Onecut3 affected cells

Load data and setup parameters

Code

# Load tidyverse infrastructure packages
library(here)
library(tidyverse)
library(magrittr)
library(future)
library(furrr)

# Load packages for scRNA-seq analysis and visualisation
library(Seurat)
library(SeuratWrappers)
library(SeuratDisk)
library(ggplot2)
library(cowplot)
library(UpSetR)
library(patchwork)
library(grid)
library(Nebulosa)
library(sctransform)
library(OmnipathR)
library(dnet)
library(gprofiler2)
library(scCustomize)

Set paths

Code

src_dir <- here::here("code")
data_dir <- here::here("data")
output_dir <- here::here("outputs")
plots_dir <- here::here(output_dir, "figures")
tables_dir <- here::here(output_dir, "tables")

Load helper functions and gene-sets

Code

source(here::here(src_dir, "main.R"))
source(here::here(src_dir, "load.R"))
source(here::here(src_dir, "genes.R"))

Set fixed variables

Code

# set seed
reseed <- 42
set.seed(seed = reseed)

# available RAM in kB
ram <- CheckRAM()

# available cores
n.cores <- AvailableCores(prop2use = .1)

# Parameters for parallel execution
plan("multisession", workers = n.cores)
options(
  future.globals.maxSize = ram,
  future.rng.onMisuse = "ignore"
)
plan()

multisession:
- args: function (..., workers = 25, envir = parent.frame())
- tweaked: TRUE
- call: plan("multisession", workers = n.cores)

Code

# bpparam <- BiocParallel::MulticoreParam(workers = n.cores)

Code

# thresholds
threshold.max.p.value <- 5e-3
threshold.min.baseMean <- 1
threshold.min.log2FoldChange <- 2
threshold.max.log2FoldChange <- -0.45

# Params
param.merged.vst.variable.features.n <- param.integrated.vst.variable.features.n <- 7000
param.merged.vst.ncells <- param.integrated.vst.ncells <- 10000
param.merged.vst.vars.to.regress <- param.integrated.vst.vars.to.regress <- c("log_umi_per_gene")
param.merged.vst.return.only.var.genes <- param.integrated.vst.return.only.var.genes <- FALSE

param.merged.dm.pcs.slct.pca.n <- param.integrated.dm.pcs.slct.pca <- 100
param.merged.dm.pcs.slct.snn.dims <- param.integrated.dm.pcs.slct.snn <- 1:100
param.merged.dm.pcs.slct.umap.dims <- param.integrated.dm.pcs.slct.umap.dims <- 1:100

param.merged.dm.umap.reduction <- param.integrated.dm.umap.reduction <- "pca"
param.merged.dm.umap.return.model <- param.integrated.dm.umap.return.model <- TRUE
param.merged.dm.umap.slot <- param.integrated.dm.umap.slot <- "data"
param.merged.dm.umap.umap.method <- param.integrated.dm.umap.umap.method <- "uwot"
param.merged.dm.umap.reduction.model <- param.integrated.dm.umap.reduction.model <- NULL
param.merged.dm.umap.return.model <- param.integrated.dm.umap.return.model <- FALSE
param.merged.dm.umap.n.neighbors <- param.integrated.dm.umap.n.neighbors <- 30L
param.merged.dm.umap.n.components <- param.integrated.dm.umap.n.components <- 2L
param.merged.dm.umap.metric <- param.integrated.dm.umap.metric <- "cosine"

param.sp.vst.variable.features.n <- 3000
param.sp.vst.ncells <- 4000
param.sp.vst.vars.to.regress <- c("log_umi_per_gene")
param.sp.vst.return.only.var.genes <- FALSE
param.sp.dm.pcs.slct.pca <- 30
param.sp.dm.pcs.slct.snn <- 1:30
param.sp.dm.pcs.slct.umap.dims <- 1:30
param.sp.dm.umap.reduction <- "pca"
param.sp.dm.umap.return.model <- TRUE
param.sp.dm.umap.slot <- "data"
param.sp.dm.umap.umap.method <- "uwot"
param.sp.dm.umap.reduction.model <- NULL
param.sp.dm.umap.return.model <- FALSE
param.sp.dm.umap.n.neighbors <- 20L
param.sp.dm.umap.n.components <- 2L
param.sp.dm.umap.metric <- "cosine"

# g1 <- 'Onecut2'
# g2 <- 'Onecut3'

# Subsets for Onecuts analysis
subsets_df <-
  tribble(
    ~srtfile,                        ~subname,            ~subdescript,
    #--|--|---
    "rar2020.srt.neuro",             "neuro",             "Neuronal clusters",
    "rar2020.srt.filt",              "filt",              "Filtered Neuronal clusters",
    "rar2020.srt.mb.onecut.related", "mb.onecut.related", "Neuronal clusters probably related to Onecut3",
    "rar2020.srt.published.onecuts", "published.onecuts", "Published Onecut2 and Onecut3 identified clusters",
    "rar2020.srt.cont.oc2or3",       "cont.oc2or3",       "Onecut2 OR Onecut3 pls-subset from Neuronal clusters",
    "rar2020.srt.cont.oc3",          "cont.oc3",          "Onecut3 pls-subset from Neuronal clusters"
  )

# Original clusters names for subsets
# TODO rename for consistent format with table
# [[I]] rewrite with do.call and strsplit or eval and parse to include all sets?
rar2020.clusters.non.neuro <-
  as.character(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 38, 42, 45))
rar2020.clusters.excl.draft <-
  as.character(c(32, 23, 30, 25, 29, 43, 26, 31, 24))
rar2020.clusters.incl.draft <-
  as.character(c(18, 16, 14, 15, 17, 13, 12, 34, 36, 41))

# Dev. Stages
rar2020.ages.all <- c("E15", "E17", "P00", "P02", "P10", "P23")
rar2020.ages.postnat <- c("P00", "P02", "P10", "P23")

Load published Seurat-object from Romanov et al. (2020) and colour-code for consistency

