---
title: "Public_data_hastagging"
author: "KK"
date: "December 16, 2019"
output: html_document
---

```{r setup, include=FALSE}

tissue_of_interest = "Liver"
library(here)
source("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/boilerplate.R")
#tiss = load_tissue_droplet(tissue_of_interest)
#library(scater)
library(dplyr)
library(Seurat)
library(cowplot)
#library(MAST)
require(stringr)
require(reshape2)
require(ggplot2)
require(MASS)
library(tools)
require(data.table)
library(ggfortify)
library(tidyverse)
require(dplyr)
library(miscTools)
library(caret)
library(Rtsne)
library(ggrepel)



```

```{r}
# Read in UMI count matrix for RNA
hto12.umis <- readRDS("Public Data/hto12_umi_mtx.rds")

# Read in HTO count matrix
hto12.htos <- readRDS("Public Data/hto12_hto_mtx.rds")

# Select cell barcodes detected in both RNA and HTO
cells.use <- intersect(rownames(hto12.htos), colnames(hto12.umis))

# Create Seurat object and add HTO data
hto12 <- CreateSeuratObject(counts = hto12.umis[, cells.use], min.features = 300)
hto12[["HTO"]] <- CreateAssayObject(counts = t(x = hto12.htos[colnames(hto12), 1:12]))

# Normalize data
hto12 <- NormalizeData(hto12)
hto12 <- NormalizeData(hto12, assay = "HTO", normalization.method = "CLR")


hto12 <- HTODemux(hto12, assay = "HTO", positive.quantile = 0.99)

RidgePlot(hto12, assay = "HTO", features = c("HEK-A", "K562-B", "KG1-A", "THP1-C"), ncol = 2)
```