Last updated: 2019-12-06

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Knit directory: bentsen-rausch-2019/

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Ignored files:
    Ignored:    .Rproj.user/
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Untracked files:
    Untracked:  analysis/figure_6.Rmd
    Untracked:  analysis/figure_7.Rmd
    Untracked:  analysis/olig_ttest_padj.csv
    Untracked:  analysis/supp1.Rmd
    Untracked:  code/sc_functions.R
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    Untracked:  data/fgf_filtered_nuclei.RDS
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    Untracked:  data/mcaod7.txt
    Untracked:  data/mouse_data/
    Untracked:  data/neur_astro_induce.xlsx
    Untracked:  data/neuron/
    Untracked:  data/synaptic_activity_induced.xlsx
    Untracked:  olig_ttest_padj.csv
    Untracked:  output/agrp_pcgenes.csv
    Untracked:  output/all_wc_markers.csv
    Untracked:  output/allglia_wgcna_genemodules.csv
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    Untracked:  output/fig.RData
    Untracked:  output/fig4_part2.RData
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    Untracked:  output/glial_markergenes.csv
    Untracked:  output/integrated_all_markergenes.csv
    Untracked:  output/integrated_neuronmarkers.csv
    Untracked:  output/neuron/
    Untracked:  wc_de.pdf

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File Version Author Date Message
Rmd e326368 Full Name 2019-12-06 wflow_publish(“analysis/13_olig_pseudotime.Rmd”)
html f4dd96b Full Name 2019-10-29 Build site.
html 3b5cbe7 Full Name 2019-10-28 Build site. 
library(here)
here() starts at /nfsdata/projects/dylan/bentsen-rausch-2019
library(Seurat)
library(monocle)
Loading required package: Matrix
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: ggplot2
Loading required package: VGAM
Loading required package: stats4
Loading required package: splines
Loading required package: DDRTree
Loading required package: irlba
library(ggplot2)
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.3          ✔ purrr   0.3.2     
✔ tidyr   0.8.3          ✔ dplyr   0.8.3     
✔ readr   1.3.1.9000     ✔ stringr 1.4.0     
✔ tibble  2.1.3          ✔ forcats 0.4.0     
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
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library(rstatix)

Attaching package: 'rstatix'
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library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
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    extract
library(ggsci)
library(ggrepel)
library(reshape2)

Attaching package: 'reshape2'
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    smiths
library(cowplot)

********************************************************
Note: As of version 1.0.0, cowplot does not change the
  default ggplot2 theme anymore. To recover the previous
  behavior, execute:
  theme_set(theme_cowplot())
********************************************************

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

    get_legend
library(ggpubr)
olig <- readRDS(here("data/glia/olig_labeled.RDS"))
olig_plot <- as.data.frame(Embeddings(olig, reduction = "umap"))
olig_plot$trt <- olig$trt
olig_plot$type <- Idents(olig)
label.df <- data.frame(cluster=levels(olig_plot$type),label=levels(olig_plot$type))
label.df_2 <- olig_plot %>% 
  dplyr::group_by(type) %>% 
  dplyr::summarize(x = median(UMAP_1), y = median(UMAP_2))

a <- ggplot(olig_plot, aes(UMAP_1, UMAP_2, colour = trt)) + 
  geom_point(alpha = 0.5, size=.5) + scale_color_manual(values=c("#000000","#999999"), name="") +  
  guides(colour = guide_legend(override.aes = list(size=2))) + theme_pubr() + theme(legend.position = c(0.3, 0.25), legend.background=element_blank())
b <- ggplot(olig_plot, aes(UMAP_1, UMAP_2, colour = type)) + 
  geom_point(alpha = 0.5, size=.5) + scale_colour_discrete(name="Treatment") +
  geom_label_repel(data = label.df_2, aes(label = type, x=x, y=y), size=3, fontface="bold", inherit.aes = F) +
  guides(colour = guide_legend(override.aes = list(size=5))) + theme_pubr() + theme(legend.position = "none") 
plot_grid(a,b)