Code

samples_df <- read_tsv(here::here("data/samples.tsv"))
rar2020.srt.pub <- readr::read_rds(file.path(data_dir, "oldCCA_nae_srt.rds"))
rar2020.srt.pub %<>% UpdateSeuratObject()
colnames(rar2020.srt.pub@reductions$umap@cell.embeddings) <- c("UMAP_1", "UMAP_2")
colours <- readr::read_lines(here(data_dir, "colours_wtree.tsv"))
clrlev <- levels(rar2020.srt.pub$wtree)
names(colours) <- clrlev

Load results of DESeq2 analysis of Onecut3 in vitro overexpression experiment in Neuro2A cell line

Code

neuro2a <-
  readr::read_csv(
    here(
      data_dir,
      "neuro2a-onecut3-overexpression_rnaseq_deseq-global-contrast-gr.csv"
    )
  )

Prepare data subsets

Derive ages in Seurat object from cell names and mark postnatal stages

Code

rar2020.srt.pub$orig.ident <-
  rar2020.srt.pub %>%
  colnames() %>%
  str_split(pattern = ":", simplify = TRUE) %>%
  .[, 1] %>%
  plyr::mapvalues(
    x = .,
    from = samples_df$fullname,
    to = samples_df$sample
  )
rar2020.srt.pub$age <-
  plyr::mapvalues(
    x = rar2020.srt.pub$orig.ident,
    from = samples_df$sample,
    to = samples_df$age
  )
Idents(rar2020.srt.pub) <-
  factor(rar2020.srt.pub$age,
    levels  = rar2020.ages.all,
    ordered = TRUE
  )
rar2020.srt.pub$age <- Idents(rar2020.srt.pub)
rar2020.srt.pub$postnat <-
  rar2020.srt.pub$age %in% rar2020.ages.postnat

Create subsets from Seurat object for exploratory analysis

Code

Idents(rar2020.srt.pub) <- "wtree"

holder_srt <- subsets_df$srtfile %>%
  map(c) %>%
  setNames(subsets_df$srtfile)
holder_srt_split <- subsets_df$srtfile %>%
  map(c) %>%
  setNames(subsets_df$srtfile)

holder_srt[[1]] <- subsetSrt(
  dat.srt = rar2020.srt.pub,
  srt.name = subsets_df$subdescript[1],
  idents = rar2020.clusters.non.neuro,
  invert = TRUE
)

holder_srt[[5]] <- subset(x = holder_srt[[1]], subset = Onecut2 > 0 | Onecut3 > 0)
holder_srt[[5]]@project.name <- subsets_df$subdescript[5]

Select most promising genes from in vitro Onecut3 over-expression experiment.

Code

neuro2a %>%
  filter(
    padj < threshold.max.p.value,
    baseMean > threshold.min.baseMean,
    (log2FoldChange >= threshold.min.log2FoldChange | log2FoldChange <= threshold.max.log2FoldChange)
  ) %>%
  select(gene_name, baseMean, log2FoldChange, lfcSE, padj) %>%
  arrange(-log2FoldChange) -> neuro2a.sub

neuro2a.sub %>% knitr::kable()

