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Data_Comparisons.Rmd
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---
title: "WBM Comparisons"
author: "D. Ford Hannum Jr."
date: "3/25/2021"
output:
html_document:
toc: true
toc_depth: 3
number_sections: false
theme: united
highlight: tango
---
```{r setup, include=TRUE, message=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE)
library(Seurat)
library(ggplot2)
library(data.table)
library(MAST)
library(SingleR)
library(dplyr)
library(tidyr)
library(limma)
library(scRNAseq)
library(ggpubr)
library(xlsx)
# devtools::install_github('jokergoo/ComplexHeatmap')
# library(ComplexHeatmap)
```
# LOADING DATA
```{r}
wbm <- readRDS('./data/v3/mpl.migr1.121420-2.rds')
table(wbm$Condition2)
wbm$figure.labels <- ifelse(wbm$v7.labels %in% c('MEP/MCP','MEP/ERP'),
ifelse(wbm$v7.labels == 'MEP/MCP', 'MkP','MEP'),
as.character(wbm$v7.labels))
wbm$figure.labels <- ifelse(wbm$figure.labels == 'Tcell', 'T-cell',
ifelse(wbm$figure.labels == 'Bcell','B-cell',
ifelse(wbm$figure.labels == 'Bcell-Prog',
'B-cell Prog.', wbm$figure.labels)))
wbm$UMAP1 <- wbm@reductions$umap@cell.embeddings[,1]
wbm$UMAP2 <- wbm@reductions$umap@cell.embeddings[,2]
wbm$figure.labels2 <- ifelse(wbm$figure.labels != 'MK',
as.character(wbm$figure.labels),
ifelse(wbm$UMAP1 < 0, 'MK-1','MK-2'))
wbm$figure.labels2 <- ifelse(wbm$figure.labels != 'Mono/Macro',
as.character(wbm$figure.labels2),
ifelse(wbm$UMAP1 < -5, 'Mono/Macro-1','Mono/Macro-2'))
wbm$figure.labels <- factor(wbm$figure.labels,
levels = levels(as.factor(wbm$figure.labels))[c(5,3,1,2,10,9,4,6,8,7)])
Idents(wbm) <- wbm$figure.labels
table(Idents(wbm))
mk1.cells <- rownames(wbm@meta.data[wbm$figure.labels == 'MK-1',])
mep1.cells <- rownames(wbm@meta.data[wbm$figure.labels == 'MkP',])
wbm$figure.labels3 <- ifelse(wbm$figure.labels2 %in% c('Mono/Macro-1','Mono/Macro-2'),
'Mono/Macro', as.character(wbm$figure.labels2))
color_pal <- c("#0072B2", "#CC79A7", "#009E73",'black',"#999999",
"#E69F00", "#56B4E9","#D55E00", 'limegreen', 'violet')
color_pal2 <- c(color_pal, 'red')
wbm$Condition3 <- ifelse(wbm$Condition2 == 'Migr1', 'MigR1',
ifelse(wbm$Condition2 == 'enrMigr1', 'CD41+ enr. MigR1',
ifelse(wbm$Condition2 == 'Mpl', 'MPLW515L', 'CD41+ enr. MPLW515L')))
wbm$Condition3 <- factor(wbm$Condition3,
levels = levels(as.factor(wbm$Condition3))[c(3,4,1,2)])
wbm$Experiment <- ifelse(wbm$Condition2 %in% c('enrMigr1','Migr1'), 'MigR1','MPL')
```
# Ref from Qianyi
```{r}
# x <- read.csv('./data/hum_ref_wbm/celltype.csv')
# summary(as.factor(x$type))
```
# Psaila_et_al
Cannot seem to find the right clustering to match the paper though the UMAP coordinates do align.
