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Single_cell_analysis_with_differential_expression.R
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Single_cell_analysis_with_differential_expression.R
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setwd("/media/owen/Backup Plus/UCSF_Project/02_PROCESSED_DATA/PNOC/ANALYSIS/07_Investigating_Low_Viral_Uptake/")
library(Seurat)
library(MASS)
library(reshape2)
library("FactoMineR")
library("factoextra")
library(heatmaply)
library(ggpubr)
library(DESeq2)
library(glmGamPoi)
library(EnhancedVolcano)
library(BiocParallel)
register(MulticoreParam(12))
library(ggplot2)
library(scales) # needed for oob parameter
library(viridis)
library("tibble")
library(reshape2)
library(ggdendro)
library(gridExtra)
library(gtable)
library(grid)
library(clusterProfiler)
library(enrichplot)
require(DOSE)
library(europepmc)
library(pathview)
library(survival)
library(survminer)
require("survival")
library("forestmodel")
library(devtools)
#install_github("KatrionaGoldmann/volcano3D", force = TRUE)
library(volcano3D)
#install_github("KatrionaGoldmann/volcano3Ddata")
library(volcano3Ddata)
library(volcano3D)
library(kableExtra)
library("ggrepel")
library("plotly")
library("dplyr")
library(knitr)
library(htmltools)
library(htmlTable)
library(htmlwidgets)
library( digest)
library( DT)
library( fs)
library(future)
library(ggrepel)
library(ggtree)
library(parallel)
library(tibble)
library(data.table)
library(speckle)
library(dittoSeq)
library(SCpubr)
library("Nebulosa")
library(MetaVolcanoR)
plan("multicore", workers = 12)
options(future.globals.maxSize = (1014*1024^2)*60)
seurat.data <- readRDS("sc10x.rna.seurat_sorted_cellTypes.rds")
seurat.data <- subset(seurat.data, subset = CellType_Phenotypes != "NA")
seurat.data <- readRDS("Three_way_neoplastic_viral_cell_analysis.rds")
# seurat.data <- subset(seurat.data, subset = Sample_ID != "MV_PD1_604")
seurat.data <- subset(seurat.data, subset=CellType_Phenotypes %in% c("Microglia", "Monocytes", "M1_Macrophages",
"M2_Macrophages", "M0_Macrophages" ))
# # Use the RNA assay to find variable genes
DefaultAssay(seurat.data) = "RNA"
seurat.data = FindVariableFeatures(seurat.data, selection.method = "vst", nfeatures = 2000)
var_genes_from_rna = seurat.data@[email protected]
# # Use the anchors features
# DefaultAssay(seurat.data) = "integrated"
# features = seurat.data@[email protected]
# # # Use the integrated assay (with all genes integrated) to find variable genes
# DefaultAssay(seurat.data) = "integrated"
# seurat.data = FindVariableFeatures(seurat.data, selection.method = "vst", nfeatures = 2000)
# var_genes_from_int = seurat.data@[email protected]
# Plot Variable genes
# Identify the 20 most highly variable genes
top20 <- head(var_genes_from_rna, 50)
#
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seurat.data)
plot2 <- LabelPoints(plot = plot1, points = top20, repel = TRUE)
print(plot2)
SIGNATURES<-list("infection_genes"= c("Slamf1", "Slamf6", "Slamf7", "Slamf8", "Slamf9", "Cd46"))
for(i in seq_along(SIGNATURES)){
seurat.data<- AddModuleScorePerso(seurat.data, assay = "RNA", features = list(SIGNATURES[[i]]), name = names(SIGNATURES)[i])
}
dittoSeq::dittoDimPlot(seurat.data, "CellType_Phenotypes", reduction.use = "umap", color.panel = c("#0062B4FF", "#FFBF47", "#FF2700FF", "mediumseagreen", "sienna4", "violet", "violetred", "violetred4", "#CC313D", "blue", "limegreen", "#3B9AB2","deeppink", "#BF812D"), size = 0.7)
D2 <- dittoSeq::dittoBarPlot(seurat.data, var = "CellType_Phenotypes", group.by = "Sample_ID", split.by = "Treatment", color.panel = c("#0062B4FF", "#FFBF47", "#FF2700FF", "mediumseagreen", "sienna4", "violet", "violetred", "violetred4", "#CC313D", "blue", "limegreen", "#3B9AB2","deeppink", "#BF812D"))
Phagocytic_markers <- c("Ctsb", "Grn", "C1qa")
dittoBoxPlot(sample, "C1qa", group.by = "Treatment")
dittoBoxPlot(seurat.data, "Slamf8", group.by = "Virus_Infection_true", split.by = "CellType_Phenotypes")
dittoBoxPlot(seurat.data, "C1qa", group.by = "Virus_Infection_true", split.by = "CellType_Phenotypes")
