Example_Code.Rmd

---
title: "BM HCA MPAL transfer label"
output: html_document
author: OW
editor_options: 
  chunk_output_type: console
---

This vignette will show basic functions provided by viewmaster
```{r warning=FALSE,message=FALSE,warning=FALSE,echo=F}
graphics.off()
rm(list=ls())
knitr::opts_chunk$set(fig.width=8, fig.height=6,dpi=300,
                      echo=TRUE, warning=FALSE, message=FALSE)
knitr::opts_chunk$set(dev.args=list(bg="transparent"))
xfun::pkg_load2(c('base64enc', 'htmltools', 'mime'))
#Load neccessary packages

suppressPackageStartupMessages({
  library(monocle3)
  library(dplyr)
  library(Matrix)
  library(ggplot2)
  library(parallel)
  library(doMC)
  library(glmnet)
  library(xfun)
  library(pals)
  library(RColorBrewer)
  library(Seurat)
  library(ggplot2)
  library(pheatmap)
  library(parallel)
  library(SeuratObject)
  library(SeuratDisk)
  library(pbmcapply)
  library(ArchR)
  library(viewmaster)
})


```

load greenleaf MPAL dataset into monocle3
```{r}
mpal<-readRDS(file ="/Volumes/fh/fast/furlan_s/grp/data/ddata/BM_data/healthy_bone_marrow_greenleaf.rds")
save(mpal, file="data/healthy_bone_marrow_greenleaf.RData")
plot_cells(mpal, color_cells_by = "BioClassification", label_cell_groups = F)+scale_color_manual(values = sfc(n = 26))
```
load hca bone marrow seurat object, convert to monocle using viewmaster's helpful function 
```{r}
hca<-readRDS(file = "/Volumes/fh/fast/furlan_s/grp/data/ddata/BM_data/palantir_bm_r1.rds")
save(hca, file="data/palantir_bone_marrow.RData")
hca<-seurat_to_monocle3(hca, seu_rd = "tsne")
plot_cells(hca , color_cells_by = "cell_annotation", label_cell_groups = F)+scale_color_manual(values = sfc(10, scramble = F))
```
Detect common variant genes between two cell data sets
```{r}
vg<-common_variant_genes(mpal, hca, top_n = 5000)
```
Use softmax regression for viewmaster. We will use the Greenleaf lab's MPAL healthy bone marrow cell annotations and transfer them to Human Cell Atals of bone marrow
```{r}
view<-viewmaster(ref_cds  = mpal, query_cds = hca, ref_celldata_col = "BioClassification", selected_genes = vg, verbose=T, FUNC = "softmax_regression", tf_idf = F)

plot_cells(view, color_cells_by = "smr_celltype", label_cell_groups = F)+scale_color_manual(values = sfc(11))
```
logistic regression matrix
```{r}
hca_seu<-monocle3_to_seurat(hca)
mpal_seu<-monocle3_to_seurat(mpal, mon_rd = "UMAP")

log_mat<-log_reg_matrix(trainDat = mpal_seu, testDat = hca_seu, trainClass = "BioClassification", testClass = "cell_annotation", downsample = 500)

testDat<- loadTrainingData(hca_seu)

clusterPreds = apply(log_mat,2,function(e) sapply(split(e,testDat$mDat[["cell_annotation"]]),mean))

#Convet to percentages
clusterPreds = (1+exp(-clusterPreds))**-1

pheatmap(clusterPreds, cluster_cols=T,cluster_rows = T ,col = ArchR::paletteContinuous("coolwarm", n=10, reverse = F), border_color = "black")
```

#humanize
```{r}
ROOT_DIR<-"~/Fred Hutchinson Cancer Research Center"
stem<-"Myeloma Exhaustion - General/mm2"
CDS_DIR <- file.path(ROOT_DIR,  stem, "cds")

#Seurat Object
mm2<- readRDS(file.path(CDS_DIR, "120621_mm2_seurat.rds"))
library(Seurat)
library(viewmaster)
mm2$test<-mm2$sample
mm2$test[sample(mm2$test, size = 1000)]<-"test1"
mm2$test[sample(mm2$test, size = 1000)]<-"test2"


DimPlot(mm2, group.by = "cluster_3d")+scale_color_manual(values = sfc(n=10))+theme_viewmastr()
DimPlot(mm2, group.by = "cluster_3d")+scale_color_manual(values =pal_continuous(set = "acidRain", n =12) %>% sample(size = 10))+theme_viewmastr()
DimPlot(mm2, group.by = "cluster_3d")+scale_color_manual(values =pal_continuous(set = "fallDay", n =10) %>% sample(size = 10))+theme_viewmastr()
DimPlot(mm2, group.by = "cluster_3d")+scale_color_manual(values =pal_continuous(set = "springTime", n =10))+theme_viewmastr()
```

```{r}
session_info()
```