.Rhistory

saveRDS(seu, file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query - aka Projectee Dataset")
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference - aka Projector Dataset")
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>%
filter(pct_diff > 0.6)
idents(seur) <- seur$celltype
Idents(seur) <- seur$celltype
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>%
filter(pct_diff > 0.6)
pct_diff
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff()
all_markers
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>%
all_markers %>% filter(pct_diff > 0.6)
all_markers
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>%
all_markers %>% filter(pct_diff > 0.6)
library(tidyr)
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>%
all_markers %>% filter(pct_diff > 0.6)
all_markers %>% filter(pct_diff > 0.6)
all_markers
all_markers[all_markers$pct_diff > 0.6,]
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>% dplyr::filter(pct_diff > 0.6)
all_markers
top_markers <- Extract_Top_Markers(marker_dataframe = all_markers, num_genes = 5, named_vector = FALSE,
make_unique = TRUE)
Clustered_DotPlot(seurat_object = seur, features = top_markers)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1)
seur <- seur[,!seur$celltype %in% c("Basophils", "Neutrophils")]
saveRDS(seur, file.path(ROOT_DIR1, "240926_final_object.RDS"))
seur <- readRDS(file.path(ROOT_DIR1, "240926_final_object.RDS"))
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:20)])
names(cols1) <- levels(factor(seur$celltype))
cols1
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:13)])
names(cols1) <- levels(factor(seur$celltype))
cols1
cols2 <- c("00_PROJECTOR"="grey90", cols1)
cols2
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference - aka Projector Dataset")
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1)
Idents(seur) <- seur$celltype
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>% dplyr::filter(pct_diff > 0.6)
top_markers <- Extract_Top_Markers(marker_dataframe = all_markers, num_genes = 5, named_vector = FALSE,
make_unique = TRUE)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=12)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11)
Idents(seu) <- seur$celltype
Idents(seu) <- seu$celltype
all_markers <- FindAllMarkers(object = seu) %>%
Add_Pct_Diff() %>% dplyr::filter(pct_diff > 0.6)
seu <- seu[,!seu$celltype %in% c("Basophils", "Neutrophils")]
saveRDS(seu, file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
seu <-readRDS(file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query - aka Projectee Dataset")
Idents(seu) <- seu$celltype
all_markers <- FindAllMarkers(object = seu) %>%
Add_Pct_Diff() %>% dplyr::filter(pct_diff > 0.6)
top_markers <- Extract_Top_Markers(marker_dataframe = all_markers, num_genes = 5, named_vector = FALSE,
make_unique = TRUE)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11)
# Calculate and plot gene dispersion in query dataset
seu <- calculate_gene_dispersion(seu)
seu <- select_genes(seu, top_n = 10000, logmean_ul = -1, logmean_ll = -8)
plot_gene_dispersion(seu)
vgq <- get_selected_genes(seu)
# Repeat the process for the reference dataset
seur <- calculate_gene_dispersion(seur)
seur <- select_genes(seur, top_n = 10000, logmean_ul = -1, logmean_ll = -8)
plot_gene_dispersion(seur)
vgr <- get_selected_genes(seur)
# Find common genes
vg <- intersect(vgq, vgr)
#iterative LSI
comps <- 25 #Number of PCs for clustering
seur<-iterative_LSI(seur, num_dim = comps, num_features = length(vg), resolution = c(1e-3), verbose = T, starting_features = vg, run_umap = T)
p1 <- DimPlot(seur, group.by = "celltype", cols = cols1)+theme_void()
p1
res <- project_data(projector = seur, projectee = seu, reduced_dim = "lsi", embedding = "umap")
p2 <- plot_projection(res, seur, seu, projectee_col = "celltype")+scale_color_manual(values = cols2)+theme_void()+ guides(color = guide_legend(override.aes = list(size = 3)))
p2
cowplot::plot_grid(p1, p2)
cp <- Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11)
cp <- Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11, plot_km_elbow = F)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11, plot_km_elbow = F)
knitr: :opts_chunk$set(message= FALSE, warning = FALSE)
knitr::opts_chunk$set(message= FALSE, warning = FALSE)
# build -------------------------------------------------------------------
pkgdown::build_site(install=F)
rm(list=ls())
# Define dataset paths depending on your machine
if(grepl("^gizmo", Sys.info()["nodename"])){
ROOT_DIR1<-"/fh/fast/furlan_s/experiments/MB_10X_5p/cds"
ROOT_DIR2<-"/fh/fast/furlan_s/grp/data/ddata/BM_data"
ROOT_DIR3<-"/fh/fast/furlan_s/experiments/LRPBMC/cds"
} else {
ROOT_DIR1<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments/MB_10X_5p/cds"
ROOT_DIR2<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/datasets/Healthy_BM_greenleaf"
ROOT_DIR3<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments/LRPBMC/cds"
}
