Nanostring data analysis
Yingxin Lin
Created on 12 May 2021, last compiled on 20 February, 2023
1 Data
library(readxl)
library(SingleCellExperiment)
library(scater)
library(ggplot2)
library(ggthemes)
library(ggpubr)
library(moon)
library(rcartocolor)
library(gridExtra)
library(RColorBrewer)
library(pheatmap)
library(ggrepel)
library(dplyr)
ggplot2::theme_set(theme_bw() + theme_yx())
theme_black <- function() {
theme_base() +
theme(panel.background = element_rect(fill = "black",
colour = NA),
plot.background = element_rect(fill = "black",
colour = NA),
legend.key = element_rect(fill = "black",
colour = NA),
legend.background = element_rect(fill = "black", color = "black"),
legend.text = element_text(color = "white"),
strip.background = element_rect(fill = "black",
colour = NA),
text = element_text(color = "white"))
}sce_subset <- readRDS("data/NanoStringDaveLin/sce_WT_MOR28.rds")
colData_cartridge_2019 <- colData(sce_subset)
annotation_col <- colData_cartridge_2019[, c("Sex", "Genotype", "Sample.Type", "Age", "OR.Type")]
age_color <- carto_pal(7, "Sunset")
names(age_color) <- names(table(annotation_col$Age))
or_color <- tableau_color_pal("Superfishel Stone")(3)[c(1, 3)]
names(or_color) <- names(table(annotation_col$OR.Type))
Genotype_color <- carto_pal(2, "Bold")[c(1:2)]
names(Genotype_color) <- names(table(annotation_col$Genotype))
names(age_color) <- names(table(annotation_col$Age))
Sex_color <- RColorBrewer::brewer.pal(2, "Set1")
names(Sex_color) <- names(table(annotation_col$Sex))
sample_color <- RColorBrewer::brewer.pal(2, "Set3")
names(sample_color) <- names(table(annotation_col$Sample.Type))
Cluster_color <- carto_pal(2, "Vivid")
names(Cluster_color) <- c(1, 2)
anno_colors <- list(Age = age_color,
Genotype = Genotype_color[c(1:2)],
cluster = Cluster_color[c(1:2)],
Sex = Sex_color[c(1:2)],
Sample.Type = sample_color[c(1:2)],
OR.Type = or_color)2 Subset of WT & MOR28 in 2019 data
sce_subset$cluster <- factor(sce_subset$cluster)
set.seed(2020)
sce_subset <- runPCA(sce_subset)
print("percent of variacne")## [1] "percent of variacne"print(attr(reducedDim(sce_subset), "percentVar"))## [1] 62.0507039 6.3727729 4.3372164 3.5675338 2.8184664 2.4638722
## [7] 2.1677750 1.9433015 1.8248348 1.6659495 1.6232741 1.3039883
## [13] 1.2345340 1.1963799 1.0828636 0.9519746 0.8808929 0.8271117
## [19] 0.7526504 0.5586262 0.3752780set.seed(2020)
sce_subset <- runTSNE(sce_subset, dimred = "PCA")2.1 PCA analysis
df_toPlot <- moon::makeMoonDF(sce_subset)
g1 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = Sex)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1)
g2 <- ggplot(df_toPlot, aes(x = PCA2, y = PCA3, color = Sex)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1)
g3 <- ggplot(df_toPlot, aes(x = PCA3, y = PCA4, color = Sex)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1)
ggarrange(g1, g2, g3, ncol = 3, nrow = 1, align = "hv", common.legend = TRUE)
ggsave("figures/DaveLinNanostring/cartridge_2019_PCA_WT_MOR28_bySex.pdf", width = 10, height = 4)g1 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = Sex)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1)
g2 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = cluster)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$cluster) +
theme(aspect.ratio = 1)
g3 <- ggplot(df_toPlot, aes(x = PCA2, y = PCA3, color = Sex)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1)
g4 <- ggplot(df_toPlot, aes(x = PCA2, y = PCA3, color = cluster)) +
geom_point(size = 4) +
scale_color_manual(values = anno_colors$cluster) +
theme(aspect.ratio = 1)
ggarrange(g1, g2, g3, g4, ncol = 2, nrow = 2, align = "hv")
ggsave("figures/DaveLinNanostring/cartridge_2019_PCA_WT_MOR28_bySex_PCA.pdf", width = 8, height = 7)g1 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Cdh8", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Cdh8")
g2 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Pcdhga6", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Pcdhga6")
g3 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Pcdh17", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Pcdh17")
g4 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Pcdh19", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Pcdh19")
ggarrange(g1, g2, g3, g4, ncol = 2, nrow = 2, align = "hv")
ggsave("figures/DaveLinNanostring/cartridge_2019_PCA_WT_MOR28_PCA_selected.pdf", width = 8, height = 7)2.1.1 Genes related to PC2 (Cluster 2)
g1 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Actb", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Actb")
g2 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Fat1", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Fat1")
g3 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Foxg1", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Foxg1")
g4 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Flrt3", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Flrt3")
ggarrange(g1, g2, g3, g4, ncol = 2, nrow = 2, align = "hv")
g1 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Dsg3", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Dsg3")
g2 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Robo3", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Robo3")
g3 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Cdh20", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Cdh20")
g4 <- ggplot(df_toPlot, aes(x = PCA1, y = PCA2, color = logcounts(sce_subset)["Dscam", ])) +
geom_point(size = 4) +
scale_color_viridis_c() +
theme(aspect.ratio = 1) +
labs(color = "", title = "Dscam")
ggarrange(g1, g2, g3, g4, ncol = 2, nrow = 2, align = "hv")
Correlation of genes with PC2.
