Last updated: 2019-04-03
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Knit directory: 10x-adipocyte-analysis/
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library(Seurat)
Loading required package: ggplot2
Loading required package: cowplot
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
Loading required package: Matrix
library(monocle)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:Matrix':
colMeans, colSums, rowMeans, rowSums, which
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind,
colMeans, colnames, colSums, dirname, do.call, duplicated,
eval, evalq, Filter, Find, get, grep, grepl, intersect,
is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which, which.max,
which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines
Loading required package: DDRTree
Loading required package: irlba
library(pheatmap)
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')
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
plot_cell_trajectory(cds, color_by='depot') + scale_color_manual(values=colors.depots, name = 'Depots')
plot_cell_trajectory(cds, color_by='type') + scale_color_manual(values=colors.type, name = "Type")
#seurobj <- AddMetaData(seurobj, pData(cds)['State'])
plot_grid(
TSNEPlot(seurobj, group.by='State', pt.size=0.1, colors.use=colors.states),
TSNEPlot(seurobj, group.by='type', pt.size=0.1, colors.use=colors.type),
labels=c('Predicted by Monocle', 'True labels')
)
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
plot_grid(
TSNEPlot(seurobj, group.by='depot', pt.size=0.1, colors.use=colors.depots),
TSNEPlot(seurobj, group.by='timepoint', pt.size=0.1, colors.use=colors.timepoints),
ncol=2
)
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
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 |
#seurobj <- AddMetaData(seurobj, pData(cds)['State'])
#saveRDS(seurobj, 'output/10x-180831')
#save metadata for Velocyto
#write.table(seurobj@meta.data, file='tables/10x-180831-metadata-labels.txt', sep='\t', quote=F)
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)
}
get_ratios('State.labels', 'depot')
n Peri Subq Visce Supra
Progenitor branch 14353 0.23 0.26 0.25 0.26
Lower branch 5476 0.17 0.32 0.36 0.16
Upper branch 3599 0.39 0.23 0.11 0.27
get_ratios('depot', 'State.labels')
n Progenitor branch Lower branch Upper branch
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
get_ratios('State.labels', 'type')
n brown white
Progenitor branch 14353 0.49 0.51
Lower branch 5476 0.33 0.67
Upper branch 3599 0.66 0.34
get_ratios('type', 'State.labels')
n Progenitor branch Lower branch Upper branch
brown 11173 0.63 0.16 0.21
white 12255 0.60 0.30 0.10
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$qval),]
BEAM_res <- BEAM_res[,c("gene_short_name", "pval", "qval")]
paste('Significant genes with q-val < 0.01:', length(BEAM_res$qval[BEAM_res$qval < 0.05]))
[1] "Significant genes with q-val < 0.01: 8647"
paste('Significant genes with q-val < 0.01:', length(BEAM_res$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)
hist(BEAM_res$qval)
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
matrix <- as.matrix(seurobj@data)
calculateAvgLogFC <- function(gene){
gene <- as.character(gene)
state2 <- log1p(mean(expm1(as.numeric(matrix[gene, row.names(seurobj@meta.data)[seurobj@meta.data$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 |
hist(all, breaks=20, probability = T)
lines(density(all), col='blue', lwd=2)
data.frame(fold_change=names(values), num_genes=unlist(values))
fold_change num_genes
0 0 8647
0.1 0.1 1857
0.2 0.2 791
0.3 0.3 413
0.4 0.4 249
0.5 0.5 148
0.6 0.6 93
0.7 0.7 63
0.8 0.8 45
0.9 0.9 33
1 1 27
1.1 1.1 20
1.2 1.2 18
1.3 1.3 16
1.4 1.4 11
1.5 1.5 6
1.6 1.6 5
1.7 1.7 5
1.8 1.8 5
1.9 1.9 2
2 2 1
2.1 2.1 1
2.2 2.2 1
2.3 2.3 1
2.4 2.4 0
2.5 2.5 0
2.6 2.6 0
2.7 2.7 0
2.8 2.8 0
2.9 2.9 0
3 3 0
Create heatmap of the significant genes with absolute average logFC > 0.3.