sample <- seurat.data
# Use custom colors.
p <- SCpubr::do_AlluvialPlot(sample = sample,
first_group = "Virus_Infection_true",
middle_groups = "CellType_Phenotypes",
last_group = "Treatment", fill.by = "CellType_Phenotypes",
repel = TRUE, use_labels = TRUE,
stratum.fill.conditional = FALSE,
use_geom_flow = FALSE,
alluvium.color = "white",
flow.color = "white",
flip = FALSE,
label.color = "black",
curve_type = "sigmoid",
viridis_color_map = "G",
viridis_direction = -1,
plot.grid = TRUE,
grid.color = "grey75",
grid.type = "dashed",
na.value = "white",
legend.position = "right",
legend.title = "Cell Types")
p
p <- SCpubr::do_ChordDiagramPlot(sample = sample,
from = "CellType_Phenotypes",
to = "Virus_Infection_true",
link.arr.type = "triangle", repel = TRUE)
p
p1 <- SCpubr::do_FeaturePlot(sample = sample,
features = c("C1qa", "C1qb", "C1qc", "C2", "Fcgr2b", "Fcgr3"))
p2 <- SCpubr::do_NebulosaPlot(sample = sample,
features = c("Ctsb", "Grn", "C1qa", "Fcgr2b", "Ighg2c", "Igkc"))
p <- p1 | p2
p
p <- SCpubr::do_BoxPlot(sample = sample,
feature = c(),
use_test = TRUE,
group.by = "CellType_Phenotypes",
comparisons = list(c("Malignant", "Microglia"),
c("Malignant", "M0_Macrophages")),
map_signif_level = FALSE)
p
# Retrieve all dimensionality reductions
getReductions(seurat.data)
phagPlot1 <- dittoDimPlot(seurat.data, "Ctsb", split.by = "Virus_Infection_true")
phagPlot2 <- dittoDimPlot(seurat.data, "Grn", split.by = "Virus_Infection_true")
phagPlot3 <- dittoDimPlot(seurat.data, "C1qa", split.by = "Virus_Infection_true")
phagPlot1 + phagPlot2 + phagPlot3
# Annotating and ordering cells by some meaningful feature(s):
dittoHeatmap(seurat.data, Phagocytic_markers,
scaled.to.max = FALSE,
show_colnames = FALSE,
show_rownames = TRUE,
complex = TRUE,
annot.by = c("Virus_Infection_true", "CellType_Phenotypes"),
order.by = "CellType_Phenotypes")
dittoDotPlot(seurat.data, vars = Phagocytic_markers, group.by = "CellType_Phenotypes", split.by = "Virus_Infection_true")
dittoPlotVarsAcrossGroups(seurat.data, Phagocytic_markers, group.by = "Virus_Infection_true", split.by = "CellType_Phenotypes",
main = "Markers of Mouse Phagocytosis")
dittoDimPlot(seurat.data, infection_genes,
split.by = c("Virus_Infection_true"))
dittoRidgePlot(seurat.data, "C1qa", group.by = "CellType_Phenotypes")
dittoRidgePlot(seurat.data, Phagocytic_markers, group.by = "CellType_Phenotypes", split.by = "Virus_Infection_true",
split.ncol = 1)
# Increase cell size.
p <- SCpubr::do_CorrelationPlot(sample = seurat.data, column_title = "Cell Type Annotation",
group.by = "Treatment",
column_names_rot = 45,
legend.position = "bottom",
cell_size = 12)
p
p1 <- SCpubr::do_CorrelationPlot(sample = sample, column_title = "MV Infection",
group.by = "Virus_Infection",
column_names_rot = 45,
legend.position = "bottom",
cell_size = 60)
p1
genes <- c("Cd274", "Pdcd1", "Arg1", "Ighg2b", "Iglc2", "Igkc", "Ighg2c", "Cd4", "Cd8a", "Cd3e",
"Jchain", "Igha", "Cxcl9", "Cxcl10", "Ccl2", "Ccl7", "Ccl8", "Ccr1", "Ccr5",
"C1qa", "C1qb", "C1qc", "C2", "Fcgr2b", "Fcgr3", "H2-Aa", "H2-Q4", "H2-Ab1", "H2-Eb1",
"Lag3", "Cxcl3", "Ctla4", "Klrd1", "Klrc1", "Xcl1", "Cx3cr1", "Ncr1", "Lyz1", "Cd19", "Cd14",
"Csf1", "Il1b", "Fos", "Jun", "Junb", "Rela", "Relb", "Ptgs2", "Stat3")