round(sort(cor(t(logcounts(sce_subset)[, sce_subset$cluster == 2]),
df_toPlot$PCA2[sce_subset$cluster == 2])[, 1]), 2)## Dsg3 Robo3 Cdh20 Dscam Cdh12
## -0.49 -0.32 -0.27 -0.22 -0.21
## Olfr151 Dsg1b Dsg1a Epha5 Polr2a
## -0.20 -0.19 -0.19 -0.16 -0.15
## PcdhgC4 Olfr1507 Cdh5 CNGA2 AdCy3
## -0.10 -0.10 -0.09 -0.08 -0.07
## NeuroD1 Pcdh17 Gfp CNGA4 Olfr160
## -0.07 -0.06 -0.05 -0.05 -0.03
## Cdhr5 mcam Cdh3 Cdh8 Pcdhb21
## -0.03 -0.03 -0.01 0.01 0.05
## Omp-2 Cdh13 O-MACS Cdhr2 Dsg4
## 0.05 0.06 0.07 0.10 0.10
## Fat2 Nfasc Pcdhb8 Itga5 Pcdha9
## 0.11 0.11 0.12 0.12 0.14
## Pcdha5 Cdh9 Pcdh20 Slc17ac/Vglut2 Cdh15
## 0.15 0.15 0.15 0.16 0.18
## Dcc Pcdh9 Cdh19 Gfap Pcdha8
## 0.19 0.20 0.20 0.20 0.21
## Sema3a Itgb8 Mash1 Ntn1 Cdh22
## 0.21 0.22 0.23 0.23 0.24
## Pcdhb7 Pcdha4 Ret Pcdha3 Cdh10
## 0.24 0.25 0.25 0.26 0.26
## cre Pcdhga8 Pcdhb22 Pcdhb9 Efna5
## 0.27 0.27 0.27 0.28 0.28
## Celsr2 Cdh23 Pcdhb10 Pcdha6 Pcdh19
## 0.28 0.29 0.30 0.30 0.31
## Slit1 Pcdhb3 Cdh7 Pcdh12 Pcdhgb6
## 0.31 0.32 0.33 0.33 0.34
## Pcdhb15 Cdh11 Pcdhgb7 GnaI Pcdha2
## 0.35 0.35 0.36 0.36 0.37
## Chl1 PcdhgC5 Lrrc4c Alcam L1cam
## 0.37 0.38 0.38 0.38 0.38
## Cntn1 Pcdha11 tomato Pcdh8 Slit3
## 0.39 0.40 0.40 0.40 0.41
## Itga8 Pcdh15 Omp-1 Pcdhb1 Pcdhac1
## 0.41 0.41 0.41 0.42 0.43
## Stmn2 Fat4 Pcdh10 Pcdhb2 Pcdhga12
## 0.43 0.43 0.43 0.44 0.44
## Cdh6 Lgi1 Boc Itga2b Pcdhb20
## 0.44 0.44 0.44 0.45 0.45
## Pcdhgb4 Cdh16 gng8 sema4D Pcdhga7
## 0.45 0.46 0.47 0.47 0.47
## olfr713 Cdh17 Pcdhb5 Pcdhb11 Dsg2
## 0.48 0.49 0.49 0.49 0.51
## Pcdhac2 Pcdhgb1 Pcdh18 Sema3f Pcdh7
## 0.51 0.52 0.52 0.52 0.53
## Cdh18 Pcdhb13 Cntn2 Cdh24 Pcdhga2
## 0.53 0.54 0.54 0.54 0.54
## Pcdha12 Robo1 Nrp1 Pcdhga3 Igf2
## 0.55 0.56 0.57 0.57 0.57
## Dsc3 Pcdh11x Itgb3 Cdhr1 Tubb3
## 0.58 0.58 0.58 0.58 0.58
## Pcdhb19 Pcdhb18 Pcdhb12 Nrcam Pcdha10
## 0.58 0.58 0.59 0.59 0.59
## Pcdhgb5 Pcdhga1 PcdhgC3 Pcdha1 Pcdhgb2
## 0.59 0.60 0.60 0.60 0.60
## Lgi2 Pcdha7 Dsc1 Pcdh1 olfr17
## 0.60 0.61 0.61 0.61 0.61
## Igf1 Pcdhga4 Cdh4 Pcdhb14 Pcdhga9
## 0.62 0.62 0.62 0.62 0.62
## Cdh2 Nqo1 Pcdhga5 Hprt Pcdhgb8
## 0.62 0.65 0.65 0.66 0.66
## Cdhr3 Dsc2 Ncam1 Itgb5 Plxna1
## 0.66 0.66 0.67 0.67 0.68
## Pcdhga11 Olfr1508 Igf1R Ncam2 Mapk1
## 0.68 0.68 0.70 0.70 0.70
## Clstn2 Slit2 Rpl13a Itgb1 Clstn1
## 0.71 0.71 0.72 0.72 0.72
## Pcdhga6 Pcdhb6 Nrp2 Dchs1 Krt8
## 0.72 0.72 0.73 0.74 0.74
## Cdh26 gap43 Syp Gapdh Pcdhb16
## 0.75 0.75 0.76 0.76 0.76
## Clstn3 Pcdhb17 Pcdhb4 ItgB6 Notch 2
## 0.76 0.76 0.77 0.77 0.77
## Flrt2 Celsr3 Pcdhga10 Robo2 Big2/cntn4
## 0.78 0.78 0.78 0.78 0.78
## Eif4a2 Itgav Celsr1 Fat3 Cdh1
## 0.79 0.80 0.80 0.81 0.82
## B2m Flrt3 Foxg1 Fat1 Actb
## 0.82 0.83 0.86 0.88 0.892.2 DE analysis between Cluster
library(limma)
x <- logcounts(sce_subset)
design <- model.matrix(~ sce_subset$cluster)
# contr.matrix <- makeContrasts(
# cellTypes = cluster2 - cluster1,
# levels = colnames(design))
vfit <- lmFit(x, design, trend = TRUE, robust = TRUE)
#vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
efit <- eBayes(vfit)
top_cluster_genes <- topTable(efit, n = Inf)
DT::datatable(round(top_cluster_genes, 3))top_cluster_genes$name <- rownames(top_cluster_genes)