gene_name	baseMean	log2FoldChange	lfcSE	padj
Onecut3	2.566847e+04	9.4825701	0.2021581	0.0000000
Uts2	3.576302e+01	7.5500575	1.1744736	0.0000000
Atp13a4	4.326759e+01	6.9163863	1.1669195	0.0000000
Pla2g2d	9.563099e+01	6.5979910	0.6667478	0.0000000
Gpnmb	4.485152e+01	6.3595647	1.0279639	0.0000000
Tns3	1.835797e+01	5.6944015	1.2142714	0.0000174
Aicda	9.631527e+00	5.6295291	1.2934371	0.0001112
Krt18	7.710241e+00	5.1954183	1.4623745	0.0006341
Btn1a1	2.287652e+01	5.0264926	0.9052171	0.0000036
Mep1a	6.555482e+00	4.8235899	1.6535449	0.0015375
Enpp2	6.553108e+00	4.8231089	1.6536941	0.0017111
Robo3	1.530657e+01	4.7692766	1.1433108	0.0001158
Nav2	1.047926e+01	4.6255816	1.6057992	0.0017726
Col5a1	1.395547e+01	4.5869179	1.2107552	0.0002587
Rnasel	1.387330e+01	4.5837092	1.2179147	0.0002702
Inava	6.092479e+00	4.5346820	1.8316075	0.0017893
Dok5	1.313822e+01	4.4924645	1.2375090	0.0003627
Abca8a	2.980160e+01	4.4903500	0.6215878	0.0000000
Ccdc80	1.581562e+01	4.4869340	0.9272581	0.0000429
Cd300a	9.749496e+00	4.4513241	1.6871202	0.0024966
Fcgbp	3.851741e+01	4.2635672	0.5129964	0.0000000
Hmgcs2	9.282358e+00	4.2330265	1.8166281	0.0036025
Ramp3	1.243662e+01	4.0484880	1.0528793	0.0005531
Lgr6	1.464627e+01	4.0212490	0.8751085	0.0001080
Ephb2	1.065564e+01	3.9024278	1.5831057	0.0042868
Lrrc15	2.288265e+01	3.8974134	0.6150118	0.0000000
Col1a1	2.046678e+02	3.8711684	0.2373334	0.0000000
Gsdma	2.206785e+01	3.8277084	0.6241420	0.0000001
Klb	2.409124e+01	3.6777896	0.5781942	0.0000000
Napsa	2.611302e+01	3.6376276	0.5641667	0.0000000
Gli1	1.680753e+02	3.5791301	0.2292389	0.0000000
Cemip	1.707480e+01	3.5782976	0.7089852	0.0000603
Oas2	4.050870e+01	3.5535095	0.4419886	0.0000000
Adcy5	1.752784e+01	3.4591966	0.6665688	0.0000077
Elovl3	1.092320e+01	3.4528391	1.0752791	0.0020589
Hba-a1	1.397219e+01	3.4455395	0.7993929	0.0006044
Chrna2	4.797299e+00	3.4269043	2.2726451	0.0046964
Tmem255a	1.463470e+01	3.3514989	0.7230162	0.0001099
Rgcc	4.899927e+01	3.2798083	0.3738403	0.0000000
Vsir	1.561047e+01	3.2764173	0.6798631	0.0000431
Id2	8.600826e+01	3.1746368	0.2720486	0.0000000
Ccnd2	3.107389e+01	3.1465288	0.4516910	0.0000000
Cnmd	4.146511e+01	3.1346735	0.3867457	0.0000000
Iffo2	2.186291e+01	3.0223522	0.5389123	0.0000051
Fibcd1	1.777831e+01	3.0211175	0.6116922	0.0000948
Rtp4	9.391453e+01	2.9807989	0.2603853	0.0000000
Fst	2.831187e+02	2.8247561	0.1549335	0.0000000
Trim12c	2.779289e+01	2.7095860	0.4603549	0.0000005
Ddx60	3.720612e+01	2.6472650	0.4068872	0.0000000
Wfdc1	3.436215e+01	2.6285033	0.4068434	0.0000000
Xaf1	1.246777e+02	2.5842174	0.2208252	0.0000000
Myh11	3.551432e+01	2.5771303	0.3966535	0.0000000
Ppl	1.617763e+01	2.5533742	0.6530466	0.0003627
Angpt2	2.027067e+02	2.4837492	0.1680342	0.0000000
Oas1g	3.081035e+02	2.4309620	0.1380667	0.0000000
Actn3	3.943567e+01	2.4256660	0.3823002	0.0000000
Gbp7	4.677136e+01	2.3661464	0.3400031	0.0000000
Irf7	5.208796e+02	2.3429517	0.1125816	0.0000000
Mmel1	5.666474e+01	2.3359727	0.3070266	0.0000000
Oas1a	2.377966e+02	2.3168755	0.1515249	0.0000000
Prss16	5.455953e+01	2.3157017	0.3026879	0.0000000
Tent5b	2.208709e+01	2.2766464	0.5200212	0.0001225
Nlrc5	2.915088e+01	2.2682699	0.4255217	0.0000048
Usp2	4.225483e+01	2.2473728	0.3665659	0.0000001
Myh7	1.292863e+01	2.1978633	0.9750005	0.0035381
Fos	3.492183e+02	2.1856028	0.1383600	0.0000000
Snx20	3.967236e+01	2.1019659	0.3808530	0.0000018
Tspan33	3.702228e+01	2.0959051	0.3613784	0.0000004
Gbp3	2.842574e+01	2.0651137	0.4209822	0.0000246
Cib2	2.696697e+01	2.0648020	0.4437731	0.0000587
Oas3	6.509119e+01	2.0551929	0.2736893	0.0000000
Lrp4	1.518820e+02	2.0157114	0.1853319	0.0000000
Axl	5.670119e+01	2.0121497	0.2936483	0.0000000
Nckap5los	3.395294e+02	-0.4510405	0.1759480	0.0007472
5430401H09Rik	1.942687e+02	-0.4812028	0.2028100	0.0009033
Gm13341	1.795072e+04	-0.4853780	0.0937359	0.0000000
Ovgp1	7.544007e+02	-0.4855046	0.1313155	0.0000243
Nags	2.359603e+02	-0.4894776	0.1750490	0.0004800
Gm13431	4.824839e+02	-0.5380966	0.1364246	0.0000129
Mfsd2b	1.515170e+02	-0.5410346	0.2271112	0.0020675
Gm13340	8.269480e+04	-0.5693516	0.0932658	0.0000000
Msh5	3.765577e+02	-0.5705829	0.1325347	0.0000025
mt-Co1	2.949108e+05	-0.5725768	0.0968816	0.0000000
Gm33148	9.249945e+01	-0.6420429	0.4401495	0.0049625
Ascl2	1.034699e+02	-0.6580709	0.2926764	0.0024956
Tmem191c	6.125862e+02	-0.6619891	0.1218598	0.0000000
1700029J07Rik	3.458456e+02	-0.6885587	0.1383063	0.0000002

Visualise selected genes for check on Seurat object

Code

param.plot.1.repel.box.padding <- 0.5
param.plot.1.filter.padj <- 0.01
param.plot.1.filter.baseMean <- 1
param.plot.1.point.colour.background <- "grey50"
DESeq2 <- neuro2a %>%
  select(gene_name, baseMean, log2FoldChange, lfcSE, padj) %>%
  filter(padj < param.plot.1.filter.padj, baseMean > param.plot.1.filter.baseMean) %>%
  mutate(gene_name = if_else(condition = gene_name %in% neuro2a.sub$gene_name, gene_name, "")) %>%
  ggplot(., aes(
    x = log2(baseMean + 1),
    y = log2FoldChange,
    label = gene_name,
    size = -log10(lfcSE))) +
  geom_vline(
    xintercept = 0,
    linetype = "solid",
    colour = "black",
    size = 0.5
  ) +
  geom_hline(
    yintercept = 0,
    linetype = "solid",
    colour = "black",
    size = 0.5
  ) +
  geom_point(
    data = neuro2a[!neuro2a$gene_name %in% neuro2a.sub$gene_name, ],
    color = param.plot.1.point.colour.background
  ) +
  ggrepel::geom_text_repel(
    aes(point.size = -log10(lfcSE)), # data point size
    size = 6, # font size in the text labels
    point.padding = 0, # additional padding around each point
    min.segment.length = 0, # draw all line segments
    max.time = 2, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
    seed = reseed,
    max.overlaps = Inf,
    box.padding = 0.5
  ) +
  geom_point(
    data = neuro2a[neuro2a$gene_name %in% neuro2a.sub$gene_name, ],
    color = "red"
  ) +
  labs(
    title = "Neuro2A cells after Onecut3 overexpression",
    subtitle = "Results of Differential Genes Expression Analysis",
    caption = sprintf(
      "Selected genes (red) with adjusted p-value < %s \n minimal expression value=%s and observed log2FoldChange value >%s or <%s",
      threshold.max.p.value,
      threshold.min.baseMean,
      threshold.min.log2FoldChange,
      threshold.max.log2FoldChange
    )
  )