knitr::opts_chunk$set(message= FALSE, warning = FALSE)
# Load required packages
suppressPackageStartupMessages({
library(flscuts)
library(Seurat)
library(monocle3)
library(viewmastR)
library(ggplot2)
library(scCustomize)
library(tidyr)
})
seur <- readRDS(file.path(ROOT_DIR1, "240926_final_object.RDS"))
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:13)])
names(cols1) <- levels(factor(seur$celltype))
cols2 <- c("00_PROJECTOR"="grey90", cols1)
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference - aka Projector Dataset")
seu <-readRDS(file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query - aka Projectee Dataset")
Idents(seur) <- seur$celltype
all_markers <- FindAllMarkers(object = seur) %>%
Add_Pct_Diff() %>% dplyr::filter(pct_diff > 0.6)
top_markers <- Extract_Top_Markers(marker_dataframe = all_markers, num_genes = 5, named_vector = FALSE,
make_unique = TRUE)
Clustered_DotPlot(seurat_object = seur, features = top_markers, colors_use_idents = cols1, k=11, plot_km_elbow = F)
pkgdown::deploy_to_branch()
pkgdown::deploy_to_branch()
pkgdown::deploy_to_branch()
seu <- NormalizeData(seu)
seur <- NormalizeData(seur)
seu <- NormalizeData(seur) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% RunUMAP(dims=1:30, return.model = T)
seur <- readRDS(file.path(ROOT_DIR1, "240926_final_object.RDS"))
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:13)])
names(cols1) <- levels(factor(seur$celltype))
cols2 <- c("00_PROJECTOR"="grey90", cols1)
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference - aka Projector Dataset")
seur <- readRDS(file.path(ROOT_DIR1, "240926_final_object.RDS"))
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:13)])
names(cols1) <- levels(factor(seur$celltype))
cols2 <- c("00_PROJECTOR"="grey90", cols1)
seur <- NormalizeData(seur) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% RunUMAP(dims=1:30, return.model = T)
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference - aka Projector Dataset")
seu <-readRDS(file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
seu <- NormalizeData(seu) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% RunUMAP(dims=1:30, return.model = T)
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query - aka Projectee Dataset")
anchors <- FindTransferAnchors(
reference = seur,
query = seu,
reference.reduction = 'pca',
features = rownames(x = seur[["pca"]]@feature.loadings),
dims = 1:30,
nn.method = "annoy",
k.filter = NA,
verbose = TRUE
)
seu.1 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap"
)
# set embeddings
seur[["umap.new"]] <- CreateDimReducObject(embeddings = seur[["umap"]]@cell.embeddings, key = "UMAPnew_")
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
list(celltype = "celltype")
DimPlot(seur)
DimPlot(seur, reduction = "umap.new")
seur[["umap.new"]]@misc$model
umap_new_model$n_neighbors <- 30
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap_new_model$n_neighbors <- 30
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
debug(MapQuery)
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
reference.dims
slot(object = anchorset,
name = "command")$dims
ProjectUMAP
pu.allarguments
ref_nn.num
Misc(object = reference[[reduction.model]],
slot = "model")$n_neighbors
reference[[reduction.model]]
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap_new_model$n_neighbors <- 30
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap.new_model$n_neighbors <- 30
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap.new.model$n_neighbors <- 30
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
undebug(MapQuery)
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
p1 <- DimPlot(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T) + NoLegend() + ggtitle("return model")
p2 <- DimPlot(seu.2, reduction = "ref.umap", group.by = "predicted.celltype", label = T) + NoLegend()+ ggtitle("set model")
library(patchwork)
p1+p2
seu.1
seu.2
seur
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query - aka Projectee Dataset; Pre-Projection")
p0 <- DimPlot(seur, reduction = "ref.umap", group.by = "predicted.celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p0 <- DimPlot(seur, group.by = "predicted.celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p1 <- DimPlot(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T) + NoLegend() +theme_void()+ggtitle("Query - ake")
p2 <- DimPlot(seu.2, reduction = "ref.umap", group.by = "predicted.celltype", label = T) + NoLegend()+ ggtitle("set model")
library(patchwork)
p1+p2+p3
p0 <- DimPlot(seur, group.by = "celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p1 <- DimPlot(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T) + NoLegend() +theme_void()+ggtitle("Query - ake")
p2 <- DimPlot(seu.2, reduction = "ref.umap", group.by = "predicted.celltype", label = T) + NoLegend()+ ggtitle("set model")
library(patchwork)
p0+p1+p2
p0 <- DimPlot(seur,  reduction = "ref.umap", group.by = "celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