# Default parameters.
p <- SCpubr::do_ExpressionHeatmap(sample = seurat.data,
features = genes, flip = FALSE,
group.by = c("CellType_Phenotypes", "Virus_Infection_true", "Treatment"),
viridis_direction = -1,
viridis_color_map = "C")
p
# Seurat sample.
sample <- seurat.data
# Set the identities correctly.
Seurat::Idents(sample) <- sample$Treatment
# Compute DE genes and transform to a tibble.
de_genes <- tibble::tibble(Seurat::FindAllMarkers(object = sample, logfc.threshold = 0.1, return.thresh = 0.01, only.pos = TRUE))
de_genes <- as.data.frame(de_genes)
de_genes$significant <- ifelse(de_genes$p_val_adj < .05, "Significant", NA)
de_genes<-na.omit(de_genes)
# Add more layers of mean expression with group.by.
p <- SCpubr::do_GroupwiseDEPlot(sample = sample,
de_genes = de_genes,
top_genes = 20,
cell_size = 6,
column_title = "Differential expression T cells control vs treated",
row_title_p_values = "P-Values",
row_title_logfc = "FC",
viridis_map_expression = "C",
group.by = c("CellType_Phenotypes",
"Virus_Infection",
"Treatment"),
row_title_expression = c("Cell Type",
"MV",
"Treatment"))
p
my_genes <- FindMarkers(sample, ident.1 = "Treated", ident.2 = "Control", logfc.threshold = 0.1, test.use = "MAST", group.by = "Treatment")
my_genes <- rownames_to_column(my_genes, var = "gene")
EnhancedVolcano(my_genes,
lab = as.character(my_genes$gene),
x = 'avg_log2FC',
y = 'p_val_adj',
title = "DEG Control vs Treated Innate Immune Cells",
pCutoff = 0.05,
FCcutoff = 0,
cutoffLineType = 'twodash',
cutoffLineWidth = 0.8,
pointSize = 2,
labSize = 4,
colAlpha = 1,
labCol = 'black',
labFace = 'bold',
boxedLabels = TRUE,
legendLabels=c('Not sig.','Log (base 2) FC','p-value',
'p-value & Log (base 2) FC'),
legendPosition = 'top',
legendLabSize = 12,
legendIconSize = 2,
drawConnectors = TRUE,
widthConnectors = 0.75)
ggplot(data=my_genes, aes(x=gene, y=avg_log2FC, fill = p_val_adj)) +
geom_bar(stat="identity") +
coord_flip()
head(meta_degs_vote@metaresult, 3)
meta_degs_vote@degfreq
write.csv(de_genes, file="My_de_genes.csv")
de_genes2 <- read.csv("de_genes2.csv")
library(decoupleR)
library(OmnipathR)
library(clusterProfiler)
library(org.Mm.eg.db)
genes.use <- c("Ccl4", "Cx3cr1" , "C1qc" , "Lgmn" , "Jun" , "Atf3" , "Fos" , "Ccl3" , "Trem2" , "C1qb" , "Ly86" , "Junb" , "Ctss" ,
"C1qa", "Tyrobp", "Mafb" , "Ctsl" , "Zfp36" , "Fcer1g" , "Csf1r" , "Cd83" , "Sat1" , "Ctsz" , "Dusp1" , "Plek" , "Ctsd" ,
"Btg2", "Egr1" , "Nfkbia" , "Cd9" , "Ccrl2" , "Selplg" , "Cebpb" , "P2ry12" , "Rhob" , "Gpr34" , "Rgs10" , "Ctsh" , "Fcgr3" ,
"Ier5" , "Laptm5" , "Cyba" , "Tmem119" ,"Tgfbr1" , "Unc93b1" , "Rgs1" , "Grn" , "Lyn" , "Cd300c2" , "Cd81" , "Ctsc" , "Rnase4" ,
"Btg1", "Klf6", "Serinc3" , "Ppp1r15a" ,"Ier2" , "Il1a")