DESeq2

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_DEG_KI-N2a.pdf"),
  plot = DESeq2,
  base_height = 12,
  base_width = 14
)

Code

neuro2a %>%
  select(gene_name, baseMean, log2FoldChange, lfcSE, padj) %>%
  filter(padj < param.plot.1.filter.padj, baseMean > param.plot.1.filter.baseMean) %>%
  mutate(gene_name = if_else(condition = gene_name %in% neuro2a.sub$gene_name, gene_name, "")) %>%
  ggplot(., aes(
    x = log2FoldChange,
    y = -log10(padj),
    label = gene_name)) +
  geom_vline(
    xintercept = 0,
    linetype = "solid",
    colour = "black",
    size = 0.5
  ) +
  geom_hline(
    yintercept = 0,
    linetype = "solid",
    colour = "black",
    size = 0.5
  ) + 
  xlim(c(-12.5, 12.5)) +
  geom_point(
    data = neuro2a[!neuro2a$gene_name %in% neuro2a.sub$gene_name, ],
    color = param.plot.1.point.colour.background
  ) +
  ggrepel::geom_text_repel(
    size = 5, # font size in the text labels
    point.padding = 0, # additional padding around each point
    min.segment.length = 0.5, # draw all line segments
    max.time = 5, max.iter = 1e6, # stop after 1 second, or after 100,000 iterations
    seed = reseed,
    max.overlaps = Inf,
    box.padding = 0.2
  ) +
  geom_point(
    data = neuro2a[neuro2a$gene_name %in% neuro2a.sub$gene_name, ],
    color = "red"
  ) +
  labs(
    title = "Neuro2A cells after Onecut3 overexpression",
    subtitle = "Results of Differential Genes Expression Analysis",
    caption = sprintf(
      "Selected genes (red) with adjusted p-value < %s \n minimal expression value=%s and observed log2FoldChange value >%s or <%s",
      threshold.max.p.value,
      threshold.min.baseMean,
      threshold.min.log2FoldChange,
      threshold.max.log2FoldChange
    )
  ) +
  theme_light()

Knit interactions, that explains KI experiment results

Code

n2a.top <- neuro2a.sub %>%
  .[.$gene_name %in% rownames(GetAssayData(rar2020.srt.pub, slot = "scale.data")), ] %>%
  .$gene_name %>%
  .[-1]
svFeaturePLOT <- function(gene) {
  save_plot(
    filename = paste0(plots_dir, "/plot_feature_", gene, ".pdf"),
    plot = FeaturePlot(rar2020.srt.pub, features = gene),
    base_height = 6
  )
}
lapply(n2a.top, svFeaturePLOT)

[[1]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Uts2.pdf"

[[2]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Atp13a4.pdf"

[[3]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Pla2g2d.pdf"

[[4]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Gpnmb.pdf"

[[5]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Tns3.pdf"

[[6]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Aicda.pdf"

[[7]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Krt18.pdf"

[[8]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Btn1a1.pdf"

[[9]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Mep1a.pdf"

[[10]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Enpp2.pdf"

[[11]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Robo3.pdf"

[[12]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Nav2.pdf"

[[13]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Col5a1.pdf"

[[14]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Rnasel.pdf"

[[15]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Dok5.pdf"

[[16]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Abca8a.pdf"

[[17]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Ccdc80.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Lgr6.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Ephb2.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Lrrc15.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Col1a1.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Gsdma.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Klb.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Napsa.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Gli1.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Cemip.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Oas2.pdf"

[[32]]
[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Adcy5.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Hba-a1.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Chrna2.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Tmem255a.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Rgcc.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Vsir.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Id2.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Ccnd2.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Iffo2.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Fibcd1.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Rtp4.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Fst.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Trim12c.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Ddx60.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_Tmem191c.pdf"

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[1] "/data/src/deciphering-role-of-onecut3-transcription-factor-in-cell-fate-decisions/outputs/figures/plot_feature_1700029J07Rik.pdf"

Code

n2a.onecut3.plots <- n2a.top %>%
  map(~ plot_density(rar2020.srt.pub, c("Onecut3", .x), joint = TRUE, combine = FALSE) %>% .[[length(.)]]) %>%
  setNames(n2a.top)
plt_combdens <- plot_grid(
  plotlist = n2a.onecut3.plots,
  ncol = 3,
  byrow = TRUE
)
cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_DEG__KI-N2a_density_combined_wt_Onecut3.pdf"),
  plot = plt_combdens,
  base_height = 138,
  base_width = 19.416,
  limitsize = FALSE
)

Code

SexScoring <- function(
    object,
    n = 10,
    ctrl = NULL,
    ...) {
  name <- "Sex.Score"
  features <- list(
    "female" = c("Tsix", "Xist") %>% .[. %in% rownames(object)], 
    "male" = c("Eif2s3y", "Ddx3y", "Uty", "Kdm5d", "Zfy", "Rps4y1", "Rps4y2", "Sry") %>% .[. %in% rownames(object)]
  )
  if (is.null(x = ctrl)) {
    ctrl <- min(vapply(X = features, FUN = length, FUN.VALUE = numeric(length = 1)))
  }
  object.cc <- AddModuleScore(
    object = object,
    features = features,
    name = name,
    ctrl = ctrl,
    ...
  )
  cc.columns <- grep(pattern = name, x = colnames(x = object.cc[[]]), value = TRUE)
  cc.scores <- object.cc[[cc.columns]]
  rm(object.cc)
  invisible(gc())
  colnames(x = cc.scores) <-
    str_c(c("female_score", "male_score"), n, sep = "_")
  rownames(x = cc.scores) <- colnames(object)
  cc.scores <-
    cc.scores[, str_c(c("female_score", "male_score"), n, sep = "_")]
  object[[colnames(x = cc.scores)]] <- cc.scores
  return(object)
}

rar2020.srt.pub <- SexScoring(
  rar2020.srt.pub,
  n = 5,
  assay = "RNA"
)