Test difference in cell numbers

cell<-as.data.frame.matrix(table(olig$orig.ident, olig@active.ident))
cell$trt<-as.factor(sapply(strsplit(rownames(cell),"_"),"[",2))
cell<-melt(cell)
stat.test <- cell %>%
  group_by(variable) %>%
  t_test(value ~ trt) %>%
  adjust_pvalue() %>%
  add_significance("p.adj")
Warning: `set_attrs()` is deprecated as of rlang 0.3.0
This warning is displayed once per session.
cell %>% dplyr::group_by(trt, variable) %>% 
  dplyr::summarise(mean=mean(value), sd = sd(value), se = sd/sqrt(length(value))) %>% 
  mutate(signif = stat.test$p.adj.signif) %>% 
  mutate(signif = ifelse(trt == "FGF1", yes = NA, no = signif)) %>% ungroup() -> plotval
write.csv(plotval, file="olig_ttest_padj.csv")

plotval %>% mutate(variable = fct_relevel(variable, c("OPC","COP", "NFOL","MFOL","MOL1"))) %>% 
  mutate(trt = fct_relevel(trt, c("Vehicle","FGF1")))-> plotval 
Warning: Unknown levels in `f`: Vehicle
ggplot(plotval, aes(x = variable, y = mean, fill = trt)) + 
    geom_bar(position=position_dodge(), stat="identity") + 
    geom_errorbar(aes(ymin=mean-se, ymax=mean+se),size=.3,width=.2,position=position_dodge(.9)) +
    xlab(NULL) + scale_fill_manual(values=c("gray80","gray30")) +
    ylab("Number of cells") +
    ggpubr::theme_pubr(legend = "none") +
    theme(axis.text.x = element_text(angle=45, hjust=1)) + 
    geom_signif(y_position=c(plotval %>% dplyr::group_by(variable) %>% dplyr::summarise(max = max(mean)) %>% pull(max) + 50), 
                xmin = c(seq(0.9,4.9, by = 1)), xmax=c(seq(1.1,5.1, by = 1)),
                annotation=c(plotval %>% slice(6:10) %>% pull(signif) %>% as.character() %>% toupper())[c(1,4,5,3,2)], 
                tip_length=0, size = 0.5, textsize = 3, color="black", vjust = -1) + coord_cartesian(clip="off") -> oligttest
oligttest

Prep data for pseudotime analysis

cds <- as.CellDataSet(olig)
cds <- estimateSizeFactors(cds)
cds <- estimateDispersions(cds)
cds <- detectGenes(cds, min_expr = 0.1)
fData(cds)$use_for_ordering <-
    fData(cds)$num_cells_expressed > 0.1 * ncol(cds)

cds <- reduceDimension(cds,
                              max_components = 2,
                              norm_method = 'log',
                              num_dim = 2,
                              reduction_method = 'tSNE',
                              verbose = T)
cds <- clusterCells(cds, verbose = T)
Distance cutoff calculated to 7.253532 
cds <- clusterCells(cds,
                 rho_threshold = 150,
                 delta_threshold = 15,
                 skip_rho_sigma = T,
                 verbose = F)
plot_cell_clusters(cds, label_groups_by_cluster=FALSE,  color_cells_by = "Cluster")

Version Author Date
f4dd96b Full Name 2019-10-29

Run monocle

olig_expressed_genes <-  row.names(subset(fData(cds), num_cells_expressed >= 10))

clustering_DEG_genes <-
    differentialGeneTest(cds[olig_expressed_genes,],
          fullModelFormulaStr = '~predicted.id',
          cores = 10)

olig_ordering_genes <-
    row.names(clustering_DEG_genes)[order(clustering_DEG_genes$qval)][1:500]

cds <-
    setOrderingFilter(cds,
        ordering_genes = olig_ordering_genes)

cds <-
    reduceDimension(cds, method = 'DDRTree')

cds <-
    orderCells(cds)

cds <-
    orderCells(cds, root_state = 2)

olig$pseudo <- cds$Pseudotime
plot_cell_trajectory(cds,color_by = "predicted.id")

ggsave(here("data/figures/supp/monocle_by_celltype.pdf"))
plot_cell_trajectory(cds, markers = "Gpr17")

ggsave(here("data/figures/supp/monocle_by_gpr17.pdf"))
plot_cell_trajectory(cds, color_by = "Pseudotime")

ggsave(here("data/figures/supp/monocle_by_pseudo.pdf"))
plot_cell_trajectory(cds, color_by = "trt", alpha=0.5)

ggsave(here("data/figures/supp/monocle_by_trt.pdf"))