#ran in terminal because of computation time
branched_3_0.3 <- plot_genes_branched_heatmap(cds[row.names(subset(BEAM_signficnat_res, abs(avgLogFC_State2_State3) > 0.3))],
branch_point = 1,
num_clusters = 3,
cores = 10,
show_rownames = T,
return_heatmap = T,
branch_labels = c("Upper branch", "Lower branch"),
branch_colors = colors.states
)
load('output/monocle/180831/heatmap')
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.\
3 clusters, logFC > 0.5:
gridExtra::grid.arrange(branched_3_0.5$ph_res$gtable)
5 clusters, genes with logFC > 0.4 between the two branches.
gridExtra::grid.arrange(branched_5_0.4$ph_res$gtable)
Version | Author | Date |
---|---|---|
b064e18 | Pytrik Folkertsma | 2019-04-03 |
3 clusters, genes with logFC > 0.3.
gridExtra::grid.arrange(branched_3_0.3$ph_res$gtable)
Version | Author | Date |
---|---|---|
4d92211 | Pytrik Folkertsma | 2019-04-03 |
4 clusters, genes with logFC > 0.3.
gridExtra::grid.arrange(branched_4_0.3$ph_res$gtable)
Version | Author | Date |
---|---|---|
4d92211 | Pytrik Folkertsma | 2019-04-03 |
5 clusters, genes with logFC > 0.3.
gridExtra::grid.arrange(branched_5_0.3$ph_res$gtable)
Version | Author | Date |
---|---|---|
4d92211 | Pytrik Folkertsma | 2019-04-03 |
6 clusters, genes with logFC > 0.3 between the two branches.
gridExtra::grid.arrange(branched_6_0.3$ph_res$gtable)
Version | Author | Date |
---|---|---|
4d92211 | Pytrik Folkertsma | 2019-04-03 |
7 clusters, genes with logFC > 0.3 between the two branches.
gridExtra::grid.arrange(branched_7_0.3$ph_res$gtable)
8 clusters, genes with logFC > 0.3 between the two branches.
gridExtra::grid.arrange(branched_8_0.3$ph_res$gtable)
Write BEAM results to files
#for (i in 1:length(unique(branched_6_0.3$annotation_row$Cluster))){
# BEAM_cluster <- BEAM_signficnat_res[BEAM_signficnat_res$gene_short_name %in% row.names(branched_6_0.3$annotation_row)[branched_6_0.3$annotation_row == i],]
# BEAM_cluster <- BEAM_cluster[order(-BEAM_cluster$avgLogFC_State2_State3),]
# write.table(BEAM_cluster, paste('../tables/BEAM/BEAM_6clusters/genelist_cluster_', i, #'.txt', sep=''), row.names=F, quote=F, sep='\t')
#}
Nr of genes
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=''))
}
}
print('For logFC 0.3:')
[1] "For logFC 0.3:"
print_nGene(branched_5_0.3)
[1] "Total number of genes: 334"
[1] "Nr of genes in cluster 1: 22"
[1] "Nr of genes in cluster 2: 138"
[1] "Nr of genes in cluster 3: 74"
[1] "Nr of genes in cluster 4: 64"
[1] "Nr of genes in cluster 5: 36"
print('For logFC 0.4:')
[1] "For logFC 0.4:"