Code

p4 <- plot_density(rar2020.srt.pub, c("Onecut3", "Onecut2"), joint = TRUE)
p4 + plot_layout(ncol = 1)

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Onecut2_density_combined_wt_Onecut3.pdf"),
  plot = p4 + plot_layout(ncol = 1),
  base_height = 18,
  base_width = 6.472,
  limitsize = FALSE
)

Code

FeaturePlot_scCustom(rar2020.srt.pub, "male_score_5")

Code

male <- c("Eif2s3y", "Ddx3y", "Uty", "Kdm5d", "Zfy", "Rps4y1", "Rps4y2", "Sry")

Plot_Density_Custom(rar2020.srt.pub, c("male_score_5", "Onecut3"), joint = TRUE)

Code

Plot_Density_Custom(rar2020.srt.pub, c("male_score_5", "Eif2s3y"), joint = TRUE)

Code

Plot_Density_Custom(rar2020.srt.pub, c("male_score_5", "Uty"), joint = TRUE)

Code

Plot_Density_Custom(rar2020.srt.pub, c("male_score_5", "Kdm5d"), joint = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("male_score_5", "Onecut3"), blend = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("Onecut3", "Eif2s3y"), blend = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("Onecut3", "Uty"), blend = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("Onecut3", "Kdm5d"), blend = TRUE)

Code

p1 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Eif2s3y"))

p2 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Uty"))

p3 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Kdm5d"))
p1 | p2 | p3

Code

FeaturePlot_scCustom(rar2020.srt.pub, "female_score_5")

Code

female <- c("Tsix", "Xist")

Plot_Density_Custom(rar2020.srt.pub, c("female_score_5", "Onecut3"), joint = TRUE)

Code

Plot_Density_Custom(rar2020.srt.pub, c("female_score_5", "Tsix"), joint = TRUE)

Code

Plot_Density_Custom(rar2020.srt.pub, c("female_score_5", "Xist"), joint = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("Onecut3", "Tsix"), blend = TRUE)

Code

FeaturePlot(rar2020.srt.pub, c("Onecut3", "Xist"), blend = TRUE)

Code

p1 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Onecut3"))

p2 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Tsix"))

p3 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Xist"))

p1 | p2 | p3

Code

p5 <- plot_density(rar2020.srt.pub, c("Onecut3", "Ascl1"), joint = TRUE)
p5 + plot_layout(ncol = 1)

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Ascl1_density_combined_wt_Onecut3.pdf"),
  plot = p5 + plot_layout(ncol = 1),
  base_height = 18,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p5 <- plot_density(rar2020.srt.pub, c("Onecut3", "Ascl2"), joint = TRUE)
p5 + plot_layout(ncol = 1)

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Ascl2_density_combined_wt_Onecut3.pdf"),
  plot = p5 + plot_layout(ncol = 1),
  base_height = 18,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p_list <- plot_density(rar2020.srt.pub, c("Onecut3", "Slc32a1"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Slc32a1_density_combined_wt_Onecut3.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p_list <- plot_density(rar2020.srt.pub, c("Onecut3", "Slc17a6"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Slc17a6_density_combined_wt_Onecut3.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p_list <- plot_density(rar2020.srt.pub, c("Gad1", "Gad2"), joint = TRUE)
p_list + plot_layout(ncol = 1)

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Gad1_density_combined_wt_Gad2.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p_list <- plot_density(rar2020.srt.pub, c("Slc32a1", "Slc17a6"), joint = TRUE)
p_list + plot_layout(ncol = 1)

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Slc17a6_density_combined_wt_Slc32a1.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p1 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Onecut3"))

p2 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Slc32a1"))

p3 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Slc17a6"))
p1 | p2 | p3

Code

p_list <- plot_density(rar2020.srt.pub, c("Onecut3", "Cnr1"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

Code

p_list <- plot_density(rar2020.srt.pub, c("Onecut3", "Th"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Th_density_combined_wt_Onecut3.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p_list <- plot_density(rar2020.srt.pub, c("Onecut3", "Trh"), joint = TRUE, combine = FALSE)
p_list[[length(p_list)]]

Code

cowplot::save_plot(
  filename = paste0(plots_dir, "/plot_Trh_density_combined_wt_Onecut3.pdf"),
  plot = p_list[[length(p_list)]],
  base_height = 6,
  base_width = 6.472,
  limitsize = FALSE
)

Code

p1 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Onecut3"))

p2 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Th"))

p3 <- FeaturePlot_scCustom(rar2020.srt.pub, c("Trh"))
p1 | p2 | p3

Code

neurons <- subset(rar2020.srt.pub, subset = Rbfox3 > 0 | Elavl4 > 0 | Snap25 > 0 | Stmn2 > 0)
onecut <- subset(neurons, subset = Onecut3 > 0)

Idents(onecut, cells = WhichCells(onecut, expression = ((Gad1 < 2 & Gad2 < 2 & Slc32a1 < 2) & Slc17a6 > 1 & Th == 0 & Trh > 0))) <- "Trh_neurons"
Idents(onecut, cells = WhichCells(onecut, expression = ((Gad1 > 1 | Gad2 > 1 | Slc32a1 > 1) & Slc17a6 == 0 & Th > 0 & Trh == 0))) <- "Th_neurons"
Idents(onecut, cells = WhichCells(onecut, expression = ((Gad1 < 2 & Gad2 < 2 & Slc32a1 < 2) & Slc17a6 > 2 & Th == 0 & Trh == 0))) <- "Glutamate"
Idents(onecut, cells = WhichCells(onecut, expression = ((Gad1 > 0 & Gad2 > 0 & Slc32a1 > 0) & Slc17a6 == 0 & Th == 0 & Trh == 0))) <- "GABA"

onecut <- subset(onecut, idents = c("Trh_neurons", "Th_neurons", "Glutamate", "GABA"))
onecut$goi <- Idents(onecut)

onecut@meta.data %>% janitor::tabyl(goi, age)

