Example_human_BM.Rmd

---
title: "sc2marker with HCA-BM (Human)"
author: "sc2marker"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document:
    toc: yes
    toc_depth: 3
    toc_float:
      collapsed: false
      smooth_scroll: false
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressMessages(library(sc2marker))
suppressMessages(library(Seurat))
devtools::install_github('satijalab/seurat-data')
suppressMessages(library(SeuratData))
```

# load data

You can load the HCA-BM object by SeuratData.

```{r SeuratObject, warning=F,message=F}
#load reference data
InstallData("bmcite")
bmc <- LoadData(ds = "bmcite")
Idents(bmc) <- "celltype.l1"
table(bmc@active.ident)
```

# Markers for NK cell

To run sc2marker you need to execute the following command, providing a clustered single cell data sets (as Seurat object), the cell type of interest and the antibody databased (IHC, ICC or Flow).

```{r NK cell markers}
nk.markers <- Detect_single_marker(bmc, id = "NK", category = "Flow", org = "human", min.pct = 0.1)
get_antibody(nk.markers, org = "human")
```

```{r message=F, warning=F, fig.height=9, fig.width=9}
# Ridge plot of top 9 markers of NK cells
plot_ridge(bmc, id = "NK", genes = nk.markers[1:9,]$gene, ncol = 3, assay = "RNA", aggr.other = F)
```


# Markers for B cell

```{r B cell markers}
b.markers <- Detect_single_marker(bmc, id = "B cell", category = "Flow", org = "human", min.pct = 0.1)
get_antibody(b.markers, org = "human")
```

```{r message=F, warning=F, fig.height=9, fig.width=9}
# Ridge plot of top 9 markers of B cells
plot_ridge(bmc, id = "B cell", genes = b.markers[1:9,]$gene, ncol = 3, assay = "RNA", aggr.other = F)
```



# Markers for T cell

```{r T cell markers}
t.markers <- Detect_single_marker(bmc, id = "T cell", category = "Flow", org = "human", min.pct = 0.1)
get_antibody(t.markers, org = "human")
```

```{r message=F, warning=F, fig.height=9, fig.width=9}
# Ridge plot of top 9 markers of T cells
plot_ridge(bmc, id = "T cell", genes = t.markers[1:9,]$gene, ncol = 3, assay = "RNA", aggr.other = F)
```


# Markers for Progenitor cells

```{r Progenitor cell markers}
Prog.markers <- Detect_single_marker(bmc, id = "Progenitor cells", category = "Flow", org = "human", min.pct = 0.1)
get_antibody(Prog.markers, org = "human")
```

```{r message=F, warning=F, fig.height=9, fig.width=9}
# Ridge plot of top 9 markers of Progenitor cells
plot_ridge(bmc, id = "Progenitor cells", genes = Prog.markers[1:9,]$gene, ncol = 3, assay = "RNA", aggr.other = F)
```

# Markers for Mono/DC

```{r Mono/DC cell markers}
Mono.markers <- Detect_single_marker(bmc, id = "Mono/DC", category = "Flow", org = "human", min.pct = 0.1)
get_antibody(Mono.markers, org = "human")
```

```{r message=F, warning=F, fig.height=9, fig.width=9}
# Ridge plot of top 9 markers of Mono cells
plot_ridge(bmc, id = "Mono/DC", genes = Mono.markers[1:9,]$gene, ncol = 3, assay = "RNA", aggr.other = F)
```