genes with q-val < 0.05: 8647"
paste('Significant genes with q-val < 0.01:', length(BEAM_res$qval[BEAM_res$qval < 0.01]))
[1] "Significant genes with q-val < 0.01: 7366"
paste('Significant genes with q-val < 0.001:', length(BEAM_res$qval[BEAM_res$qval < 0.001]))
[1] "Significant genes with q-val < 0.001: 6250"
paste('Significant genes with q-val < 0.0001:', length(BEAM_res$qval[BEAM_res$qval < 0.0001]))
[1] "Significant genes with q-val < 0.0001: 5523"
paste('Significant genes with q-val < 0.00001:', length(BEAM_res$qval[BEAM_res$qval < 0.00001]))
[1] "Significant genes with q-val < 0.00001: 5029"
paste('Significant genes with q-val = 0:', length(BEAM_res$qval[BEAM_res$qval == 0]))
[1] "Significant genes with q-val = 0: 271"

Histograms of p-values and q-values

hist(BEAM_res$pval)

Version Author Date
221a47f Pytrik Folkertsma 2019-01-04 
hist(BEAM_res$qval)

Version Author Date
0abf4f1 Pytrik Folkertsma 2019-05-13
b3cc370 Pytrik Folkertsma 2019-04-04
b064e18 Pytrik Folkertsma 2019-04-03
221a47f Pytrik Folkertsma 2019-01-04

Filtering BEAM results on fold change

matrix <- as.matrix(seurobj@data)
calculateAvgLogFC <- function(gene){
  gene <- as.character(gene)
    state2 <- log1p(mean(expm1(as.numeric(matrix[gene, row.names(seurobj@meta.data)[seurobj@meta.data$State == 2]])))) # first un-log transform. then average. then logp1 again. This is all done to calculate the mean in non-log-space.
  state3 <- log1p(mean(expm1(as.numeric(matrix[gene, row.names(seurobj@meta.data)[seurobj@meta.data$State == 3]]))))
  return(state2-state3)
}

BEAM_signficnat_res <- BEAM_res[BEAM_res$qval < 0.05,]
BEAM_signficnat_res$avgLogFC_State2_State3 <- sapply(BEAM_signficnat_res$gene_short_name, calculateAvgLogFC)

X axis = minimum log fold change.

all <- c()
values <- list()
for (i in seq(0.0, 3, by=0.1)){
  fc <- abs(BEAM_signficnat_res$avgLogFC_State2_State3[abs(BEAM_signficnat_res$avgLogFC_State2_State3) >= i])
  all <- c(all, fc)
  values[toString(i)] <- length(fc)
}
hist(all, breaks=20, probability = F)

Version Author Date
b064e18 Pytrik Folkertsma 2019-04-03 
df <- data.frame(min_fold_change=names(values), num_genes=unlist(values))
rownames(df) <- NULL
kable(df) %>%
  kable_styling(bootstrap_options = "striped", full_width = F)
min_fold_change num_genes
0 8647
0.1 1857
0.2 791
0.3 413
0.4 249
0.5 148
0.6 93
0.7 63
0.8 45
0.9 33
1 27
1.1 20
1.2 18
1.3 16
1.4 11
1.5 6
1.6 5
1.7 5
1.8 5
1.9 2
2 1
2.1 1
2.2 1
2.3 1
2.4 0
2.5 0
2.6 0
2.7 0
2.8 0
2.9 0
3 0

BEAM heatmap

Create heatmap of the significant genes with absolute average logFC > 0.3.

