---
title: "Quick start to Harmony"
author: "Korsunsky et al.: Fast, sensitive, and accurate integration of single 
cell data with Harmony"
output:
  rmarkdown::html_vignette:
    code_folding: show
vignette: >
    %\VignetteIndexEntry{Quick start to Harmony}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8} 
---
  

# Introduction

Harmony is an algorithm for performing integration of single cell genomics
datasets. Please check out our latest 
[manuscript on Nature Methods](https://www.nature.com/articles/s41592-019-0619-0). 

![](main.jpg){width=100%}


# Installation

Install Harmony from CRAN with standard commands.

```{r eval=FALSE}
install.packages('harmony')
```

Once Harmony is installed, load it up! 

```{r}
library(harmony)
```


# Integrating cell line datasets from 10X

The example below follows Figure 2 in the manuscript. 

We downloaded 3 cell line datasets from the 10X website. The first two (jurkat
and 293t) come from pure cell lines while the *half* dataset is a 50:50
mixture of Jurkat and HEK293T cells. We inferred cell type with the canonical 
marker XIST, since the two cell lines come from 1 male and 1 female donor. 

* support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/jurkat
* support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/293t
* support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/jurkat:293t_50:50

We library normalized the cells, log transformed the counts, and scaled the 
genes. Then we performed PCA and kept the top 20 PCs. The PCA embeddings and 
meta data are available as part of this package. 

```{r}
data(cell_lines)
V <- cell_lines$scaled_pcs
meta_data <- cell_lines$meta_data

```


Initially, the cells cluster by both dataset (left) and cell type (right). 

```{r class.source='fold-hide', fig.width=5, fig.height=3, fig.align="center"}

library(ggplot2)

do_scatter <- function(xy, meta_data, label_name, base_size = 12) {    
    palette_use <- c(`jurkat` = '#810F7C', `t293` = '#D09E2D',`half` = '#006D2C')
    xy <- xy[, 1:2]
    colnames(xy) <- c('X1', 'X2')
    plt_df <- xy %>% data.frame() %>% cbind(meta_data)
    plt <- ggplot(plt_df, aes(X1, X2, col = !!rlang::sym(label_name), fill = !!rlang::sym(label_name))) + 
        theme_test(base_size = base_size) +
        guides(color = guide_legend(override.aes = list(stroke = 1, alpha = 1,
                                                        shape = 16, size = 4))) +
        scale_color_manual(values = palette_use) +
        scale_fill_manual(values = palette_use) +
        theme(plot.title = element_text(hjust = .5)) +
        labs(x = "PC 1", y = "PC 2") +
        theme(legend.position = "none") +
        geom_point(shape = '.')
    
    ## Add labels
    data_labels <- plt_df %>%
        dplyr::group_by(!!rlang::sym(label_name)) %>%
        dplyr::summarise(X1 = mean(X1), X2 = mean(X2)) %>%
        dplyr::ungroup()
    plt + geom_label(data = data_labels, aes(label = !!rlang::sym(label_name)), 
                            color = "white", size = 4)
}
p1 <- do_scatter(V, meta_data, 'dataset') + 
    labs(title = 'Colored by dataset')
p2 <- do_scatter(V, meta_data, 'cell_type') + 
    labs(title = 'Colored by cell type')

cowplot::plot_grid(p1, p2)

```

Let's run Harmony to remove the influence of dataset-of-origin from the cell
embeddings.

```{r}
harmony_embeddings <- harmony::RunHarmony(
    V, meta_data, 'dataset', verbose=FALSE
)

```

After Harmony, the datasets are now mixed (left) and the cell types are still
separate (right). 

```{r, fig.width=5, fig.height=3, fig.align="center"}
p1 <- do_scatter(harmony_embeddings, meta_data, 'dataset') + 
    labs(title = 'Colored by dataset')
p2 <- do_scatter(harmony_embeddings, meta_data, 'cell_type') + 
    labs(title = 'Colored by cell type')
cowplot::plot_grid(p1, p2, nrow = 1)

```

# Next Steps

## Interfacing to software packages

You can also run Harmony as part of an established pipeline in several packages, such as Seurat. For these vignettes, please [visit our github page](https://github.com/immunogenomics/harmony/).


## Detailed breakdown of the Harmony algorithm

For more details on how each part of Harmony works, consult our more detailed
[vignette](https://htmlpreview.github.io/?https://github.com/immunogenomics/harmony/blob/master/doc/detailedWalkthrough.html)
"Detailed Walkthrough of Harmony Algorithm".

# Session Info

```{r}
sessionInfo()

```
