Last updated: 2019-01-03
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Modified: analysis/10x-180504-general-analysis.Rmd
Modified: analysis/10x-180504-mixture-cluster.Rmd
Modified: analysis/10x-180831-general-analysis.Rmd
Deleted: analysis/velocyto_notebook_180504-Copy1.ipynb
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Modified: code/velocyto_preprocess.py
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Modified: plots/180504_pca_tsne.pdf
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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, cbind, colMeans,
colnames, colSums, 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)
seurobj <- readRDS('output/10x-180831')
Genes with high dispersion
cds_high_disp <- readRDS('output/monocle/180831/monocle_high_dispersion/10x-180831-monocle')
plot_cell_trajectory(cds_high_disp, color_by = 'timepoint')
DE genes between T1T2T3 and T4T5
cds_timecombined <- readRDS('output/monocle/180831/monocle_time-combined/10x-180831-monocle')
plot_cell_trajectory(cds_timecombined, color_by='timepoint')
DE genes from cluster resolution 1.5
cds_res1.5 <- readRDS('output/monocle/180831/monocle_res1.5/10x-180831-monocle')
plot_cell_trajectory(cds_res1.5, color_by='timepoint')
Dataset split into T1+T2+T3 and T4+T5, DE genes from clusters res0.5.
cds_split_res0.5 <- readRDS('output/monocle/180831/monocle_T1T2T3_T4T5_res0.5/10x-180831-noreg-monocle')
plot_cell_trajectory(cds_split_res0.5, color_by='timepoint')
Dataset split into T1+T2+T3 and T4+T5, DE genes from clusters res1.5 are used here. This trajectory was used for further analyses.
cds <- readRDS('output/monocle/180831/monocle_T1T2T3_T4T5_res1.5/10x-180831-monocle')
plot_cell_trajectory(cds, color_by='timepoint')
fig <- plot_grid(ncol=1,
plot_cell_trajectory(cds, color_by='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=c("#f67770", "#964B00", "orange"), name = "State"))
fig
plot_cell_trajectory(cds, color_by = "timepoint") + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.1)
plot_cell_trajectory(cds, color_by = "timepoint") + geom_point(color='white', size=5) + geom_point(aes(colour=timepoint), alpha=0.01)
#seurobj <- AddMetaData(seurobj, pData(cds)['State'])
#saveRDS(seurobj, 'output/10x-180831')
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')
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$qval < 0.01]))
[1] "Significant genes with q-val < 0.01: 8360"
paste('Significant genes with q-val < 0.001:', length(BEAM_res$qval[BEAM_res$qval < 0.001]))
[1] "Significant genes with q-val < 0.001: 7216"
paste('Significant genes with q-val < 0.0001:', length(BEAM_res$qval[BEAM_res$qval < 0.0001]))
[1] "Significant genes with q-val < 0.0001: 6421"
paste('Significant genes with q-val < 0.00001:', length(BEAM_res$qval[BEAM_res$qval < 0.00001]))
[1] "Significant genes with q-val < 0.00001: 5860"
paste('Significant genes with q-val = 0:', length(BEAM_res$qval[BEAM_res$qval == 0]))
[1] "Significant genes with q-val = 0: 329"
Histograms of p-values and q-values
hist(BEAM_res$pval)
hist(BEAM_res$qval)
matrix <- as.matrix(seurobj@data)
calculateAvgLogFC <- function(gene){
gene <- as.character(gene)
state2 <- log1p(mean(expm1(as.numeric(matrix[gene, row.names(seurobj@meta.data)[seurobj@meta.data$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)
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 9799
0.1 0.1 1775
0.2 0.2 724
0.3 0.3 372
0.4 0.4 215
0.5 0.5 127
0.6 0.6 72
0.7 0.7 50
0.8 0.8 36
0.9 0.9 28
1 1 20
1.1 1.1 18
1.2 1.2 17
1.3 1.3 10
1.4 1.4 6
1.5 1.5 5
1.6 1.6 5
1.7 1.7 5
1.8 1.8 2
1.9 1.9 1
2 2 1
2.1 2.1 1
2.2 2.2 1
2.3 2.3 0
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_5 <- 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 = F,
# return_heatmap = T,
# branch_labels = c("Cell fate 1 (State 2)", "Cell fate 2 #(State 3)"),
# branch_colors = c('#f67770', '#1bb840', '#649efc')
# )
load('output/monocle/180831/branched')
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.