ggplot(top_cluster_genes, aes(x = logFC, y = -log10(P.Value))) +
geom_point() +
geom_point(data = top_cluster_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05),
aes(x = logFC, y = -log10(P.Value)), color = "red") +
geom_text_repel(data = top_cluster_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05),
aes(x = logFC, y = -log10(P.Value), label = name)) +
theme(aspect.ratio = 1)
ggsave("figures/DaveLinNanostring/cartridge_2019_WT_MOR28_byCluster_volcano.pdf",
width = 9, height = 7)
selected_genes <- top_cluster_genes %>% dplyr::filter(abs(logFC) > 4,
adj.P.Val < 0.01) %>% rownames()
rownames(df_toPlot) <- colnames(sce_subset)
pheatmap(logcounts(sce_subset)[selected_genes, order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("cluster", "Sex"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_byCluster_heatmap_selected.pdf",
width = 8,
height = 6,
#cluster_cols = FALSE,
border_color = NA)
hclust_res <- hclust(dist(t(logcounts(sce_subset))))
pheatmap(logcounts(sce_subset)[, order(sce_subset$Sex, hclust_res$order)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("Sex", "cluster"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_all.pdf",
width = 8,
height = 20,
#cluster_cols = FALSE,
border_color = NA)
2.3 DE analysis between sex
x <- logcounts(sce_subset)
design <- model.matrix(~ sce_subset$Sex)
# contr.matrix <- makeContrasts(
# cellTypes = cluster2 - cluster1,
# levels = colnames(design))
vfit <- lmFit(x, design, trend = TRUE, robust = TRUE)
#vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
efit <- eBayes(vfit)
top_sex_genes <- topTable(efit, n = Inf)
DT::datatable(round(top_sex_genes, 3))top_sex_genes$name <- rownames(top_sex_genes)
ggplot(top_sex_genes, aes(x = logFC, y = -log10(P.Value))) +
geom_point() +
geom_point(data = top_sex_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05),
aes(x = logFC, y = -log10(P.Value)), color = "red") +
geom_text_repel(data = top_sex_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05),
aes(x = logFC, y = -log10(P.Value), label = name)) +
theme(aspect.ratio = 1)
ggsave("figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_volcano.pdf", width = 9, height = 7)
g <- lapply(top_sex_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05) %>% rownames(),
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x)
})
ggarrange(plotlist = g, ncol = 4, nrow = 2, align = "hv", common.legend = TRUE)
ggsave("figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_boxplot.pdf", width = 10, height = 8)
g <- lapply(top_sex_genes %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.05) %>% rownames(),
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x) + facet_wrap(~cluster)
})
ggarrange(plotlist = g, ncol = 2, nrow = 4, align = "hv", common.legend = TRUE)
ggsave("figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySexCluster_boxplot.pdf",
width = 10, height = 12)selected_genes <- top_sex_genes %>% dplyr::filter(abs(logFC) > 0.5, P.Value < 0.05) %>% rownames()
rownames(df_toPlot) <- colnames(sce_subset)