Plot pseudotime on umap plots

olig_plot$pseudo <- olig$pseudo
olig_plot$gpr17 <- as.numeric(olig@assays[["SCT"]]@data["Gpr17",])

ggplot(olig_plot, aes(UMAP_1, UMAP_2, colour = pseudo)) + 
  geom_point(alpha = 0.5, size=.5) + ggsci::scale_color_material(name="Pseudotime", guide = guide_colorbar(title.position = "top"), palette = "blue-grey") +
  ggpubr::theme_pubr() + xlab(NULL) + ylab(NULL) +
  theme(legend.position = c(0.3,0.25), legend.direction =  "horizontal",legend.title = element_text(hjust=0.5), legend.background = element_blank()) -> olig_pseudo

ggplot(olig_plot, aes(UMAP_1, UMAP_2, color = gpr17)) + 
  geom_point(alpha = 0.75, size=.5) + ggsci::scale_color_material(name="Gpr17 Expression", palette = "deep-orange",
                                                                 guide = guide_colorbar(title.position = "top")) +
  ggpubr::theme_pubr() + xlab(NULL) + ylab(NULL) +
  theme(legend.position = c(0.3,0.25), legend.direction =  "horizontal",legend.title = element_text(hjust=0.5), legend.background = element_blank()) -> olig_gpr17

sc_olig <- cowplot::plot_grid(b, addSmallLegend(olig_pseudo), oligttest, addSmallLegend(olig_gpr17), ncol=2, labels="auto", scale=0.9, align="hv")
sc_olig

Version Author Date
f4dd96b Full Name 2019-10-29 
readxl::read_xlsx(path = here("data/mouse_data/fig6/191118_Gpr17.xlsx"), range="A4:B10") %>%
  reshape2::melt() %>% na.omit() -> gpr17

gpr17 %>% dplyr::group_by(variable) %>% dplyr::summarise(mean=mean(value), sd = sd(value), se = sd/sqrt(length(value))) %>%
  ggplot(aes(x=variable, y=mean, fill=variable, color=variable)) + 
  geom_col(width=1, alpha=0.75, colour="black", position="dodge") +
  geom_errorbar(aes(x=variable, ymin = mean-se, ymax=mean+se), width=0.2, position=position_dodge(.9), size=1) +
  geom_jitter(data = gpr17, inherit.aes = F, aes(x=variable, y=value, fill=variable), 
              alpha=0.5, shape=21, position = position_jitterdodge(.5)) + xlab(NULL) +
  #geom_text(position = position_dodge2(width=.9, preserve="single"), aes(y=value+se+1), face = "bold", size=8) + 
  ylab("GPR17+ Cells Per mm²") + scale_fill_manual("Treatment", values=c("gray80","gray30")) + 
  scale_color_manual("Treatment", values=c("gray80","gray30"))+ theme_classic() +
  theme(legend.position = "none", legend.background = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank()) + 
  geom_signif(y_position=max(gpr17$value), xmin=1.2, xmax=1.8,
              annotation=c("*"), tip_length=0, size = 0.5, textsize = 6, color="black") + coord_cartesian(clip="off") -> gpr17_bp

olig_val <- cowplot::plot_grid(ggplot() + theme_void(), gpr17_bp, nrow=1, scale=0.9, labels=c("e","f"), rel_widths = c(2,1))
cowplot::plot_grid(sc_olig, olig_val, ncol=1, align="hv", rel_heights = c(1.75,1))

Version Author Date
3b5cbe7 Full Name 2019-10-28 
#ggsave("figure_6.tiff", width=12, h=8, dpi=600, compression = "lzw")

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_DK.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_DK.UTF-8        LC_COLLATE=en_DK.UTF-8    
 [5] LC_MONETARY=en_DK.UTF-8    LC_MESSAGES=en_DK.UTF-8   
 [7] LC_PAPER=en_DK.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] splines   stats4    parallel  stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] here_0.1            cowplot_1.0.0       reshape2_1.4.3     
 [4] ggrepel_0.8.0.9000  ggsci_2.9           ggpubr_0.2.1       
 [7] magrittr_1.5        rstatix_0.1.1       forcats_0.4.0      
[10] stringr_1.4.0       dplyr_0.8.3         purrr_0.3.2        
[13] readr_1.3.1.9000    tidyr_0.8.3         tibble_2.1.3       
[16] tidyverse_1.2.1     monocle_2.10.1      DDRTree_0.1.5      
[19] irlba_2.3.3         VGAM_1.1-1          ggplot2_3.2.1      
[22] Biobase_2.42.0      BiocGenerics_0.28.0 Matrix_1.2-17      
[25] Seurat_3.0.3.9036  