# set embeddings
seur[["umap.new"]] <- CreateDimReducObject(embeddings = seur[["umap"]]@cell.embeddings, key = "UMAPnew_")
p0 <- DimPlot(seur,  reduction = "ref.umap", group.by = "celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
seur@reductions
p0 <- DimPlot(seur,  reduction = "umap.new", group.by = "celltype", label = T) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p0
rm(list=ls())
# Define dataset paths depending on your machine
if(grepl("^gizmo", Sys.info()["nodename"])){
ROOT_DIR1<-"/fh/fast/furlan_s/experiments/MB_10X_5p/cds"
ROOT_DIR2<-"/fh/fast/furlan_s/grp/data/ddata/BM_data"
ROOT_DIR3<-"/fh/fast/furlan_s/experiments/LRPBMC/cds"
} else {
ROOT_DIR1<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments/MB_10X_5p/cds"
ROOT_DIR2<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/datasets/Healthy_BM_greenleaf"
ROOT_DIR3<-"/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments/LRPBMC/cds"
}
knitr::opts_chunk$set(message= FALSE, warning = FALSE)
# Load required packages
suppressPackageStartupMessages({
library(flscuts)
library(Seurat)
library(monocle3)
library(viewmastR)
library(ggplot2)
library(scCustomize)
library(tidyr)
})
seur <- readRDS(file.path(ROOT_DIR1, "240926_final_object.RDS"))
# Make some colors
cols1 <- as.character(pals::polychrome()[c(1,3:13)])
seur <- NormalizeData(seur) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% RunUMAP(dims=1:30, return.model = T)
DimPlot_scCustom(seur, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Reference")
seu <-readRDS(file.path(ROOT_DIR3, "240926_5p_seu.RDS"))
seu <- NormalizeData(seu) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% RunUMAP(dims=1:30, return.model = T)
DimPlot_scCustom(seu, group.by = "celltype", colors_use = cols1)+theme_void()+ggtitle("Query")
anchors <- FindTransferAnchors(
reference = seur,
query = seu,
reference.reduction = 'pca',
features = rownames(x = seur[["pca"]]@feature.loadings),
dims = 1:30,
nn.method = "annoy",
k.filter = NA,
verbose = TRUE
)
seu.1 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap"
)
p1 <- DimPlot(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T) + NoLegend() +theme_void()+ggtitle("Query - Unmodified model")
p1
p1 <- DimPlot_scCustom(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend() +theme_void()+ggtitle("Query - Mapped using Reference")
DimPlot_scCustom(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend() +theme_void()+ggtitle("Query - Mapped using Reference")
p0 <- DimPlot(seur,  reduction = "umap.new", group.by = "celltype", label = T, colors_use = cols1) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p1 <- DimPlot_scCustom(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend() +theme_void()+ggtitle("Query - Mapped using UMAP Projection from Reference")
p0 <- DimPlot_scCustom(seur,  reduction = "umap.new", group.by = "celltype", label = T, colors_use = cols1) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p0 <- DimPlot_scCustom(seur, group.by = "celltype", label = T, colors_use = cols1) +theme_void()+ggtitle("Reference - aka Projector Dataset")
p1 <- DimPlot_scCustom(seu.1, reduction = "ref.umap",group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend() +theme_void()+ggtitle("Query - Mapped using UMAP Projection from Reference")
p0+p1
# set embeddings
seur[["umap.new"]] <- CreateDimReducObject(embeddings = seur[["umap"]]@cell.embeddings, key = "UMAPnew_")
# set embeddings
seur[["umap.new"]] <- CreateDimReducObject(embeddings = seur[["umap"]]@cell.embeddings, key = "UMAPnew_", assay = "RNA")
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 500
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap.new.model$n_neighbors <- 30
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
umap.new.model$n_epochs <- 1000
# set UMAP models
umap.new.model <- list()
umap.new.model$n_epochs <- 1000
umap.new.model$alpha <-1
umap.new.model$method <- "umap"
umap.new.model$negative_sample_rate <- 5
umap.new.model$gamma <- 1
umap.new.model$approx_pow <- 0
umap.new.model$n_neighbors <- 10
umap.new.model$metric$cosine <- list()
umap.new.model$embedding <- seur[["umap.new"]]@cell.embeddings
ab_param <- uwot:::find_ab_params(spread = 1, min_dist = 0.3)
umap.new.model$a <- ab_param["a"]
umap.new.model$b <- ab_param["b"]
seur[["umap.new"]]@misc$model <- umap.new.model
seu.2 <- MapQuery(
reference = seur,
query = seu,
anchorset = anchors,
refdata = list(celltype = "celltype"),
reference.reduction = "pca",
reduction.model = "umap.new"
)
p2 <- DimPlot_scCustom(seu.2, reduction = "ref.umap", group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend()+ ggtitle("set model")
p0+p2
p0+p1
p2 <- DimPlot_scCustom(seu.2, reduction = "ref.umap",group.by = "predicted.celltype", label = T, colors_use = cols1) + NoLegend() +theme_void()+ggtitle("Query - Mapped using Altered UMAP Projection from Reference")
p0+p2
# quick testing -----------------------------------------------------------
pkgdown::clean_site(pkg = ".")
