Supplementary Figure 1
Last updated: 2019-12-06
Checks: 6 1
Knit directory: bentsen-rausch-2019/
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#Load Libraries
library(Seurat)
library(tidyverse)
library(DESeq2)
library(here)
library(future)
library(cluster)
library(parallelDist)
library(ggplot2)
library(cowplot)
library(ggrepel)
library(future.apply)
library(reshape2)
library(gProfileR)
library(ggsignif)
plan("multiprocess", workers = 40)
options(future.globals.maxSize = 4000 * 1024^2)Load data
knitr::opts_chunk$set(message = FALSE, warnings = FALSE)
source(here("code/sc_functions.R"))
fgf.neur <- readRDS(here("data/neuron/fgf_neur_mappingscores.RDS"))Filter and recluster unmapped data
fgf.neur.unmap <- subset(fgf.neur, ref == "unmap")
fgf.neur.unmap <- reprocess_subset(obj = fgf.neur.unmap, dims = 30, resolution = 0.3)Warning in FindVariableFeatures.Assay(object = assay.data, selection.method
= selection.method, : selection.method set to 'vst' but count slot is
empty; will use data slot insteadWarning in eval(predvars, data, env): NaNs producedWarning in hvf.info$variance.expected[not.const] <- 10^fit$fitted: number
of items to replace is not a multiple of replacement lengthWarning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionModularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11986
Number of edges: 612849
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8920
Number of communities: 14
Elapsed time: 1 secondsDefaultAssay(fgf.neur.unmap) <- "SCT"
lab.mark <- FindAllMarkers(fgf.neur.unmap, only.pos = T, logfc.threshold = 0.5)
write_csv(x = lab.mark, here("output/neuron/neuron_clusters_unmapped.csv"))Plot unmapped neurons
data.frame(Embeddings(fgf.neur.unmap, reduction = "umap")) %>%
mutate(group = fgf.neur.unmap$group) %>%
mutate(celltype = Idents(fgf.neur.unmap)) %>%
sample_frac(1L) -> umap_embed
colnames(umap_embed)[1:2] <- c("UMAP 1", "UMAP 2")
label.df <- data.frame(cluster=levels(umap_embed$celltype),label=levels(umap_embed$celltype))
label.df_2 <- umap_embed %>%
group_by(celltype) %>%
dplyr::summarize(x = median(`UMAP 1`), y = median(`UMAP 2`))
prop_neur_byclus <- ggplot(umap_embed, aes(x=`UMAP 1`, y=`UMAP 2`, color=celltype)) +
geom_point(size=0.5, alpha=0.5) +
geom_text_repel(data = label.df_2, aes(label = celltype, x=x, y=y),
size=2,
inherit.aes = F, bg.colour="white", fontface="bold",
force=1, min.segment.length = unit(0, 'lines')) +
xlab("UMAP1") + ylab("UMAP2") +
ggpubr::theme_pubr(legend="none") + ggsci::scale_color_igv() + theme_figure
prop_neur_byclus
ggsave("data/figures/supp/umap_plot.pdf")Extract color scheme
g <- ggplot_build(prop_neur_byclus)
cols<-data.frame(colours = as.character(unique(g$data[[1]]$colour)),
label = as.character(unique(g$plot$data[, g$plot$labels$colour])))
colvec<-as.character(cols$colours)
names(colvec)<-as.character(cols$label)Resampling DEG
#Generate matrices
split_mats <- splitbysamp(fgf.neur.unmap, split_by="sample")
names(split_mats) <- unique(Idents(fgf.neur.unmap))
pb<-replicate(100, gen_pseudo_counts(split_mats, ncells=10))
names(pb) <- paste0(rep(names(split_mats)),"_",rep(1:100, each=length(names(split_mats))))
# Generate DESeq2 Objects
res<-rundeseq(pb)Boxplot
degenes<-lapply(res, function(x) {
tryCatch({
y<-x[[2]]
y<-na.omit(y)
data.frame(y)%>%filter(padj<0.1)%>%nrow()},
error=function(err) {NA})
})
boxplot<-lapply(unique(Idents(fgf.neur.unmap)), function(x) {