# Compute the grouped GO terms.
# out <- SCpubr::do_GroupedGOTermPlot(genes = genes.use,
# org.db = org.Mm.eg.db)
#
# # Plot the output combined heatmap.
# out$Plots$BP$Combined
#
# # Compute the grouped GO terms.
# out <- SCpubr::do_GroupedGOTermPlot(genes = genes.use,
# org.db = org.Mm.eg.db,
# cluster_rows = FALSE,
# cluster_cols = FALSE)
#
# # Plot the output combined heatmap.
# out$Plots$BP$Combined
#
#
# # Compute the grouped GO terms.
# out <- SCpubr::do_GroupedGOTermPlot(genes = genes.use,
# org.db = org.Mm.eg.db,
# min.overlap = 2,
# flip = FALSE,
# levels.use = c(1, 2, 3, 4),
# colors.use = c("lightblue", "navyblue"))
#
# # Plot the output combined heatmap.
# out$Plots$BP$Combined
# Compute the grouped GO terms.
out <- SCpubr::do_FunctionalAnnotationPlot(genes = de_genes$gene,
org.db = org.Mm.eg.db,
min.overlap = 10,
p.adjust.cutoff = 0.00001,
pAdjustMethod = "BH",
minGSSize = 10,
maxGSSize = 500)
# Retrieve the heatmap.
out$Heatmap
# Retrieve the Bar and Dot plot.
out$BarPlot | out$DotPlot
# Retrieve the Tree plot.
out$TreePlot
library(enrichR)
# Set necessary enrichR global options. This is copied from EnrichR code to avoid having to load the package.
suppressMessages({
options(enrichR.base.address = "https://maayanlab.cloud/Enrichr/")
options(enrichR.live = TRUE)
options(modEnrichR.use = TRUE)
options(enrichR.sites.base.address = "https://maayanlab.cloud/")
options(enrichR.sites = c("Enrichr", "FlyEnrichr", "WormEnrichr", "YeastEnrichr", "FishEnrichr"))
# Set the search to Human genes.
enrichR::setEnrichrSite(site = "Enrichr")
websiteLive <- TRUE
dbs <- enrichR::listEnrichrDbs()
# Get all the possible databases to query.
dbs <- sort(dbs$libraryName)
})
# Choose the dataset to query against.
dbs_use <- c("GO_Biological_Process_2021",
"GO_Cellular_Component_2021",
"Azimuth_Cell_Types_2021")
# List of genes to use as input.
genes <- c("Ctsb", "Grn", "C1qa")
# Retrieve the enriched terms.
enriched_terms <- enrichR::enrichr(genes.use, dbs_use)
# Default plot.
p <- SCpubr::do_TermEnrichmentPlot(enriched_terms = enriched_terms)
p
# Modify font size of the terms.
p1 <- SCpubr::do_TermEnrichmentPlot(enriched_terms = enriched_terms)
p2 <- SCpubr::do_TermEnrichmentPlot(enriched_terms = enriched_terms,
text_labels_size = 6)
p1
p2
library(SeuratWrappers)
# Define your sample and assay.
sample <- seurat.data
assay <- sample@[email protected]
# Retrieve prior knowledge network.
network <- decoupleR::get_progeny(organism = "mouse", top=500)
# Run weighted means algorithm.
activities <- decoupleR::run_wmean(mat = as.matrix(assay),
network = network,
.source = "source",
.targe = "target",
.mor = "weight",
times = 100,
minsize = 5)
# General heatmap.
library(ggdist)
out <- SCpubr::do_PathwayActivityPlot(sample = sample,
group.by = "CellType_Phenotypes",
activities = activities,
split.by = "Virus_Infection_true",
plot_FeaturePlots = TRUE,
plot_GeyserPlots = TRUE)
p <- out$heatmaps$average_scores
p
# Retrieve feature plots.
out <- SCpubr::do_PathwayActivityPlot(sample = sample,
activities = activities,
group.by = "CellType_Phenotypes",
plot_FeaturePlots = TRUE)
p1 <- SCpubr::do_DimPlot(sample)
p2 <- out$feature_plots$EGFR
p <- p1 | p2
p
# SET THE DESIRED ORGANISM HERE
organism = "org.Mm.eg.db"
library(organism, character.only = TRUE)
my_genes <- as.data.frame(my_genes)
my_genes$significant <- ifelse(my_genes$p_val_adj < .05, "Significant", NA)
my_genes<-na.omit(my_genes)
# we want the log2 fold change
original_gene_list <- my_genes$avg_log2FC
# name the vector
names(original_gene_list) <- my_genes$gene
# omit any NA values
gene_list<-na.omit(original_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
gse <- gseGO(geneList=gene_list,
ont ="ALL",
keyType = "SYMBOL",
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
verbose = TRUE,
OrgDb = organism,
pAdjustMethod = "none")
require(DOSE)
dotplot(gse, showCategory=15, split=".sign") + facet_grid(.~.sign)
gseaplot2(gse, geneSetID = 1639, pvalue_table = TRUE, ES_geom = "line", title = gse$Description[1639])