#branched_5_0.3_2 <- plot_genes_branched_heatmap(cds[row.names(subset(BEAM_signficnat_res, abs(avgLogFC_State2_State3) > 0.3))],
#                                         branch_point = 1,
#                                         num_clusters = 5,
#                                         cores = 10,
#                                         show_rownames = T,
#                                       return_heatmap = T,
#                                       branch_labels = c("Upper branch", "Lower branch"),
#                                       branch_colors = colors.states
#                                       )
load('output/monocle/180831/heatmaps')

You can visualize changes for all the genes that are significantly branch dependent using a special type of heatmap. This heatmap shows changes in both lineages at the same time. It also requires that you choose a branch point to inspect. Columns are points in pseudotime, rows are genes, and the beginning of pseudotime is in the middle of the heatmap. As you read from the middle of the heatmap to the right, you are following one lineage through pseudotime. As you read left, the other. The genes are clustered hierarchically, so you can visualize modules of genes that have similar lineage-dependent expression patterns.\ Below heatmaps with different logFC thresholds and nClusters are shown. For the heatmap in the publication figure, genes were filtered on absolute logFC > 0.3 between U and L branch and clustered in 6 groups.

print_nGene <- function(branched){
  print(paste('Total number of genes:', length(branched$annotation_row$Cluster)))
  for (i in 1:length(unique(branched$annotation_row$Cluster))){
    cluster <- rownames(branched$annotation_row)[branched$annotation_row$Cluster == i]
    print(paste('Nr of genes in cluster ', i, ': ', length(cluster), sep=''))
  }
}
for (name in names(heatmaps)){
   cat('\n')  
   cat(paste('BEAM heatmap:', name))
   gridExtra::grid.arrange(heatmaps[[name]]$ph_res$gtable)
   cat(print_nGene(heatmaps[[name]]))
   cat('\n') 
}

BEAM heatmap: heatmap_logFC0.3_ncluster3[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 249” [1] “Nr of genes in cluster 2: 95” [1] “Nr of genes in cluster 3: 69”

BEAM heatmap: heatmap_logFC0.3_ncluster4[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 74” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 95” [1] “Nr of genes in cluster 4: 69”

BEAM heatmap: heatmap_logFC0.3_ncluster5[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 74” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 58” [1] “Nr of genes in cluster 4: 69” [1] “Nr of genes in cluster 5: 37”

BEAM heatmap: heatmap_logFC0.3_ncluster6[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 37” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 58” [1] “Nr of genes in cluster 4: 69” [1] “Nr of genes in cluster 5: 37” [1] “Nr of genes in cluster 6: 37”

BEAM heatmap: heatmap_logFC0.3_ncluster7[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 37” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 58” [1] “Nr of genes in cluster 4: 69” [1] “Nr of genes in cluster 5: 26” [1] “Nr of genes in cluster 6: 11” [1] “Nr of genes in cluster 7: 37”

BEAM heatmap: heatmap_logFC0.3_ncluster8[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 37” [1] “Nr of genes in cluster 2: 45” [1] “Nr of genes in cluster 3: 130” [1] “Nr of genes in cluster 4: 58” [1] “Nr of genes in cluster 5: 69” [1] “Nr of genes in cluster 6: 26” [1] “Nr of genes in cluster 7: 11” [1] “Nr of genes in cluster 8: 37”

BEAM heatmap: heatmap_logFC0.4_ncluster3[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 150” [1] “Nr of genes in cluster 2: 53” [1] “Nr of genes in cluster 3: 46”

BEAM heatmap: heatmap_logFC0.4_ncluster4[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 41” [1] “Nr of genes in cluster 2: 109” [1] “Nr of genes in cluster 3: 53” [1] “Nr of genes in cluster 4: 46”

BEAM heatmap: heatmap_logFC0.4_ncluster5[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 41” [1] “Nr of genes in cluster 2: 109” [1] “Nr of genes in cluster 3: 25” [1] “Nr of genes in cluster 4: 46” [1] “Nr of genes in cluster 5: 28”

BEAM heatmap: heatmap_logFC0.4_ncluster6[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 14” [1] “Nr of genes in cluster 2: 109” [1] “Nr of genes in cluster 3: 25” [1] “Nr of genes in cluster 4: 46” [1] “Nr of genes in cluster 5: 28” [1] “Nr of genes in cluster 6: 27”