pheatmap(logcounts(sce_subset)[selected_genes, order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, "Sex", drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_selected.pdf",
width = 8,
height = 6,
cluster_cols = FALSE,
border_color = NA)
hclust_res <- hclust(dist(t(logcounts(sce_subset))))
pheatmap(logcounts(sce_subset)[, order(sce_subset$Sex, hclust_res$order)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, "Sex", drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_all.pdf",
width = 8,
height = 20,
cluster_cols = FALSE,
border_color = NA)
2.4 DE analysis between sex + cluster
x <- logcounts(sce_subset)
design <- model.matrix(~ sce_subset$Sex + sce_subset$cluster)
# contr.matrix <- makeContrasts(
# cellTypes = cluster2 - cluster1,
# levels = colnames(design))
vfit <- lmFit(x, design, trend = TRUE, robust = TRUE)
#vfit <- contrasts.fit(vfit, contrasts = contr.matrix)
efit <- eBayes(vfit)
top_sex_genes_model2 <- topTable(efit, n = Inf, coef = 2)
DT::datatable(round(top_sex_genes_model2, 3))g <- lapply(top_sex_genes_model2 %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.1) %>%
rownames(),
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x)
})
ggarrange(plotlist = g, ncol = 4, nrow = 2, align = "hv", common.legend = TRUE)
g <- lapply(top_sex_genes_model2 %>% dplyr::filter(abs(logFC) > 1, P.Value < 0.1) %>%
rownames(),
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x) + facet_wrap(~cluster)
})
ggarrange(plotlist = g, ncol = 4, nrow = 2, align = "hv", common.legend = TRUE)
2.5 Selected gene visualisation
selected_genes <- c("Pcdhga6", "Pcdhga11", "Pcdh1", "Pcdh7", "Pcdh9")
g <- lapply(selected_genes,
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x)
})
ggarrange(plotlist = g, ncol = 3, nrow = 2, align = "hv", common.legend = TRUE)
g <- lapply(selected_genes,
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x) + facet_wrap(~cluster)
})
ggarrange(plotlist = g, ncol = 2, nrow = 3, align = "hv", common.legend = TRUE)
hclust_res <- hclust(dist(t(logcounts(sce_subset)[selected_genes, ])))
pheatmap(logcounts(sce_subset)[selected_genes,
order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("Sex", "cluster"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_all.pdf",
width = 8,
height = 20,
cluster_rows = FALSE,
# cluster_cols = FALSE,
border_color = NA)
selected_genes <- c("Pcdh8", "Pcdh9", "Pcdhga6", "Pcdh17", "Pcdhga8",
"Cdh9", "Pcdhga11")
g <- lapply(selected_genes,
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x)
})
ggarrange(plotlist = g, ncol = 4, nrow = 2, align = "hv", common.legend = TRUE)
g <- lapply(selected_genes,
function(x) {
ggplot(df_toPlot, aes(x = Sex, y = logcounts(sce_subset)[x, ], color = Sex)) +
geom_boxplot() +
scale_color_manual(values = anno_colors$Sex) +
theme(aspect.ratio = 1) +
ylab(x) + facet_wrap(~cluster)
})
ggarrange(plotlist = g, ncol = 2, nrow = 4, align = "hv", common.legend = TRUE)
hclust_res <- hclust(dist(t(logcounts(sce_subset)[selected_genes, ])))
pheatmap(logcounts(sce_subset)[selected_genes,
order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("Sex", "cluster"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
#file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_all.pdf",