```{r}
# load('./data/comparison_data/GSE144568/Psaila_et_al.rdata')
# names(attributes(Psaila_et_al))
#
# x <- attributes(Psaila_et_al)$umap.result
#
# clusters <- attributes(Psaila_et_al)$sc.clusters
#
# x$cluster1 <- clusters$louvain_cluster
# x$cluster2 <- clusters$merged_louvain
#
# p <- ggplot(x, aes(x = UMAP1, y = UMAP2, color = cluster2)) +
# geom_point()
```
```{r}
# load('./data/comparison_data/GSE144568/GSE144568_cell_information.rdata')
# load('./data/comparison_data/GSE144568/GSE144568_UMI_count.rdata')
```
# HCL
```{r}
hcl <- read.table('../CZI_Ovary/data/HCL_data/HCL_centroids.txt', sep = '\t')
hcl.genes <- rownames(hcl)
comp.list <- read.table('../mouse.human.orthologs.unique.tsv', sep = '\t', header = T)
# head(comp.list)
dim(comp.list[comp.list$Human.gene.name %in% hcl.genes,])
h.list <- comp.list[comp.list$Human.gene.name %in% hcl.genes,]
m.list <- comp.list[comp.list$Gene.name %in% rownames(wbm),]
dim(m.list)
dim(h.list)
length(intersect(rownames(m.list), rownames(h.list)))
int.list <- m.list[rownames(m.list)%in% rownames(h.list),]
dim(int.list)
int.list <- int.list[,c('Human.gene.name','Gene.name')]
int.list <- distinct(int.list)
dim(int.list)
Idents(wbm) <- wbm$figure.labels3
av <- AverageExpression(wbm)
av <- av$RNA
int.list2 <- int.list[!duplicated(int.list$Human.gene.name),]
dim(int.list2)
int.list2 <- int.list2[!duplicated(int.list2$Gene.name),]
dim(int.list2)
av <- av[rownames(av) %in% int.list2$Gene.name,]
hcl <- hcl[rownames(hcl) %in% int.list2$Human.gene.name,]
av2 <- av
av <- av2
rownames(int.list2) <- int.list2$Gene.name
av <- merge(av,int.list2, by = 0)
rownames(av) <- av$Human.gene.name
av <- av[,2:12]
mrg <- merge(hcl, av, by = 0)
dim(mrg)
rownames(mrg) <- mrg$Row.names
mrg <- mrg[,-1]
# comp.list
summary(rownames(hcl) %in% toupper(rownames(wbm)))
int.genes <- rownames(hcl)[rownames(hcl) %in% toupper(rownames(wbm))]
# summary(int.genes %in% toupper(VariableFeatures(wbm)))
summary(toupper(VariableFeatures(wbm)) %in% rownames(hcl))
hv.overlap.genes <- unique(comp.list[comp.list$Gene.name %in%
VariableFeatures(wbm),]$Human.gene.name)
```
## All Genes
### Top 3 Hits
```{r}
mrg <- merge(hcl, av, by = 0)
dim(mrg)
rownames(mrg) <- mrg$Row.names
mrg <- mrg[,-1]
```
```{r}
temp.cor <- cor(mrg, method = 'spearman')
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
# Only keeping HCL centroids that are a top3 hit for our centroids
corr.out <- split(temp.cor, f = temp.cor$Cluster2)
top3.hits <- as.data.frame(matrix(ncol = 3))
colnames(top3.hits) <- colnames(corr.out[[1]])
for (i in 1:length(corr.out)){
temp <- corr.out[[i]][order(corr.out[[i]]$Correlation, decreasing = T),][1:3,]
top3.hits <- rbind(top3.hits,temp)
}
top3.hits <- top3.hits[-1,c(3,1,2)]
colnames(top3.hits) <- c('Our Clusters','HCL Cluster','Correlation')
rownames(top3.hits) <- 1:length(rownames(top3.hits))
temp.cor <- temp.cor[temp.cor$Cluster1 %in% top3.hits$`HCL Cluster`,]
temp.cor$`HCL Clusters` <- factor(temp.cor$Cluster1, levels = unique(top3.hits$`HCL Cluster`))
###
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster2[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster2 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = `HCL Clusters`, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .4) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 6)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all genes')
#
# ggplot(temp.cor, aes(x = hcl.clusters2, y = Cluster2, fill = Correlation)) +
# geom_tile() + coord_flip() +
# scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .4) +
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
# xlab('HCL Centroids') + ylab('Our Samples') +
# geom_text(aes(label = cor.label), size = 3) +
# ggtitle('HCL Comparison using highly variable genes')
```
### Interesting comparisons in Hcl
#### MK
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('Megak', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('Megak', hcl.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 6)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### Erythrocyte
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('Eryth', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('Eryth', hcl.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### Myeloid Progenitor
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('prog', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('prog', hcl.exp, ignore.case = T)]
mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### Monocyte
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('monocyte', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('monocyte', hcl.exp, ignore.case = T)]
# mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### Macrophage
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('macrophage', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('macrophage', hcl.exp, ignore.case = T)]
# mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### T-cell
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('t.cell', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('t.cell', hcl.exp, ignore.case = T)]
# mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all matching genes')
```
#### B-cell
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('b.cell', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('b.cell', hcl.exp, ignore.case = T)]
# mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using HV genes')
```
#### Prog
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('progen', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('progen', hcl.exp, ignore.case = T)]
# mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using all genes genes')
```
## Highly Variable Genes
### Top 3 Hits
```{r}
mrg <- mrg[rownames(mrg) %in% hv.overlap.genes,]
temp.cor <- cor(mrg, method = 'spearman')
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
# Only keeping HCL centroids that are a top3 hit for our centroids
corr.out <- split(temp.cor, f = temp.cor$Cluster2)
top3.hits <- as.data.frame(matrix(ncol = 3))
colnames(top3.hits) <- colnames(corr.out[[1]])
for (i in 1:length(corr.out)){
temp <- corr.out[[i]][order(corr.out[[i]]$Correlation, decreasing = T),][1:3,]
top3.hits <- rbind(top3.hits,temp)
}
top3.hits <- top3.hits[-1,c(3,1,2)]
colnames(top3.hits) <- c('Our Clusters','HCL Cluster','Correlation')
rownames(top3.hits) <- 1:length(rownames(top3.hits))
temp.cor <- temp.cor[temp.cor$Cluster1 %in% top3.hits$`HCL Cluster`,]
temp.cor$`HCL Clusters` <- factor(temp.cor$Cluster1, levels = unique(top3.hits$`HCL Cluster`))
###
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster2[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster2 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = `HCL Clusters`, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .4) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 6)) +
xlab('HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using highly variable genes')
#
# ggplot(temp.cor, aes(x = hcl.clusters2, y = Cluster2, fill = Correlation)) +
# geom_tile() + coord_flip() +
# scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .4) +
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
# xlab('HCL Centroids') + ylab('Our Samples') +
# geom_text(aes(label = cor.label), size = 3) +
# ggtitle('HCL Comparison using highly variable genes')
```
### Interesting comparisons in Hcl
Doing a text based search.
#### MK
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('Megak', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('Megak', hcl.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
mrg2 <- mrg2[rownames(mrg2) %in% hv.overlap.genes,]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 6)) +
xlab('"Megak" HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using HV genes')
```
#### Erythrocyte
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('Eryth', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('Eryth', hcl.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
mrg2 <- mrg2[rownames(mrg2) %in% hv.overlap.genes,]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}
}
# temp.cor$hcl.clusters2 <- tstrsplit(temp.cor$`HCL Clusters`,'\\.', keep = 1)[[1]]
ggplot(temp.cor, aes(x = Cluster1, y = Cluster2, fill = Correlation)) +
geom_tile() + coord_flip() +
scale_fill_gradient2(low = 'blue', mid = 'white', high = 'red', midpoint = .3) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text.y = element_text(size = 4)) +
xlab('"Eryth" HCL Centroids') + ylab('Our Samples') +
geom_text(aes(label = cor.label), size = 3) +
ggtitle('HCL Comparison using HV genes')
```
#### Myeloid Progenitor
```{r}
hcl.exp <- colnames(hcl)
hcl.exp[grepl('prog', hcl.exp, ignore.case = T)]
mk.exp <- hcl.exp[grepl('prog', hcl.exp, ignore.case = T)]
mk.exp <- mk.exp[grepl('mye', mk.exp, ignore.case = T)]
hcl.mk <- hcl[,mk.exp]
key.columns <- unique(Idents(wbm))
mrg2 <- merge(av, hcl.mk, by = 0)
rownames(mrg2) <- mrg2$Row.names
mrg2 <- mrg2[,-1]
mrg2 <- mrg2[rownames(mrg2) %in% hv.overlap.genes,]
temp.cor <- cor(mrg2, method = 'spearman')
temp.cor
key.columns <- unique(Idents(wbm))
# Subsetting the correlation matrix to just the upper left quadrant
temp.cor <- temp.cor[rownames(temp.cor) %in% key.columns, !(colnames(temp.cor) %in% key.columns)]
temp.cor <- as.data.frame(temp.cor)
temp.cor <- gather(temp.cor, Cluster1, Correlation, factor_key = T)
temp.cor$Cluster2 <- rep(key.columns, dim(temp.cor)[1]/length(key.columns))
temp.cor$cor.label <- ''
summary(is.na(temp.cor$Correlation))
for (i in 1:dim(temp.cor)[1]){
temp.cluster <- temp.cor$Cluster1[i]
if (temp.cor$Correlation[i] == max(temp.cor[temp.cor$Cluster1 == temp.cluster,]$Correlation)){
temp.cor$cor.label[i] <- round(temp.cor$Correlation[i],2)
}