BEAM heatmap: heatmap_logFC0.4_ncluster7[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 14” [1] “Nr of genes in cluster 2: 109” [1] “Nr of genes in cluster 3: 25” [1] “Nr of genes in cluster 4: 26” [1] “Nr of genes in cluster 5: 28” [1] “Nr of genes in cluster 6: 20” [1] “Nr of genes in cluster 7: 27”

BEAM heatmap: heatmap_logFC0.4_ncluster8[1] “Total number of genes: 249” [1] “Nr of genes in cluster 1: 14” [1] “Nr of genes in cluster 2: 109” [1] “Nr of genes in cluster 3: 25” [1] “Nr of genes in cluster 4: 26” [1] “Nr of genes in cluster 5: 22” [1] “Nr of genes in cluster 6: 6” [1] “Nr of genes in cluster 7: 20” [1] “Nr of genes in cluster 8: 27”

BEAM heatmap: heatmap_logFC0.5_ncluster3[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 85” [1] “Nr of genes in cluster 2: 35” [1] “Nr of genes in cluster 3: 28”

BEAM heatmap: heatmap_logFC0.5_ncluster4[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 16” [1] “Nr of genes in cluster 2: 69” [1] “Nr of genes in cluster 3: 35” [1] “Nr of genes in cluster 4: 28”

BEAM heatmap: heatmap_logFC0.5_ncluster5[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 16” [1] “Nr of genes in cluster 2: 69” [1] “Nr of genes in cluster 3: 20” [1] “Nr of genes in cluster 4: 28” [1] “Nr of genes in cluster 5: 15”

BEAM heatmap: heatmap_logFC0.5_ncluster6[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 3” [1] “Nr of genes in cluster 2: 69” [1] “Nr of genes in cluster 3: 20” [1] “Nr of genes in cluster 4: 28” [1] “Nr of genes in cluster 5: 15” [1] “Nr of genes in cluster 6: 13”

BEAM heatmap: heatmap_logFC0.5_ncluster7[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 3” [1] “Nr of genes in cluster 2: 69” [1] “Nr of genes in cluster 3: 20” [1] “Nr of genes in cluster 4: 14” [1] “Nr of genes in cluster 5: 15” [1] “Nr of genes in cluster 6: 14” [1] “Nr of genes in cluster 7: 13”

BEAM heatmap: heatmap_logFC0.5_ncluster8[1] “Total number of genes: 148” [1] “Nr of genes in cluster 1: 3” [1] “Nr of genes in cluster 2: 69” [1] “Nr of genes in cluster 3: 20” [1] “Nr of genes in cluster 4: 14” [1] “Nr of genes in cluster 5: 4” [1] “Nr of genes in cluster 6: 14” [1] “Nr of genes in cluster 7: 13” [1] “Nr of genes in cluster 8: 11”

BEAM heatmap: heatmap_logFC0.3_ncluster6_filtered_genes[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 37” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 58” [1] “Nr of genes in cluster 4: 69” [1] “Nr of genes in cluster 5: 37” [1] “Nr of genes in cluster 6: 37”

BEAM heatmap: heatmap_logFC0.3_ncluster6_filtered_genes_OLD[1] “Total number of genes: 413” [1] “Nr of genes in cluster 1: 37” [1] “Nr of genes in cluster 2: 175” [1] “Nr of genes in cluster 3: 58” [1] “Nr of genes in cluster 4: 69” [1] “Nr of genes in cluster 5: 37” [1] “Nr of genes in cluster 6: 37”



sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.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] splines   stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] kableExtra_1.1.0    dplyr_0.8.0.1       gtools_3.8.1       
 [4] knitr_1.22          pheatmap_1.0.12     monocle_2.8.0      
 [7] DDRTree_0.1.5       irlba_2.3.3         VGAM_1.1-1         
[10] Biobase_2.42.0      BiocGenerics_0.28.0 Seurat_2.3.4       
[13] Matrix_1.2-17       cowplot_0.9.4       ggplot2_3.1.0      