# Seurat sample.
sample <- seurat.data
# Set the identities correctly.
Seurat::Idents(sample) <- sample$CellType_Phenotypes
pbmc.markers <- FindAllMarkers(sample, min.pct = 0.1, logfc.threshold = 1, test.use = "MAST", group.by = "CellType_Phenotypes")
pbmc.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC)
##################################################################
#Subsetting top 100 markers with adjusted p values lower than .05#
##################################################################
top100 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 100, wt = avg_log2FC)
top100pval <- subset(top100, rowSums(top100[5] < 0.05) > 0)
library("clusterProfiler")
library("org.Mm.eg.db")
library("AnnotationHub")
df <- top100pval[,7:6]
dfsample <- split(df$gene,df$cluster)
length(dfsample)
#The output of length(dfsample) returns how many clusters you have
#Here there at 9 clusters (0, 1, 2, 3, 4, 5, 6, 7 and 8)
#I'm sure there's a better way but you have to make a line like below for each cluster
dfsample$Malignant = bitr(dfsample$Malignant, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$Microglia = bitr(dfsample$Microglia, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$NK_cells = bitr(dfsample$NK_cells, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$`Endothelial cells` = bitr(dfsample$Endothelial, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$Monocytes = bitr(dfsample$Monocytes, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$M1_Macrophages = bitr(dfsample$M1_Macrophages, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$`B cells` = bitr(dfsample$`B cells`, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$`CD4+_T_cells` = bitr(dfsample$`CD4+_T_cells`, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$Oligodendrocytes = bitr(dfsample$Oligodendrocytes, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$M2_Macrophages = bitr(dfsample$M2_Macrophages, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$M0_Macrophages = bitr(dfsample$M0_Macrophages, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$Neurons = bitr(dfsample$Neurons, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$Astrocytes = bitr(dfsample$Astrocytes, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
dfsample$`Epithelial cells` = bitr(dfsample$`Epithelial cells`, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
#do the same here, a line like below for each cluster
genelist <- list("Malignant" = dfsample$Malignant$ENTREZID,
"Microglia" = dfsample$Microglia$ENTREZID,
"NK_cells" = dfsample$NK_cells$ENTREZID,
"Endothelial cells" = dfsample$`Endothelial cells`$ENTREZID,
"Monocytes" = dfsample$Monocytes$ENTREZID,
"M1_Macrophages" = dfsample$M1_Macrophages$ENTREZID,
"B cells" = dfsample$`B cells`$ENTREZID,
"CD4+_T_cells" = dfsample$`CD4+_T_cells`$ENTREZID,
"Oligodendrocytes" = dfsample$Oligodendrocytes$ENTREZID,
"M2_Macrophages" = dfsample$M2_Macrophages$ENTREZID,
"M0_Macrophages" = dfsample$M0_Macrophages$ENTREZID,
"Neurons" = dfsample$Neurons$ENTREZID,
"Astrocytes" = dfsample$Astrocytes$ENTREZID,
"Epithelial cells" = dfsample$`Epithelial cells`$ENTREZID
)
GOclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Mm.eg.db")
dotplot(GOclusterplot)
ggplot(top100pval, aes(x = gene, y = avg_log2FC, fill = factor(cluster))) +
geom_col(alpha = 0.8) +
scale_fill_brewer(palette = "Dark2", name = "Differential Expression Clusters") +
theme_bw() +
theme(
legend.position = c(0.8, 0.2),
legend.background = element_blank(),
legend.title = element_blank(),
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.border = element_rect(color = "grey", fill = NA)
) +
labs(x = "Subject", y = "Change from baseline",
title = "Week 4 outcome by treatment")