Code

table(Idents(onecut))

       GABA   Glutamate  Th_neurons Trh_neurons 
         89          35          17          30 

Code

onecut <- FindVariableFeatures(onecut, selection.method = "vst", nfeatures = 5000)
onecut <- ScaleData(onecut)

Code

invisible(gc())
plan("sequential")
invisible(gc())
plan("multisession", workers = n.cores)
all_markers_genes <-
  FindAllMarkers(object = onecut, verbose = F, test.use = "LR", latent.vars = c("log_umi_per_gene"), only.pos = T, min.pct = 1e-2, logfc.threshold = 0, random.seed = reseed, min.cells.feature = 2, return.thresh = 1)
all_markers_genes <-
  all_markers_genes %>%
  Add_Pct_Diff()

write_csv(all_markers_genes, here(tables_dir, "all-marker-genes-goi.csv"))
all_markers_genes |> filter(p_val_adj <= 1e-02) |> arrange(desc(avg_log2FC)) |> group_by(cluster)

Code

top_5 <- Extract_Top_Markers(marker_dataframe = all_markers_genes, num_genes = 5, group_by = "cluster", gene_column = "gene", rank_by = "avg_log2FC", make_unique = TRUE, named_vector = FALSE)

Code

DotPlot_scCustom(seurat_object = onecut, colors_use = viridis::viridis(n = 30, alpha = .75, direction = -1, option = "E"), features = c("Onecut3", top_5), flip_axes = T, x_lab_rotate = TRUE)

Code

Stacked_VlnPlot(seurat_object = onecut, features = c("Onecut3", top_5), x_lab_rotate = TRUE)

Code

get_interaction_resources()

  [1] "ABS"                         "ACSN"                       
  [3] "ACSN_SignaLink3"             "Adhesome"                   
  [5] "AlzPathway"                  "ARACNe-GTEx_DoRothEA"       
  [7] "ARN"                         "Baccin2019"                 
  [9] "BEL-Large-Corpus_ProtMapper" "BioGRID"                    
 [11] "CA1"                         "CancerCellMap"              
 [13] "CancerDrugsDB"               "CellCall"                   
 [15] "CellChatDB"                  "CellChatDB-cofactors"       
 [17] "Cellinker"                   "CellPhoneDB"                
 [19] "CellPhoneDB_Cellinker"       "CellTalkDB"                 
 [21] "CollecTRI"                   "connectomeDB2020"           
 [23] "Cui2007"                     "CytReg_CollecTRI"           
 [25] "dbPTM"                       "DeathDomain"                
 [27] "DEPOD"                       "DIP"                        
 [29] "DLRP_Cellinker"              "DLRP_talklr"                
 [31] "DOMINO"                      "DoRothEA"                   
 [33] "DoRothEA-A_CollecTRI"        "DoRothEA-reviews_DoRothEA"  
 [35] "ELM"                         "EMBRACE"                    
 [37] "ENCODE_tf-mirna"             "ENCODE-distal"              
 [39] "ENCODE-proximal"             "ExTRI_CollecTRI"            
 [41] "FANTOM4_DoRothEA"            "Fantom5_LRdb"               
 [43] "GEREDB_CollecTRI"            "GOA_CollecTRI"              
 [45] "Guide2Pharma"                "Guide2Pharma_Cellinker"     
 [47] "Guide2Pharma_LRdb"           "Guide2Pharma_talklr"        
 [49] "HOCOMOCO_DoRothEA"           "HPMR"                       
 [51] "HPMR_Cellinker"              "HPMR_LRdb"                  
 [53] "HPMR_talklr"                 "HPRD"                       
 [55] "HPRD_KEA"                    "HPRD_LRdb"                  
 [57] "HPRD_MIMP"                   "HPRD_talklr"                
 [59] "HPRD-phos"                   "HTRI_CollecTRI"             
 [61] "HTRIdb"                      "HTRIdb_DoRothEA"            
 [63] "HuRI"                        "ICELLNET"                   
 [65] "InnateDB"                    "InnateDB_SignaLink3"        
 [67] "IntAct"                      "IntAct_CollecTRI"           
 [69] "IntAct_DoRothEA"             "iPTMnet"                    
 [71] "iTALK"                       "JASPAR_DoRothEA"            
 [73] "KEA"                         "KEGG-MEDICUS"               
 [75] "Kinexus_KEA"                 "Kirouac2010"                
 [77] "Li2012"                      "Lit-BM-17"                  
 [79] "LncRNADisease"               "LRdb"                       
 [81] "Macrophage"                  "MatrixDB"                   
 [83] "MIMP"                        "miR2Disease"                
 [85] "miRDeathDB"                  "miRecords"                  
 [87] "miRTarBase"                  "MPPI"                       
 [89] "NCI-PID_ProtMapper"          "ncRDeathDB"                 
 [91] "NetPath"                     "NetworKIN_KEA"              
 [93] "NFIRegulomeDB_DoRothEA"      "NRF2ome"                    
 [95] "NTNU.Curated_CollecTRI"      "ORegAnno"                   
 [97] "ORegAnno_DoRothEA"           "Pavlidis2021_CollecTRI"     
 [99] "PAZAR_DoRothEA"              "phosphoELM"                 
[101] "phosphoELM_KEA"              "phosphoELM_MIMP"            
[103] "PhosphoNetworks"             "PhosphoPoint"               
[105] "PhosphoSite"                 "PhosphoSite_KEA"            
[107] "PhosphoSite_MIMP"            "PhosphoSite_noref"          
[109] "PhosphoSite_ProtMapper"      "ProtMapper"                 
[111] "Ramilowski2015"              "Ramilowski2015_Baccin2019"  
[113] "REACH_ProtMapper"            "Reactome_LRdb"              
[115] "Reactome_ProtMapper"         "Reactome_SignaLink3"        
[117] "RegNetwork_DoRothEA"         "ReMap_DoRothEA"             
[119] "RLIMS-P_ProtMapper"          "scConnect"                  
[121] "SignaLink3"                  "SIGNOR"                     
[123] "SIGNOR_CollecTRI"            "SIGNOR_ProtMapper"          
[125] "Sparser_ProtMapper"          "SPIKE"                      
[127] "SPIKE_LC"                    "STRING_talklr"              
[129] "talklr"                      "TCRcuration_SignaLink3"     
[131] "TFactS_CollecTRI"            "TFactS_DoRothEA"            
[133] "TFe_DoRothEA"                "TransmiR"                   
[135] "TRED_DoRothEA"               "TRIP"                       
[137] "TRRD_DoRothEA"               "TRRUST_CollecTRI"           
[139] "TRRUST_DoRothEA"             "UniProt_LRdb"               
[141] "Wang"                        "Wojtowicz2020"              