width = 8,
height = 20,
cluster_rows = FALSE,
# cluster_cols = FALSE,
border_color = NA)
pheatmap(logcounts(sce_subset)[grep("Pcdh|Cdh|Cels", rownames(sce_subset), value = TRUE), order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("Sex"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
# file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_selected_cadherins.pdf",
width = 8,
height = 15,
cluster_cols = FALSE,
border_color = NA)
pheatmap(logcounts(sce_subset)[grep("Pcdh|Cdh|Cels", rownames(sce_subset), value = TRUE), order(sce_subset$Sex)],
clustering_method = "ward.D2",
annotation_col = df_toPlot[, c("Sex", "cluster"), drop = FALSE],
annotation_colors = anno_colors,
# annotation_row = annotation_col,
# main = "Single cell vs Bulk correlation",
# file = "figures/DaveLinNanostring/cartridge_2019_WT_MOR28_bySex_heatmap_selected_cadherins_withCluster.pdf",
width = 8,
height = 15,
#cluster_cols = FALSE,
border_color = NA)
3 Session Info
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] limma_3.52.4 dplyr_1.0.10
## [3] ggrepel_0.9.1 pheatmap_1.0.12
## [5] RColorBrewer_1.1-3 gridExtra_2.3
## [7] rcartocolor_2.0.0 moon_0.1.0
## [9] ggpubr_0.4.0 ggthemes_4.2.4
## [11] scater_1.24.0 ggplot2_3.4.0
## [13] scuttle_1.6.3 SingleCellExperiment_1.18.1
## [15] SummarizedExperiment_1.26.1 Biobase_2.56.0
## [17] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4
## [19] IRanges_2.30.1 S4Vectors_0.34.0
## [21] BiocGenerics_0.42.0 MatrixGenerics_1.8.1
## [23] matrixStats_0.62.0 readxl_1.4.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 tools_4.2.1
## [3] backports_1.4.1 bslib_0.4.0
## [5] DT_0.26 utf8_1.2.2
## [7] R6_2.5.1 irlba_2.3.5.1
## [9] vipor_0.4.5 DBI_1.1.3
## [11] colorspace_2.0-3 withr_2.5.0
## [13] tidyselect_1.2.0 compiler_4.2.1
## [15] textshaping_0.3.6 cli_3.4.1
## [17] BiocNeighbors_1.14.0 DelayedArray_0.22.0
## [19] labeling_0.4.2 sass_0.4.2
## [21] scales_1.2.1 systemfonts_1.0.4
## [23] stringr_1.4.1 digest_0.6.30
## [25] rmarkdown_2.17 XVector_0.36.0
## [27] pkgconfig_2.0.3 htmltools_0.5.3
## [29] sparseMatrixStats_1.8.0 highr_0.9
## [31] fastmap_1.1.0 htmlwidgets_1.5.4
## [33] rlang_1.0.6 rstudioapi_0.14
## [35] DelayedMatrixStats_1.18.2 farver_2.1.1
## [37] jquerylib_0.1.4 generics_0.1.3
## [39] jsonlite_1.8.2 crosstalk_1.2.0
## [41] BiocParallel_1.30.4 car_3.1-1
## [43] RCurl_1.98-1.9 magrittr_2.0.3
## [45] BiocSingular_1.12.0 GenomeInfoDbData_1.2.8
## [47] Matrix_1.5-1 Rcpp_1.0.9
## [49] ggbeeswarm_0.6.0 munsell_0.5.0
## [51] fansi_1.0.3 abind_1.4-5
## [53] viridis_0.6.2 lifecycle_1.0.3
## [55] stringi_1.7.8 yaml_2.3.6
## [57] carData_3.0-5 zlibbioc_1.42.0
## [59] Rtsne_0.16 grid_4.2.1
## [61] parallel_4.2.1 lattice_0.20-45
## [63] cowplot_1.1.1 beachmat_2.12.0
## [65] knitr_1.40 pillar_1.8.1
## [67] ggsignif_0.6.4 codetools_0.2-18
## [69] ScaledMatrix_1.4.1 glue_1.6.2
## [71] evaluate_0.17 vctrs_0.5.1
## [73] cellranger_1.1.0 gtable_0.3.1
## [75] purrr_0.3.5 tidyr_1.2.1
## [77] assertthat_0.2.1 cachem_1.0.6
## [79] xfun_0.34 rsvd_1.0.5
## [81] broom_1.0.1 rstatix_0.7.1
## [83] ragg_1.2.3 viridisLite_0.4.1
## [85] tibble_3.1.8 beeswarm_0.4.0