grid::grid.draw(branched_5$ph_res$gtable)
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$Cluster == i]
print(paste('Nr of genes in cluster ', i, ': ', length(cluster), sep=''))
}
}
print('For logFC 0.3:')
[1] "For logFC 0.3:"
print_nGene(branched_5)
[1] "Total number of genes: 334"
[1] "Nr of genes in cluster 1: 23"
[1] "Nr of genes in cluster 2: 172"
[1] "Nr of genes in cluster 3: 46"
[1] "Nr of genes in cluster 4: 62"
[1] "Nr of genes in cluster 5: 31"
Write BEAM results to files
for (i in 1:length(unique(branched_5$annotation_row$Cluster))){
BEAM_cluster <- BEAM_signficnat_res[BEAM_signficnat_res$gene_short_name %in% row.names(branched_5$annotation_row)[branched_5$annotation_row == i],]
BEAM_cluster <- BEAM_cluster[order(-BEAM_cluster$avgLogFC_State2_State3),]
write.table(BEAM_cluster, paste('tables/BEAM/genelist_cluster_', i, '.txt', sep=''), row.names=F, quote=F, sep='\t')
}
C/EBPa more important in white. C/EBPb and C/EBPd more important in brown (described in several reviews). Weird to see it the other way around in our data.
cds_subset <- cds[row.names(subset(fData(cds), gene_short_name %in% c('EBF2', 'PDGFRA', 'PDGFRB', 'PPARG', 'MALAT1', 'NEAT1', 'PRDM16', 'CEBPA', 'CEBPB', 'CEBPD', 'UCP1', 'LEP'))),]
p2 <- plot_genes_branched_pseudotime(cds_subset, branch_point = 1, color_by = "timepoint", ncol = 2)
p2
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
#save_plot("../plots/180831_monocle_genes-in-pseudotime-2.pdf", p2, base_width=10, base_height=18)
Pseudotime figures report
cds_subset <- cds[row.names(subset(fData(cds), gene_short_name %in% c("IGF2", 'CD36', 'CIDEC', 'PLIN4', 'UCP1', 'UCP2'))),]
p1 <- plot_genes_branched_pseudotime(cds_subset, branch_point = 1, color_by = "timepoint", ncol = 2)
p1
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous y-axis
#save_plot("../plots/180831_monocle_genes-in-pseudotime.pdf", p1, base_width=10, base_height=9)
fig <- plot_grid(
plot_cell_trajectory(cds, color_by='timepoint'),
plot_cell_trajectory(cds, color_by='Pseudotime'),
labels='auto', nrow=1
)
#save_plot("../plots/180831_monocle_timepoint_pseudotime.pdf", fig, base_width=12, base_height=5)
fig
fig2 <- plot_grid(plot_cell_trajectory(cds, color_by='State'), labels=c('d'))
#save_plot("../plots/180831_monocle_state.pdf", fig2, base_width=6, base_height=5)
fig2
grid::grid.draw(branched_5$ph_res$gtable)
#save_plot('../plots/180831_beam_heatmap.pdf', branched_5$ph_res$gtable, base_width=6, base_height=7)
Supplementary figures
sfig <- plot_grid(
plot_cell_trajectory(cds_high_disp, color_by='timepoint'),
plot_cell_trajectory(cds_timecombined, color_by='timepoint'),
plot_cell_trajectory(cds_res1.5, color_by='timepoint'),
