Olig Pseudotime

Last updated: 2019-10-28

Checks: 6 1

Knit directory: fgf_alldata/

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library(here)

here() starts at /nfsdata/projects/dylan/fgf_alldata

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() ──
✖ dplyr::combine()    masks Biobase::combine(), BiocGenerics::combine()
✖ tidyr::expand()     masks Matrix::expand()
✖ tidyr::fill()       masks VGAM::fill()
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::lag()        masks stats::lag()
✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()

library(rstatix)


Attaching package: 'rstatix'

The following object is masked from 'package:stats':

    filter

library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

library(ggsci)
library(ggrepel)
library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    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")

summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)

    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )

    # Rename the "mean" column    
    names(datac)[4] <- measurevar

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}

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<-summarySE(cell, measurevar="value", groupvars=c("trt","variable"))
plotval<-cbind(cell, signif=stat.test$p.adj.signif)
plotval$signif[plotval$trt!="FGF1"]<-NA
write.csv(plotval, file="olig_ttest_padj.csv")
c <- ggplot(plotval, aes(x = variable, y = value, fill = trt, label = signif)) + 
    geom_bar(position=position_dodge(), stat="identity") + geom_text(aes(y = c(500,1200,300,250,200, NA,NA,NA,NA,NA))) +
    geom_errorbar(aes(ymin=value-se, ymax=value+se),size=.3,width=.2,position=position_dodge(.9)) +
    xlab(NULL) + scale_fill_manual(values=c("#000000","#999999")) +
    ylab("Mean Number\n of Cells") +
    theme_pubr(legend = "none") +
    theme(axis.text.x = element_text(angle=45, hjust=1))

top <- plot_grid(b, a, c, scale=0.9, labels = "AUTO", nrow = 1, rel_widths = c(1,1,1.25), align="h", axis="tb")

Warning: Removed 5 rows containing missing values (geom_text).

top

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")

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")

lib_info_with_pseudo <- pData(cds)
t(monocle::reducedDimS(cds)) %>%
    as.data.frame() %>%
    select_(data_dim_1 = 1, data_dim_2 = 2) %>%
    rownames_to_column("sample_name") %>%
    mutate(sample_state) %>% 
    left_join(lib_info_with_pseudo %>% rownames_to_column("sample_name"), by = "sample_name") %>%
  arrange(Pseudotime) -> data_df

Warning: select_() is deprecated. 
Please use select() instead

The 'programming' vignette or the tidyeval book can help you
to program with select() : https://tidyeval.tidyverse.org
This warning is displayed once per session.

reduced_dim_coords <- reducedDimK(cds)

pseudo <- ggplot(data_df, aes(x=data_dim_1, y=data_dim_2, colour=Pseudotime)) + 
  geom_point(size=0.5) + 
  geom_point(data = data.frame(t(cds@reducedDimK)), aes(X1, X2), inherit.aes = F, size=0.2) + 
  xlab("Dim 1") + ylab("Dim 2") +
  theme_pubr(legend="right") + facet_wrap(.~trt)

plot_grid(top, pseudo, ncol=1, labels = c("", "D"))

detach("package:here", unload = T)
library(here)
save.image(file = here("data/glia/olig_alldata.RData"))

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            plyr_1.8.4          cowplot_1.0.0      
 [4] reshape2_1.4.3      ggrepel_0.8.1       ggsci_2.9          
 [7] ggpubr_0.2.1        magrittr_1.5        rstatix_0.1.1      
[10] forcats_0.4.0       stringr_1.4.0       dplyr_0.8.3        
[13] purrr_0.3.2         readr_1.3.1.9000    tidyr_0.8.3        
[16] tibble_2.1.3        tidyverse_1.2.1     monocle_2.10.1     
[19] DDRTree_0.1.5       irlba_2.3.3         VGAM_1.1-1         
[22] ggplot2_3.2.1       Biobase_2.42.0      BiocGenerics_0.28.0
[25] Matrix_1.2-17       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] igraph_1.2.4.1       lazyeval_0.2.2       densityClust_0.3    
  [7] listenv_0.7.0        fastICA_1.2-1        digest_0.6.20       
 [10] htmltools_0.3.6      viridis_0.5.1        gdata_2.18.0        
 [13] cluster_2.1.0        ROCR_1.0-7           openxlsx_4.1.0.1    
 [16] limma_3.38.3         globals_0.12.4       modelr_0.1.4        
 [19] RcppParallel_4.4.3   matrixStats_0.54.0   R.utils_2.9.0       
 [22] docopt_0.6.1         colorspace_1.4-1     rvest_0.3.4         
 [25] haven_2.1.0          xfun_0.8             sparsesvd_0.1-4     
 [28] crayon_1.3.4         jsonlite_1.6         zeallot_0.1.0       
 [31] survival_2.44-1.1    zoo_1.8-6            ape_5.3             
 [34] glue_1.3.1           gtable_0.3.0         leiden_0.3.1        
 [37] car_3.0-3            future.apply_1.3.0   abind_1.4-5         
 [40] scales_1.0.0         pheatmap_1.0.12      bibtex_0.4.2        
 [43] Rcpp_1.0.2           metap_1.1            viridisLite_0.3.0   
 [46] reticulate_1.13      proxy_0.4-23         foreign_0.8-71      
 [49] rsvd_1.0.2           SDMTools_1.1-221.1   tsne_0.1-3          
 [52] htmlwidgets_1.3      httr_1.4.1           FNN_1.1.3           
 [55] gplots_3.0.1.1       RColorBrewer_1.1-2   ica_1.0-2           
 [58] pkgconfig_2.0.2      R.methodsS3_1.7.1    uwot_0.1.3          
 [61] labeling_0.3         tidyselect_0.2.5     rlang_0.4.0         
 [64] munsell_0.5.0        cellranger_1.1.0     tools_3.5.3         
 [67] cli_1.1.0            generics_0.0.2       broom_0.5.2         
 [70] ggridges_0.5.1       evaluate_0.14        yaml_2.2.0          
 [73] npsurv_0.4-0         knitr_1.23           fs_1.3.1            
 [76] fitdistrplus_1.0-14  zip_2.0.3            caTools_1.17.1.2    
 [79] RANN_2.6.1           pbapply_1.4-1        future_1.14.0       
 [82] nlme_3.1-140         slam_0.1-45          R.oo_1.22.0         
 [85] xml2_1.2.0           compiler_3.5.3       rstudioapi_0.10     
 [88] curl_4.0             plotly_4.9.0         png_0.1-7           
 [91] ggsignif_0.5.0       lsei_1.2-0           stringi_1.4.3       
 [94] highr_0.8            lattice_0.20-38      HSMMSingleCell_1.2.0
 [97] vctrs_0.2.0          pillar_1.4.2         combinat_0.0-8      
[100] Rdpack_0.11-0        lmtest_0.9-37        RcppAnnoy_0.0.12    
[103] data.table_1.12.2    bitops_1.0-6         gbRd_0.4-11         
[106] R6_2.4.0             rio_0.5.16           KernSmooth_2.23-15  
[109] gridExtra_2.3        codetools_0.2-16     MASS_7.3-51.4       
[112] gtools_3.8.1         assertthat_0.2.1     rprojroot_1.3-2     
[115] withr_2.1.2          qlcMatrix_0.9.7      sctransform_0.2.0   
[118] hms_0.5.0            grid_3.5.3           rmarkdown_1.13      
[121] carData_3.0-2        Rtsne_0.15           git2r_0.25.2        
[124] lubridate_1.7.4