conos.Rmd

---
title: "Integration of datasets using Conos"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: 
  github_document:
    html_preview: true
    toc: true
    toc_depth: 3
    fig_width: 16
  html_document:
    df_print: kable
    theme: united
    fig_height: 5
    fig_width: 16
    out_height: 4
---

This vignette demonstrates the use of the Conos package in Seurat. Commands and parameters are based off of the [Conos tutorial](https://github.com/hms-dbmi/conos/blob/master/vignettes/walkthrough.md). If you use Conos in your work, please cite:

> *Joint analysis of heterogeneous single-cell RNA-seq dataset collections*
>
> Nikolas Barkas, Viktor Petukhov, Daria Nikolaeva, Yaroslav Lozinsky, Samuel Demharter, Konstantin Khodosevich, Peter V. Kharchenko
>
> Nature Methods, 2019.
>
> doi: [10.1038/s41592-019-0466-z](https://doi.org/10.1038/s41592-019-0466-z)
>
> GitHub: https://github.com/hms-dbmi/conos


```{r setup, include=FALSE}
knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE
)
```

Prerequisites to install: 

* [Seurat](https://satijalab.org/seurat/install)
* [Conos](https://github.com/hms-dbmi/conos)
* [SeuratWrappers](https://github.com/satijalab/seurat-wrappers)
* [SeuratData](https://github.com/satijalab/seurat-data) 

```{r packages}
library(conos)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
```

## {.tabset .tabset-pills}

### Systematic comparative analysis of human PBMC

To learn more about this dataset, type `?pbmcsca` 

```{r pbmcsca, results='hide', cache=TRUE}
InstallData("pbmcsca")
data("pbmcsca")
pbmcsca.panel <- SplitObject(pbmcsca, split.by = 'Method')
for (i in 1:length(pbmcsca.panel)) {
  pbmcsca.panel[[i]] <- NormalizeData(pbmcsca.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA(verbose = FALSE)
}
pbmcsca.con <- Conos$new(pbmcsca.panel)
pbmcsca.con$buildGraph(
  k = 15,
  k.self = 5,
  space = 'PCA',
  ncomps = 30,
  n.odgenes = 2000,
  matching.method = 'mNN',
  metric = 'angular',
  score.component.variance = TRUE,
  verbose = TRUE
)
pbmcsca.con$findCommunities()
pbmcsca.con$embedGraph()
pbmcsca <- as.Seurat(pbmcsca.con)
DimPlot(pbmcsca, reduction = "largeVis", group.by = c("Method", "ident", 'CellType'), ncol = 3)
```

### Interferon-stimulated and control PBMC

To learn more about this dataset, type `?ifnb` 

```{r ifnb, results='hide', cache=TRUE}
InstallData("ifnb")
data("ifnb")
ifnb.panel <- SplitObject(ifnb, split.by = 'stim')
for (i in 1:length(ifnb.panel)) {
  ifnb.panel[[i]] <- NormalizeData(ifnb.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA(verbose = FALSE)
}
ifnb.con <- Conos$new(ifnb.panel)
ifnb.con$buildGraph(
  k = 15,
  k.self = 5,
  space = 'PCA',
  ncomps = 30,
  n.odgenes = 2000,
  matching.method = 'mNN',
  metric = 'angular',
  score.component.variance = TRUE,
  verbose = TRUE
)
ifnb.con$findCommunities()
ifnb.con$embedGraph()
ifnb <- as.Seurat(ifnb.con)
DimPlot(ifnb, reduction = "largeVis", group.by = c("stim", "ident", 'seurat_annotations'), ncol = 3)
```

### Eight human pancreatic islet datasets

To learn more about this dataset, type `?panc8` 

```{r pancreas, results='hide', cache=TRUE}
InstallData("panc8")
data("panc8")
panc8.panel <- SplitObject(panc8, split.by = 'replicate')
for (i in 1:length(panc8.panel)) {
  panc8.panel[[i]] <- NormalizeData(panc8.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA(verbose = FALSE)
}
panc8.con <- Conos$new(panc8.panel)
panc8.con$buildGraph(
  k = 15,
  k.self = 5,
  space = 'PCA',
  ncomps = 30,
  n.odgenes = 2000,
  matching.method = 'mNN',
  metric = 'angular',
  score.component.variance = TRUE,
  verbose = TRUE
)
panc8.con$findCommunities()
panc8.con$embedGraph()
panc8 <- as.Seurat(panc8.con)
DimPlot(panc8, reduction = "largeVis", group.by = c("replicate", "ident", 'celltype'), ncol = 3)
```