GSE262787

Efferocytosis

UC MSC vs. AKI

cts <- as.data.frame(counts[rownames(IP_ADSC_CTRL), ])

cts <- cts[, c(5:12)]

cts <- t(cts)
cts <- scale(cts, center=T, scale = T)
cts <- t(cts)



annot_col <- data.frame(row.names = colnames(cts), status = factor(c(rep("UC_MSC", 4), rep("AKI", 4))))

cols <- c("blue", "red")
names(cols) <- c("UC_MSC", "AKI")
annot_colors <- list(status = cols)

library(viridis)
## Loading required package: viridisLite
pheatmap::pheatmap(cts, cluster_cols = F, cluster_rows = T, show_rownames = T,
                   show_colnames = T, annotation_col = annot_col,
                   annotation_colors = annot_colors,
                   col = hcl.colors(100, "Purple-Green",rev=F),
                   main = "DE Efferocytosis UC MSC v AKI\nP.Value <= 0.05",
                   cutree_cols = 2)

pFUS UC MSC vs AKI

Does pFUS elicit a stronger Efferocytosis response?

Note this analysis does not subtract genes common to UC_MSC vs AKI.

cts <- as.data.frame(counts[rownames(IP_ADSC_CTRL), ])

cts <- cts[, c(1:4, 9:12)]

cts <- t(cts)
cts <- scale(cts, center=T, scale = T)
cts <- t(cts)



annot_col <- data.frame(row.names = colnames(cts), status = factor(c(rep("pFUS_UC_MSC", 4), rep("AKI", 4))))

cols <- c("blue", "red")
names(cols) <- c("pFUS_UC_MSC", "AKI")
annot_colors <- list(status = cols)

library(viridis)

pheatmap::pheatmap(cts, cluster_cols = F, cluster_rows = T, show_rownames = T,
                   show_colnames = T, annotation_col = annot_col,
                   annotation_colors = annot_colors,
                   col = hcl.colors(100, "Purple-Green",rev=F),
                   main = "DE Efferocytosis pFUS UC MSC v AKI\nP.Value <= 0.05",
                   cutree_cols = 2)

Very good result by adding pFUS. Heatmap filtering by adj.P.Val <= 0.01

cts <- as.data.frame(counts[rownames(IP_ADSC_CTRL)[which(IP_ADSC_CTRL$adj.P.Val <= 0.01)], ])

cts <- cts[, c(1:4, 9:12)]

cts <- t(cts)
cts <- scale(cts, center=T, scale = T)
cts <- t(cts)



annot_col <- data.frame(row.names = colnames(cts), status = factor(c(rep("pFUS_UC_MSC", 4), rep("AKI", 4))))

cols <- c("blue", "red")
names(cols) <- c("pFUS_UC_MSC", "AKI")
annot_colors <- list(status = cols)

library(viridis)

pheatmap::pheatmap(cts, cluster_cols = F, cluster_rows = T, show_rownames = T,
                   show_colnames = T, annotation_col = annot_col,
                   annotation_colors = annot_colors,
                   col = hcl.colors(100, "Purple-Green",rev=F),
                   main = "DE Efferocytosis pFUS UC MSC v AKI\nadj.P.Val <= 0.01",
                   cutree_cols = 2)

Fibrosis

UC MSC vs. AKI

cts <- as.data.frame(counts[rownames(IP_ADSC_CTRL), ])

cts <- cts[, c(5:12)]

cts <- t(cts)
cts <- scale(cts, center=T, scale = T)
cts <- t(cts)



annot_col <- data.frame(row.names = colnames(cts), status = factor(c(rep("UC_MSC", 4), rep("AKI", 4))))

cols <- c("blue", "red")
names(cols) <- c("UC_MSC", "AKI")
annot_colors <- list(status = cols)

library(viridis)

pheatmap::pheatmap(cts, cluster_cols = F, cluster_rows = T, show_rownames = T,
                   show_colnames = T, annotation_col = annot_col,
                   annotation_colors = annot_colors,
                   col = hcl.colors(100, "Purple-Green",rev=F),
                   main = "DE Fibrosis UC MSC v AKI\nP.Value <= 0.05",
                   cutree_cols = 2)

pFUS UC MSC vs AKI

Does pFUS elicit a stronger Fibrosis response?

Note this analysis does not subtract genes common to UC_MSC vs AKI.

cts <- as.data.frame(counts[rownames(IP_ADSC_CTRL), ])

cts <- cts[, c(1:4, 9:12)]

cts <- t(cts)
cts <- scale(cts, center=T, scale = T)
cts <- t(cts)



annot_col <- data.frame(row.names = colnames(cts), status = factor(c(rep("pFUS_UC_MSC", 4), rep("AKI", 4))))

cols <- c("blue", "red")
names(cols) <- c("pFUS_UC_MSC", "AKI")
annot_colors <- list(status = cols)

library(viridis)

pheatmap::pheatmap(cts, cluster_cols = F, cluster_rows = T, show_rownames = T,
                   show_colnames = T, annotation_col = annot_col,
                   annotation_colors = annot_colors,
                   col = hcl.colors(100, "Purple-Green",rev=F),
                   main = "DE Fibrosis pFUS UC MSC v AKI\nP.Value <= 0.05",
                   cutree_cols = 2)