plot_cell_trajectory(cds, color_by='timepoint'),
labels='auto', nrow=2
)
#save_plot("../plots/supplementary_figures/sfig_180831_monocle_highdisp_timecombined_res1.5_split-res1.5.pdf", sfig, base_width=12, base_height=10)
sfig
hist(BEAM_res$qval)
#pdf('plots/supplementary_figures/sfig_180831_BEAM_qval_hist.pdf')
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.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_1.0.10 monocle_2.6.4 DDRTree_0.1.5
[4] irlba_2.3.2 VGAM_1.0-6 Biobase_2.38.0
[7] BiocGenerics_0.24.0 Seurat_2.3.4 Matrix_1.2-14
[10] cowplot_0.9.3 ggplot2_3.0.0
loaded via a namespace (and not attached):
[1] Rtsne_0.13 colorspace_1.3-2 class_7.3-14
[4] modeltools_0.2-22 ggridges_0.5.0 mclust_5.4.1
[7] rprojroot_1.3-2 htmlTable_1.12 base64enc_0.1-3
[10] rstudioapi_0.7 proxy_0.4-22 ggrepel_0.8.0
[13] flexmix_2.3-14 bit64_0.9-7 mvtnorm_1.0-8
[16] codetools_0.2-15 R.methodsS3_1.7.1 docopt_0.6
[19] robustbase_0.93-2 knitr_1.20 Formula_1.2-3
[22] jsonlite_1.5 workflowr_1.1.1 ica_1.0-2
[25] cluster_2.0.7-1 kernlab_0.9-27 png_0.1-7
[28] R.oo_1.22.0 compiler_3.4.3 httr_1.3.1
[31] backports_1.1.2 assertthat_0.2.0 lazyeval_0.2.1
[34] limma_3.34.9 lars_1.2 acepack_1.4.1
[37] htmltools_0.3.6 tools_3.4.3 bindrcpp_0.2.2
[40] igraph_1.2.2 gtable_0.2.0 glue_1.3.0
[43] RANN_2.6 reshape2_1.4.3 dplyr_0.7.6
[46] Rcpp_0.12.18 slam_0.1-43 trimcluster_0.1-2.1
[49] gdata_2.18.0 ape_5.1 nlme_3.1-137
[52] iterators_1.0.10 fpc_2.1-11.1 gbRd_0.4-11
[55] lmtest_0.9-36 stringr_1.3.1 gtools_3.8.1
[58] DEoptimR_1.0-8 MASS_7.3-50 zoo_1.8-3
[61] scales_1.0.0 doSNOW_1.0.16 RColorBrewer_1.1-2
[64] yaml_2.2.0 reticulate_1.10 pbapply_1.3-4
[67] gridExtra_2.3 rpart_4.1-13 segmented_0.5-3.0
[70] fastICA_1.2-1 latticeExtra_0.6-28 stringi_1.2.4
[73] foreach_1.4.4 checkmate_1.8.5 caTools_1.17.1.1
[76] densityClust_0.3 bibtex_0.4.2 matrixStats_0.54.0
[79] Rdpack_0.9-0 SDMTools_1.1-221 rlang_0.2.2
[82] pkgconfig_2.0.2 dtw_1.20-1 prabclus_2.2-6
[85] bitops_1.0-6 qlcMatrix_0.9.7 evaluate_0.11
[88] lattice_0.20-35 ROCR_1.0-7 purrr_0.2.5
[91] bindr_0.1.1 labeling_0.3 htmlwidgets_1.2
[94] bit_1.1-14 tidyselect_0.2.4 plyr_1.8.4
[97] magrittr_1.5 R6_2.2.2 snow_0.4-2
[100] gplots_3.0.1 Hmisc_4.1-1 combinat_0.0-8
[103] pillar_1.3.0 whisker_0.3-2 foreign_0.8-71
[106] withr_2.1.2 fitdistrplus_1.0-9 mixtools_1.1.0
[109] survival_2.42-6 nnet_7.3-12 tsne_0.1-3
[112] tibble_1.4.2 crayon_1.3.4 hdf5r_1.0.0
[115] KernSmooth_2.23-15 rmarkdown_1.10 viridis_0.5.1
[118] grid_3.4.3 data.table_1.11.4 FNN_1.1.2.1
[121] git2r_0.23.0 sparsesvd_0.1-4 HSMMSingleCell_0.112.0
[124] metap_1.0 digest_0.6.16 diptest_0.75-7
[127] tidyr_0.8.1 R.utils_2.7.0 munsell_0.5.0
[130] viridisLite_0.3.0
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