Code

interactions_hs <-
  import_omnipath_interactions(resources = get_interaction_resources())

Code

interactions_hs$source_genesymbol %>%
  table() %>%
  length()

[1] 5336

Code

interactions_hs$target_genesymbol %>%
  table() %>%
  length()

[1] 6449

Code

interactions_all_mm <- import_all_interactions(organism = 10090) ## 123477 interactions

Code

interactions_all_mm$source_genesymbol %>%
  table() %>%
  length()

[1] 6375

Code

interactions_all_mm$target_genesymbol %>%
  table() %>%
  length()

[1] 14252

Code

print_interactions(head(interactions_all_mm))

Code

(interactions_Onecut3 <- dplyr::filter(
  interactions_all_mm,
  source_genesymbol == "Onecut3" |
    target_genesymbol == "Onecut3"
))

Code

(interactions_Onecut2 <- dplyr::filter(
  interactions_all_mm,
  source_genesymbol == "Onecut2" |
    target_genesymbol == "Onecut2"
))

Code

(interactions_Onecut1 <- dplyr::filter(
  interactions_all_mm,
  source_genesymbol == "Onecut1" |
    target_genesymbol == "Onecut1"
))

Code

(interactions_Nav1 <- dplyr::filter(
  interactions_all_mm,
  source_genesymbol == "Nav1" |
    target_genesymbol == "Nav1"
))

Code

(interactions_Nav2 <- dplyr::filter(
  interactions_all_mm,
  source_genesymbol == "Nav2" |
    target_genesymbol == "Nav2"
))

Code

## We transform the interactions data frame into a graph
OPI_g <- interaction_graph(interactions = interactions_all_mm)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "Onecut1",
  to = "Nav1"
)$res, OPI_g)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "Onecut2",
  to = "Nav1"
)$res, OPI_g)

Code

print_path_es(shortest_paths(OPI_g,
  from = "Onecut1", to = "Nav1",
  output = "epath"
)$epath[[1]], OPI_g)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "Onecut1",
  to = "Nav2"
)$res, OPI_g)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "Onecut2",
  to = "Nav2"
)$res, OPI_g)

Code

print_path_es(shortest_paths(OPI_g,
  from = "Onecut1", to = "Nav2",
  output = "epath"
)$epath[[1]], OPI_g)

Code

interactions_all_hs <- import_all_interactions() ## 164, 710

Code

interactions_all_hs$source_genesymbol %>%
  table() %>%
  length()

[1] 9823

Code

interactions_all_hs$target_genesymbol %>%
  table() %>%
  length()

[1] 17723

Code

print_interactions(head(interactions_all_hs))

Code

(interactions_Onecut3_hs <- dplyr::filter(
  interactions_all_hs,
  source_genesymbol == "ONECUT3" |
    target_genesymbol == "ONECUT3"
))

Code

(interactions_Onecut2_hs <- dplyr::filter(
  interactions_all_hs,
  source_genesymbol == "ONECUT2" |
    target_genesymbol == "ONECUT2"
))

Code

(interactions_Onecut1_hs <- dplyr::filter(
  interactions_all_hs,
  source_genesymbol == "ONECUT1" |
    target_genesymbol == "ONECUT1"
))

Code

(interactions_Nav1_hs <- dplyr::filter(
  interactions_all_hs,
  source_genesymbol == "NAV1" |
    target_genesymbol == "NAV1"
))

Code

(interactions_Nav2_hs <- dplyr::filter(
  interactions_all_hs,
  source_genesymbol == "NAV2" |
    target_genesymbol == "NAV2"
))

Code

## We transform the interactions data frame into a graph
OPI_g <- interaction_graph(interactions = interactions_all_hs)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "ONECUT1",
  to = "NAV2"
)$res, OPI_g)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "ONECUT2",
  to = "NAV2"
)$res, OPI_g)

Code

print_path_vs(all_shortest_paths(OPI_g,
  from = "ONECUT3",
  to = "NAV2"
)$res, OPI_g)

Code

print_path_es(shortest_paths(OPI_g,
  from = "ONECUT1", to = "NAV2",
  output = "epath"
)$epath[[1]], OPI_g)

Code

print_path_es(shortest_paths(OPI_g,
  from = "ONECUT2", to = "NAV2",
  output = "epath"
)$epath[[1]], OPI_g)

Code

print_path_es(shortest_paths(OPI_g,
  from = "ONECUT3", to = "NAV2",
  output = "epath"
)$epath[[1]], OPI_g)

Code

Idents(object = holder_srt$rar2020.srt.neuro, cells = colnames(holder_srt$rar2020.srt.cont.oc2or3)) <- "OC"
markOC3 <- FindMarkers(holder_srt$rar2020.srt.neuro, ident.1 = "OC", test.use = "MAST")
markOC3 %>%
  rownames_to_column(var = "gene") %>%
  write_csv(x = ., file = paste0(tables_dir, "/DEG_from_mm_dev_OC_positive_cells.csv"), col_names = T)