z<-unlist(degenes[grep(paste0("^",x,"_"), names(degenes))])
})
names(boxplot)<-unique(Idents(fgf.neur.unmap))
boxplot<-t(as.data.frame(do.call("rbind", boxplot)))
rownames(boxplot)<-1:100
genenum<-melt(boxplot)
write_csv(genenum, path = here("output/neuron/genenum_supplemental.csv"))resampling results
deboxplot<-ggplot(genenum,aes(x=reorder(Var2, -value), y=value, fill=factor(Var2))) +
geom_boxplot(notch = T, alpha=0.75) +
scale_fill_manual(values = colvec) +
ggpubr::theme_pubr() +
theme(axis.text.x = element_text(angle=45, hjust=1), legend.position = "none") +
ylab("Number DEG") + xlab(NULL) + theme_figure
deboxplot
ggsave(here("data/figures/supp/deboxplot.pdf"))
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggsignif_0.5.0 gProfileR_0.6.7
[3] reshape2_1.4.3 future.apply_1.3.0
[5] ggrepel_0.8.0.9000 cowplot_1.0.0
[7] parallelDist_0.2.4 cluster_2.1.0
[9] future_1.14.0 here_0.1
[11] DESeq2_1.22.2 SummarizedExperiment_1.12.0
[13] DelayedArray_0.8.0 BiocParallel_1.16.6
[15] matrixStats_0.54.0 Biobase_2.42.0
[17] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
[19] IRanges_2.16.0 S4Vectors_0.20.1
[21] BiocGenerics_0.28.0 forcats_0.4.0
[23] stringr_1.4.0 dplyr_0.8.3
[25] purrr_0.3.2 readr_1.3.1.9000
[27] tidyr_0.8.3 tibble_2.1.3
[29] ggplot2_3.2.1 tidyverse_1.2.1
[31] Seurat_3.0.3.9036
loaded via a namespace (and not attached):
[1] reticulate_1.13 R.utils_2.9.0 tidyselect_0.2.5
[4] RSQLite_2.1.1 AnnotationDbi_1.44.0 htmlwidgets_1.3
[7] grid_3.5.3 Rtsne_0.15 munsell_0.5.0
[10] codetools_0.2-16 ica_1.0-2 withr_2.1.2
[13] colorspace_1.4-1 highr_0.8 knitr_1.23
[16] rstudioapi_0.10 ROCR_1.0-7 gbRd_0.4-11
[19] listenv_0.7.0 labeling_0.3 Rdpack_0.11-0
[22] git2r_0.25.2 GenomeInfoDbData_1.2.0 bit64_0.9-7
[25] rprojroot_1.3-2 vctrs_0.2.0 generics_0.0.2
[28] xfun_0.8 R6_2.4.0 rsvd_1.0.2
[31] locfit_1.5-9.1 bitops_1.0-6 assertthat_0.2.1
[34] SDMTools_1.1-221.1 scales_1.0.0 nnet_7.3-12
[37] gtable_0.3.0 npsurv_0.4-0 globals_0.12.4
[40] workflowr_1.4.0 rlang_0.4.0 zeallot_0.1.0
[43] genefilter_1.64.0 splines_3.5.3 lazyeval_0.2.2
[46] acepack_1.4.1 broom_0.5.2 checkmate_1.9.4
[49] yaml_2.2.0 modelr_0.1.4 backports_1.1.4
[52] Hmisc_4.2-0 tools_3.5.3 gplots_3.0.1.1
[55] RColorBrewer_1.1-2 ggridges_0.5.1 Rcpp_1.0.2
[58] plyr_1.8.4 base64enc_0.1-3 zlibbioc_1.28.0
[61] RCurl_1.95-4.12 ggpubr_0.2.1 rpart_4.1-15
[64] pbapply_1.4-1 zoo_1.8-6 haven_2.1.0
[67] fs_1.3.1 magrittr_1.5 RSpectra_0.15-0
[70] data.table_1.12.2 lmtest_0.9-37 RANN_2.6.1
[73] whisker_0.3-2 fitdistrplus_1.0-14 hms_0.5.0
[76] lsei_1.2-0 evaluate_0.14 xtable_1.8-4
[79] XML_3.98-1.20 readxl_1.3.1 gridExtra_2.3
[82] compiler_3.5.3 KernSmooth_2.23-15 crayon_1.3.4
[85] R.oo_1.22.0 htmltools_0.3.6 Formula_1.2-3
[88] geneplotter_1.60.0 RcppParallel_4.4.3 lubridate_1.7.4
[91] DBI_1.0.0 MASS_7.3-51.4 Matrix_1.2-17
[94] cli_1.1.0 R.methodsS3_1.7.1 gdata_2.18.0
[97] metap_1.1 igraph_1.2.4.1 pkgconfig_2.0.2
[100] foreign_0.8-71 plotly_4.9.0 xml2_1.2.0
[103] annotate_1.60.1 XVector_0.22.0 bibtex_0.4.2
[106] rvest_0.3.4 digest_0.6.20 sctransform_0.2.0
[109] RcppAnnoy_0.0.12 tsne_0.1-3 rmarkdown_1.13
[112] cellranger_1.1.0 leiden_0.3.1 htmlTable_1.13.1
[115] uwot_0.1.3 gtools_3.8.1 nlme_3.1-140
[118] jsonlite_1.6 viridisLite_0.3.0 pillar_1.4.2
[121] ggsci_2.9 lattice_0.20-38 httr_1.4.1
[124] survival_2.44-1.1 glue_1.3.1 png_0.1-7
[127] bit_1.1-14 stringi_1.4.3 blob_1.1.1
[130] latticeExtra_0.6-28 caTools_1.17.1.2 memoise_1.1.0
[133] irlba_2.3.3 ape_5.3