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.1.4      workflowr_1.4.0     
  [4] plyr_1.8.4           igraph_1.2.4.1       lazyeval_0.2.2      
  [7] densityClust_0.3     listenv_0.7.0        fastICA_1.2-1       
 [10] digest_0.6.20        htmltools_0.3.6      viridis_0.5.1       
 [13] gdata_2.18.0         cluster_2.1.0        ROCR_1.0-7          
 [16] openxlsx_4.1.0.1     limma_3.38.3         globals_0.12.4      
 [19] modelr_0.1.4         RcppParallel_4.4.3   matrixStats_0.54.0  
 [22] R.utils_2.9.0        docopt_0.6.1         colorspace_1.4-1    
 [25] rvest_0.3.4          haven_2.1.0          xfun_0.8            
 [28] sparsesvd_0.1-4      crayon_1.3.4         jsonlite_1.6        
 [31] zeallot_0.1.0        survival_2.44-1.1    zoo_1.8-6           
 [34] ape_5.3              glue_1.3.1           gtable_0.3.0        
 [37] leiden_0.3.1         car_3.0-3            future.apply_1.3.0  
 [40] abind_1.4-5          scales_1.0.0         pheatmap_1.0.12     
 [43] bibtex_0.4.2         Rcpp_1.0.2           metap_1.1           
 [46] viridisLite_0.3.0    reticulate_1.13      proxy_0.4-23        
 [49] foreign_0.8-71       rsvd_1.0.2           SDMTools_1.1-221.1  
 [52] tsne_0.1-3           htmlwidgets_1.3      httr_1.4.1          
 [55] FNN_1.1.3            gplots_3.0.1.1       RColorBrewer_1.1-2  
 [58] ica_1.0-2            pkgconfig_2.0.2      R.methodsS3_1.7.1   
 [61] uwot_0.1.3           labeling_0.3         tidyselect_0.2.5    
 [64] rlang_0.4.0          munsell_0.5.0        cellranger_1.1.0    
 [67] tools_3.5.3          cli_1.1.0            generics_0.0.2      
 [70] broom_0.5.2          ggridges_0.5.1       evaluate_0.14       
 [73] yaml_2.2.0           npsurv_0.4-0         knitr_1.23          
 [76] fs_1.3.1             fitdistrplus_1.0-14  zip_2.0.3           
 [79] caTools_1.17.1.2     RANN_2.6.1           pbapply_1.4-1       
 [82] future_1.14.0        nlme_3.1-140         whisker_0.3-2       
 [85] slam_0.1-45          R.oo_1.22.0          xml2_1.2.0          
 [88] compiler_3.5.3       rstudioapi_0.10      curl_4.0            
 [91] plotly_4.9.0         png_0.1-7            ggsignif_0.5.0      
 [94] lsei_1.2-0           stringi_1.4.3        highr_0.8           
 [97] lattice_0.20-38      HSMMSingleCell_1.2.0 vctrs_0.2.0         
[100] pillar_1.4.2         combinat_0.0-8       Rdpack_0.11-0       
[103] lmtest_0.9-37        RcppAnnoy_0.0.12     data.table_1.12.2   
[106] bitops_1.0-6         gbRd_0.4-11          R6_2.4.0            
[109] rio_0.5.16           KernSmooth_2.23-15   gridExtra_2.3       
[112] codetools_0.2-16     MASS_7.3-51.4        gtools_3.8.1        
[115] assertthat_0.2.1     rprojroot_1.3-2      withr_2.1.2         
[118] qlcMatrix_0.9.7      sctransform_0.2.0    hms_0.5.0           
[121] grid_3.5.3           rmarkdown_1.13       carData_3.0-2       
[124] Rtsne_0.15           git2r_0.25.2         lubridate_1.7.4     
[127] rematch_1.0.1