Code

select_to_test <- markOC3 %>%
  filter(p_val_adj < 0.0001, avg_log2FC > 0) %>%
  rownames_to_column(var = "gene") %>%
  .$gene

intersect_neuro2a <- neuro2a %>%
  filter(significant == "yes", gene_name %in% c(select_to_test, "Nav1", "Nav2")) %>%
  select(gene_name, log2FoldChange, padj) %>%
  mutate(gene_name = str_to_upper(gene_name))
intersect_neuro2a %>% write_csv(x = ., file = paste0(tables_dir, "/N2a_KI_Onecut3.csv"), col_names = F)

Code

svCorrDotPLOT <- function(dat) {
  selection <- markOC3 %>%
    rownames_to_column(var = "gene") %>%
    filter(
      p_val_adj < 0.0000000001,
      str_detect(string = gene, pattern = "^mt-", negate = T),
      str_detect(string = gene, pattern = "^Gm", negate = T)
    ) %>%
    top_n(n = 40, wt = avg_log2FC) %>%
    top_n(n = 20, wt = pct.1) %>%
    dplyr::arrange(desc(pct.1)) %>%
    .$gene
  selection2 <- markOC3 %>%
    rownames_to_column(var = "gene") %>%
    filter(
      p_val_adj < 0.0000000001,
      str_detect(string = gene, pattern = "^mt-", negate = T),
      str_detect(string = gene, pattern = "^Gm", negate = T)
    ) %>%
    top_n(n = 25, wt = p_val_adj) %>%
    top_n(n = 20, wt = pct.1) %>%
    top_n(n = 15, wt = avg_log2FC) %>%
    dplyr::arrange(desc(pct.1)) %>%
    .$gene

  srt_onecut_filtered_ages <-
    GetAssayData(dat, "data", "RNA") %>%
    as.data.frame() %>%
    t()
  filt_age_oc_sum <-
    colSums(srt_onecut_filtered_ages) %>%
    .[. > 10] %>%
    names() %>%
    .[. %in% c(
      selection,
      "Onecut1",
      "Onecut2",
      "Onecut3"
    )]
  srt_onecut_filtered_ages %<>% .[, filt_age_oc_sum]

  oc_age_corr_genes <- corrr::correlate(srt_onecut_filtered_ages)

  oc3_age_corr_genes <-
    oc_age_corr_genes %>%
    arrange(-Onecut3) %>%
    select(term, Onecut3, Onecut2)

  threshold.min.pct.Onecut2 <- .3
  threshold.min.pct.OC3 <- .3
  threshold.max.pct.Onecut2 <- .95
  threshold.max.pct.OC3 <- .95

  oc3_age_corr_genes_intersect_2 <-
    intersect(
      x = oc3_age_corr_genes %>%
        slice_min(
          order_by = Onecut2,
          prop = threshold.min.pct.Onecut2
        ),
      y = oc3_age_corr_genes %>%
        slice_min(
          order_by = Onecut3,
          prop = threshold.max.pct.OC3
        )
    )
  highlight_oc3_age_corr_genes <-
    union(
      oc3_age_corr_genes_intersect_2$term,
      selection2
    )
  additional_genes <-
    c(
      "Onecut2", "Pias2", "Tp53", "Trio", "Fas", "Rhoa", "Limk1", "Vegfa", "Bcar1",
      "Ywhab", "Nf1", "Eif4e", "Rb1", "Sp1", "Rps13", "Rps3a", "Traf6", "Fgfr1op",
      "Ifi16", "Irf3", "Emg1", "Myc", "Pten", "Sirt1", "E2f1", "Nr0b2", "Crp", "Erg",
      "Foxa1", "Spi1", "Nfkb1", "Nfkbia", "Pan2", "Eif4a1", "Gfra1", "Stat6", "Agt",
      "Onecut1", "Grb2",
      intersect_neuro2a$gene_name %>%
        str_to_sentence()
    ) %>%
    .[!. %in% highlight_oc3_age_corr_genes] %>%
    unique() %>%
    .[. %in% rownames(dat)]
  plot_dot_oc23_age_corr_genes <-
    DotPlot(
      dat,
      assay = "RNA",
      features = c(additional_genes, highlight_oc3_age_corr_genes),
      group.by = "age"
    ) + RotatedAxis()
  save_plot(
    filename = paste0(plots_dir, "/plot_dot_age_corrr_expression_wtin_OC23.pdf"),
    plot = plot_dot_oc23_age_corr_genes,
    base_height = 6,
    base_aspect_ratio = 2
  )

  return(highlight_oc3_age_corr_genes)
}

# oc_list <- Seurat::SplitObject(holder_srt$rar2020.srt.cont.oc2or3, split.by = "age")

Code

svCorrDotPLOT(holder_srt$rar2020.srt.cont.oc2or3)

[1] "Ptma"    "Onecut2" "Islr2"   "Tcf12"   "Eef2"    "Onecut3"

Code

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       Ubuntu 22.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language en_US:en
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2023-08-18
 pandoc   3.1.3 @ /opt/python/3.8.8/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
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 ape                    5.7-1      2023-03-13 [1] RSPM (R 4.3.0)
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 callr                  3.7.3      2022-11-02 [1] RSPM (R 4.3.0)
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 fitdistrplus           1.1-11     2023-04-25 [1] RSPM (R 4.3.0)
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 GenomeInfoDbData       1.2.10     2023-08-17 [1] RSPM (R 4.3.1)
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 ggbeeswarm             0.7.2      2023-08-17 [1] Github (eclarke/ggbeeswarm@3cf58a9)
 ggplot2              * 3.4.3      2023-08-14 [1] RSPM (R 4.3.0)
 ggprism                1.0.4      2023-08-17 [1] Github (csdaw/ggprism@0e411f4)
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 [1] /opt/R/4.3.1/lib/R/library

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References

Romanov, Roman A., Evgenii O. Tretiakov, Maria Eleni Kastriti, Maja Zupancic, Martin Häring, Solomiia Korchynska, Konstantin Popadin, et al. 2020. “Molecular Design of Hypothalamus Development.” Nature 582 (7811): 246–52. https://doi.org/10.1038/s41586-020-2266-0.