print_nGene(branched_5_0.4)
[1] "Total number of genes: 184"
[1] "Nr of genes in cluster 1: 24"
[1] "Nr of genes in cluster 2: 83"
[1] "Nr of genes in cluster 3: 21"
[1] "Nr of genes in cluster 4: 25"
[1] "Nr of genes in cluster 5: 31"
print('For logFC 0.5:')
[1] "For logFC 0.5:"
print_nGene(branched_5_0.5)
[1] "Total number of genes: 102"
[1] "Nr of genes in cluster 1: 11"
[1] "Nr of genes in cluster 2: 14"
[1] "Nr of genes in cluster 3: 19"
[1] "Nr of genes in cluster 4: 8"
[1] "Nr of genes in cluster 5: 50"
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] pheatmap_1.0.12 monocle_2.8.0 DDRTree_0.1.5
[4] irlba_2.3.3 VGAM_1.1-1 Biobase_2.42.0
[7] BiocGenerics_0.28.0 Seurat_2.3.4 Matrix_1.2-17
[10] 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] matrixStats_0.54.0 R.utils_2.8.0 docopt_0.6.1
[25] colorspace_1.4-1 ggrepel_0.8.0 xfun_0.5
[28] dplyr_0.8.0.1 sparsesvd_0.1-4 crayon_1.3.4
[31] jsonlite_1.6 survival_2.43-3 zoo_1.8-5
[34] iterators_1.0.10 ape_5.3 glue_1.3.1
[37] gtable_0.3.0 kernlab_0.9-27 prabclus_2.2-7
[40] DEoptimR_1.0-8 scales_1.0.0 mvtnorm_1.0-10
[43] bibtex_0.4.2 Rcpp_1.0.1 metap_1.1
[46] dtw_1.20-1 viridisLite_0.3.0 htmlTable_1.13.1
[49] reticulate_1.11.1 foreign_0.8-71 bit_1.1-14
[52] proxy_0.4-23 mclust_5.4.3 SDMTools_1.1-221
[55] Formula_1.2-3 tsne_0.1-3 htmlwidgets_1.3
[58] httr_1.4.0 FNN_1.1.3 gplots_3.0.1.1
[61] RColorBrewer_1.1-2 fpc_2.1-11.1 acepack_1.4.1
[64] modeltools_0.2-22 ica_1.0-2 pkgconfig_2.0.2
[67] R.methodsS3_1.7.1 flexmix_2.3-15 nnet_7.3-12
[70] tidyselect_0.2.5 labeling_0.3 rlang_0.3.2
[73] reshape2_1.4.3 munsell_0.5.0 tools_3.5.3
[76] ggridges_0.5.1 evaluate_0.13 stringr_1.4.0
[79] yaml_2.2.0 npsurv_0.4-0 knitr_1.22
[82] bit64_0.9-7 fs_1.2.7 fitdistrplus_1.0-14
[85] robustbase_0.93-4 caTools_1.17.1.2 purrr_0.3.2
[88] RANN_2.6.1 pbapply_1.4-0 nlme_3.1-137
[91] whisker_0.3-2 slam_0.1-45 R.oo_1.22.0
[94] hdf5r_1.1.1 compiler_3.5.3 rstudioapi_0.10
[97] png_0.1-7 lsei_1.2-0 tibble_2.1.1
[100] stringi_1.4.3 lattice_0.20-38 trimcluster_0.1-2.1
[103] HSMMSingleCell_0.114.0 pillar_1.3.1 combinat_0.0-8
[106] Rdpack_0.10-1 lmtest_0.9-36 data.table_1.12.0
[109] bitops_1.0-6 gbRd_0.4-11 R6_2.4.0
[112] latticeExtra_0.6-28 KernSmooth_2.23-15 gridExtra_2.3
[115] codetools_0.2-16 MASS_7.3-51.1 gtools_3.8.1
[118] assertthat_0.2.1 rprojroot_1.3-2 withr_2.1.2
[121] qlcMatrix_0.9.7 diptest_0.75-7 doSNOW_1.0.16
[124] grid_3.5.3 rpart_4.1-13 tidyr_0.8.3
[127] class_7.3-15 rmarkdown_1.12 segmented_0.5-3.0
[130] Rtsne_0.15 git2r_0.25.2 base64enc_0.1-3