pkgdown::build_article("ProjectDataSeuratWorkflow")
# quick testing -----------------------------------------------------------
pkgdown::clean_site(pkg = ".")
pkgdown::init_site(pkg = ".")
pkgdown::build_home_index()
pkgdown::preview_page("index.html")
pkgdown::build_article(name = "ProjectDataSeuratWorkflow")
pkgdown::preview_page('articles/ProjectDataSeuratWorkflow.html')
pkgdown::preview_page("articles/ProjectDataSeuratWorkflow")
# quick testing -----------------------------------------------------------
pkgdown::clean_site(pkg = ".")
pkgdown::init_site(pkg = ".")
pkgdown::deploy_to_branch()
rm(list=ls())
# Define dataset paths depending on your machine
if (grepl("^gizmo", Sys.info()["nodename"])) {
ROOT_DIR1 <- "NA"
} else {
ROOT_DIR1 <- "/Users/sfurlan/Library/CloudStorage/OneDrive-SharedLibraries-FredHutchinsonCancerCenter/Furlan_Lab - General/experiments"
}
stem <- "Pitt/Pitt_2"
DATA_DIR <- file.path(ROOT_DIR1,  stem, "data")      # SPECIFY HERE
RES_DIR  <- file.path(ROOT_DIR1, stem, "res")     # SPECIFY HERE
RMD_DIR  <- file.path(ROOT_DIR1, stem, "rmd")     # SPECIFY HERE
CDS_DIR <- file.path(ROOT_DIR1,  stem, "cds")
FIG_DIR <- file.path(ROOT_DIR1,  stem, "figs")
# Load required packages
suppressPackageStartupMessages({
library(flscuts)
library(viewmastR)
library(Seurat)
library(scCustomize)
library(magrittr)
library(miloR)
library(scater)
})
set.seed(1234) # Set Seed
group_cols<-c("Pre_Ifng_1_BM" = "#594286",
"Post_Ifng_1_BM" = "#bd9895",
"Pre_Ifng_1_34" = "#f76b14",
"Post_Ifng_1_34" = "#20B2AA",
"Pre_Ifng_5_BM" = "orange",
"Post_Ifng_5_BM" = "grey",
"Pre_Ifng_7_BM" = "violetred4",
"Post_Ifng_7_BM" = "lightgreen")
seu <- readRDS(file.path(CDS_DIR, "Patient_1_final_CDS.RDS"))
seu <- seu[,seu$celltype %in% c("01_HSC", "04_Myeloid_Progenitor",  "02_Early_Erythroid")]
DimPlot(seu, group.by = "geno_5")
DimPlot(seu, group.by = "geno_5")
DimPlot(seu, group.by = "geno")
seu <- seu[,seu$geno %in% "1"]
seu$rx <- strsplit(colnames(seu), "_") %>% sapply("[[", 1)
DimPlot(seu, group.by="rx")
DimPlot(seu, group.by = "celltype")
seu$celltype<- factor(as.character(seu$celltype))
#roxygen2::roxygenise()
DefaultAssay(seu)<-"RNA"
seu$rx <- factor(seu$rx)
scm <- make_nhoods(seu, variable="rx", covariate1 = "celltype")
debug(make_nhoods)
scm <- make_nhoods(seu, variable="rx", covariate1 = "celltype")
miloR::plotNhoodSizeHist(sce_milo)
roxygen2::roxygenise()
scm <- make_nhoods(seu, variable="rx", covariate1 = "celltype")
scm <- make_nhoods(seu, variable="rx", covariate1 = "celltype")
roxygen2::roxygenise()
scm <- make_nhoods(seu, variable="rx", covariate1 = "celltype")
da_result <- test_nhoods(scm, "~rx+celltype")
scm$design