loaded via a namespace (and not attached):
  [1] snow_0.4-3             backports_1.1.3        Hmisc_4.2-0           
  [4] workflowr_1.2.0        plyr_1.8.4             igraph_1.2.4          
  [7] lazyeval_0.2.2         densityClust_0.3       fastICA_1.2-1         
 [10] digest_0.6.18          foreach_1.4.4          htmltools_0.3.6       
 [13] viridis_0.5.1          lars_1.2               gdata_2.18.0          
 [16] magrittr_1.5           checkmate_1.9.1        cluster_2.0.7-1       
 [19] mixtools_1.1.0         ROCR_1.0-7             limma_3.36.5          
 [22] readr_1.3.1            matrixStats_0.54.0     R.utils_2.8.0         
 [25] docopt_0.6.1           colorspace_1.4-1       rvest_0.3.3           
 [28] ggrepel_0.8.0          xfun_0.5               sparsesvd_0.1-4       
 [31] crayon_1.3.4           jsonlite_1.6           survival_2.43-3       
 [34] zoo_1.8-5              iterators_1.0.10       ape_5.3               
 [37] glue_1.3.1             gtable_0.3.0           webshot_0.5.1         
 [40] kernlab_0.9-27         prabclus_2.2-7         DEoptimR_1.0-8        
 [43] scales_1.0.0           mvtnorm_1.0-10         bibtex_0.4.2          
 [46] Rcpp_1.0.1             metap_1.1              dtw_1.20-1            
 [49] viridisLite_0.3.0      htmlTable_1.13.1       reticulate_1.11.1     
 [52] foreign_0.8-71         bit_1.1-14             proxy_0.4-23          
 [55] mclust_5.4.3           SDMTools_1.1-221       Formula_1.2-3         
 [58] tsne_0.1-3             htmlwidgets_1.3        httr_1.4.0            
 [61] FNN_1.1.3              gplots_3.0.1.1         RColorBrewer_1.1-2    
 [64] fpc_2.1-11.1           acepack_1.4.1          modeltools_0.2-22     
 [67] ica_1.0-2              pkgconfig_2.0.2        R.methodsS3_1.7.1     
 [70] flexmix_2.3-15         nnet_7.3-12            labeling_0.3          
 [73] tidyselect_0.2.5       rlang_0.3.2            reshape2_1.4.3        
 [76] munsell_0.5.0          tools_3.5.3            ggridges_0.5.1        
 [79] evaluate_0.13          stringr_1.4.0          yaml_2.2.0            
 [82] npsurv_0.4-0           bit64_0.9-7            fs_1.2.7              
 [85] fitdistrplus_1.0-14    robustbase_0.93-4      caTools_1.17.1.2      
 [88] purrr_0.3.2            RANN_2.6.1             pbapply_1.4-0         
 [91] nlme_3.1-137           whisker_0.3-2          slam_0.1-45           
 [94] R.oo_1.22.0            xml2_1.2.0             hdf5r_1.1.1           
 [97] compiler_3.5.3         rstudioapi_0.10        png_0.1-7             
[100] lsei_1.2-0             tibble_2.1.1           stringi_1.4.3         
[103] highr_0.8              lattice_0.20-38        trimcluster_0.1-2.1   
[106] HSMMSingleCell_0.114.0 pillar_1.3.1           combinat_0.0-8        
[109] Rdpack_0.10-1          lmtest_0.9-36          data.table_1.12.0     
[112] bitops_1.0-6           gbRd_0.4-11            R6_2.4.0              
[115] latticeExtra_0.6-28    KernSmooth_2.23-15     gridExtra_2.3         
[118] codetools_0.2-16       MASS_7.3-51.1          assertthat_0.2.1      
[121] rprojroot_1.3-2        withr_2.1.2            qlcMatrix_0.9.7       
[124] hms_0.4.2              diptest_0.75-7         doSNOW_1.0.16         
[127] grid_3.5.3             rpart_4.1-13           tidyr_0.8.3           
[130] class_7.3-15           rmarkdown_1.12         segmented_0.5-3.0     
[133] Rtsne_0.15             git2r_0.25.2           base64enc_0.1-3