# Convert gene IDs for gseKEGG function
# We will lose some genes here because not all IDs will be converted
ids<-bitr(names(original_gene_list), fromType = "SYMBOL", toType = "ENTREZID", OrgDb=organism)
# remove duplicate IDS (here I use "SYMBOL", but it should be whatever was selected as keyType)
dedup_ids = ids[!duplicated(ids[c("SYMBOL")]),]
# Create a new dataframe df2 which has only the genes which were successfully mapped using the bitr function above
df2 = my_genes[my_genes$gene %in% dedup_ids$SYMBOL,]
# Create a new column in df2 with the corresponding ENTREZ IDs
df2$Y = dedup_ids$ENTREZID
# Create a vector of the gene unuiverse
kegg_gene_list <- df2$avg_log2FC
# Name vector with ENTREZ ids
names(kegg_gene_list) <- df2$Y
# omit any NA values
kegg_gene_list<-na.omit(kegg_gene_list)
# sort the list in decreasing order (required for clusterProfiler)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)
kegg_organism = "mouse"
kk2 <- gseKEGG(geneList = kegg_gene_list,
organism = kegg_organism,
nPerm = 10000,
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
pAdjustMethod = "none",
keyType = "ncbi-geneid")
dotplot(kk2, showCategory = 15, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign)
my_genes1 <- my_genes[which(abs(my_genes$avg_log2FC) > 0.5 & my_genes$p_val_adj < 0.05),]
genes <- my_genes1$gene
mat<- sample[["RNA"]]@data[genes, ] %>% as.matrix()
## scale the rows
mat<- t(scale(t(mat)))
cluster_anno<- [email protected]$Treatment
# what's the value range in the matrix
quantile(mat, c(0.1, 0.95))
Seurat::PurpleAndYellow()
## make the black color map to 0. the yellow map to highest and the purle map to the lowest
col_fun = circlize::colorRamp2(c(-1, 0, 3), c("#FF00FF", "black", "#FFFF00"))
# ha = HeatmapAnnotation(type = [email protected]$CellType_Phenotypes,
# col = list(type = c("Astrocytes" = "#0062B4FF", "B cells" = "#FFBF47", "CD4+_T_cells" = "#FF2700FF", "Endothelial cells" = "mediumseagreen", "Epithelial cells" = "sienna4",
# "M0_Macrophages" = "violet", "M1_Macrophages" = "violetred", "M2_Macrophages" = "violetred4", "Malignant" = "#CC313D",
# "Microglia" = "blue", "Monocytes" = "limegreen", "Neurons" = "#3B9AB2", "NK_cells" = "deeppink", "Oligodendrocytes" = "#BF812D")))
ha = HeatmapAnnotation(type = [email protected]$Treatment,
col = list(type = c("Treated" = "#0062B4FF", "Control" = "#FFBF47")))
MyCoolHeatmap1 <- Heatmap(mat, name = "Expression",
column_split = factor(cluster_anno),
top_annotation = ha,
cluster_columns = TRUE,
show_column_dend = FALSE,
cluster_column_slices = TRUE,
column_title_gp = gpar(fontsize = 20),
column_gap = unit(0.5, "mm"),
cluster_rows = TRUE,
show_row_dend = FALSE,
col = col_fun,
row_names_gp = gpar(fontsize = 4),
clustering_distance_rows = "euclidean",
clustering_method_rows = "ward.D2",
column_title_rot = 0,
#top_annotation = HeatmapAnnotation(foo = anno_block(gp = gpar(fill = scales::hue_pal()(9)))),
show_column_names = FALSE,
show_heatmap_legend = TRUE,
use_raster = TRUE,
raster_resize_mat = TRUE,
raster_quality = 4)
print(MyCoolHeatmap1)
cellType = c("Malignant" = "#CC313D", "Neurons" = "#3B9AB2", "Endothelial cells" = "mediumseagreen", "Monocytes" = "limegreen", "Microglia" = "blue", "M2_Macrophages" = "violetred4", "M1_Macrophages" = "violetred", "M0_Macrophages" = "violet", "CD4+_T_cells" = "#FF2700FF", "B cells" = "#FFBF47", "Astrocytes" = "#0062B4FF", "Oligodendrocytes" = "#BF812D", "NK_cells" = "deeppink", "Epithelial cells" = "sienna4")
VlnPlot(seurat.data, features = c("Igkc", "H2-Eb1", "Slamf7", "Cd274", "Fcgr2b", "Cd46"), group.by = "CellType_Phenotypes", cols = cellType)
p = VlnPlot(seurat.data, features = c("Igkc", "H2-Eb1", "Slamf7", "Cd274", "Fcgr2b", "Cd46"), group.by = "Treatment", cols = c("#0062B4FF", "#FFBF47"))
p + theme(legend.position = 'right')
p1 = VlnPlot(seurat.data, features = c("Cd274", "Fcer1g"), group.by = "CellType_Phenotypes")
p1 + theme(legend.position = 'right')