---
title: 'Cell type classification with SignacX: PBMCs from 10X Genomics'
author: 'Mathew Chamberlain'
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Analysis of PBMCs from 10X Genomics}
  %\VignetteEngine{knitr::rmarkdown_notangle}
  %\VignetteEncoding{UTF-8}
---

```{r setup0, include=FALSE}
all_times <- list()  # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      now <<- Sys.time()
    } else {
      res <- difftime(Sys.time(), now, units = "secs")
      all_times[[options$label]] <<- res
    }
  }
}))
knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  time_it = TRUE
)
celltypes_fast = readRDS("./fls/celltypes_fast.rds")
celltypes = readRDS("./fls/celltypes.rds")
# pbmc = readRDS("fls/pbmcs_signac.rds")
```

This vignette shows how to use Signac with Seurat. There are three parts: Seurat, Signac and then visualization. We use an example PBMCs scRNA-seq data set from 10X Genomics. First, we set up a folder for holding the Signac reference data:

## Seurat 

Start with the standard pre-processing steps for a Seurat object.

```{r setupSeurat, message = F, eval = F}
library(Seurat)
```

Download data from 10X Genomics.

```{r setup, message = F, eval = F}
dir.create("fls")
download.file("https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_v3/pbmc_1k_v3_filtered_feature_bc_matrix.h5", destfile = "fls/pbmc_1k_v3_filtered_feature_bc_matrix.h5")
```

Create a Seurat object, and then perform SCTransform normalization. Note:

* You can use the legacy functions here (i.e., NormalizeData, ScaleData, etc.), use SCTransform or any other normalization method (including no normalization). We did not notice a significant difference in cell type annotations with different normalization methods.
* We think that it is best practice to use SCTransform, but it is not a necessary step. Signac will work fine without it.

```{r Seurat, message = T, eval = F}
# load data
E = Read10X_h5(filename = "fls/pbmc_1k_v3_filtered_feature_bc_matrix.h5")
pbmc <- CreateSeuratObject(counts = E, project = "pbmc")

# run sctransform
pbmc <- SCTransform(pbmc, verbose = FALSE)
```

Perform dimensionality reduction by PCA and UMAP embedding. Note:

* Signac actually needs these functions since it uses the nearest neighbor graph generated by Seurat.

```{r Seurat 2, message = T, eval = F}
# These are now standard steps in the Seurat workflow for visualization and clustering
pbmc <- RunPCA(pbmc, verbose = FALSE)
pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE)
pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE)
```

## SignacX

First, make sure you have the Signac package installed.

```{r setup2, message = F, eval = F}
install.packages("SignacX")
```

Load the library

```{r Signac setup folder, message = T, eval = F}
# load library
library(SignacX)
```

Generate SignacX labels for the Seurat object. Note:

* Optionally, you can do parallel computing by setting num.cores > 1 in the Signac function.
* Run time is ~10 minutes for ~10,000 cells.

```{r Signac, message = T, eval = F}
# Run Signac
labels <- Signac(pbmc, num.cores = 4)
celltypes = GenerateLabels(labels, E = pbmc)
```

Sometimes, training the neural networks takes a lot of time. To make Signac faster, we implemented SignacFast which uses an ensemble of pre-trained neural network models. Note:

* SignacFast uses an ensemble of 1,800 pre-calculated neural networks rather than performing bespoke model training.

```{r SignacFast, message = T, eval = F}
# Run Signac
labels_fast <- SignacFast(pbmc)
celltypes_fast = GenerateLabels(labels_fast, E = pbmc)
```

SignacFast took only ~30 seconds. Relative to Signac, the main difference is that SignacFast tends to leave a few more cells "Unclassified."

<details>
  <summary>**How does SignacFast compare to Signac?**</summary>
```{r, echo=FALSE, eval = T}
knitr::kable(table(Signac = celltypes$CellTypes, SignacFast = celltypes_fast$CellTypes), format = "html")
```
</details>

## Visualizations

Now we can visualize the cell type classifications at many different levels:
Immune and nonimmune
```{r Seurat Visualization 0, message = F, eval = F}
pbmc <- AddMetaData(pbmc, metadata=celltypes$Immune, col.name = "immmune")
pbmc <- SetIdent(pbmc, value='immmune')
png(filename="fls/plot1.png")
DimPlot(pbmc)
dev.off()
```
![Immune, Nonimmune (if any) and unclassified cells](./fls/plot1.png)

```{r Seurat Visualization 1, message = F, eval = F}
pbmc <- AddMetaData(pbmc, metadata=celltypes$L2, col.name = "L2")
pbmc <- SetIdent(pbmc, value='L2')
png(filename="fls/plot2.png")
DimPlot(pbmc)
dev.off()
```
![Myeloid and lymphocytes](./fls/plot2.png)

```{r Seurat Visualization 2, message = F, eval = F}
lbls = factor(celltypes$CellTypes)
levels(lbls) <- sort(unique(lbls))
pbmc <- AddMetaData(pbmc, metadata=lbls, col.name = "celltypes")
pbmc <- SetIdent(pbmc, value='celltypes')
png(filename="./fls/plot3.png")
DimPlot(pbmc)
dev.off()
```
![Cell types](./fls/plot3.png)

```{r Seurat Visualization 3, message = F, eval = F}
pbmc <- AddMetaData(pbmc, metadata=celltypes$CellTypes_novel, col.name = "celltypes_novel")
pbmc <- SetIdent(pbmc, value='celltypes_novel')
png(filename="./fls/plot4.png")
DimPlot(pbmc)
dev.off()
```
![Cell types with novel populations](./fls/plot4.png)

```{r Seurat Visualization 4, message = F, eval = F}
pbmc <- AddMetaData(pbmc, metadata=celltypes$CellStates, col.name = "cellstates")
pbmc <- SetIdent(pbmc, value='cellstates')
png(filename="./fls/plot5.png")
DimPlot(pbmc)
dev.off()
```
![Cell states](./fls/plot5.png)

Identify differentially expressed genes between cell types. Here, we see that Signac identified two novel cell populations that are positive for platelet and plasma cell markers, respectively.

```{r Seurat visualize protein 3, message = F, eval = F}
pbmc <- SetIdent(pbmc, value='celltypes_novel')

# Find protein markers for all clusters, and draw a heatmap
markers <- FindAllMarkers(pbmc, only.pos = TRUE, verbose = F, logfc.threshold = 1)
library(dplyr)
top5 <- markers %>%  group_by(cluster) %>% top_n(n = 5, wt = avg_logFC)
png(filename="./fls/plot6.png")
DoHeatmap(pbmc, features = unique(top5$gene), angle = 90)
dev.off()
```
![Immune marker genes](./fls/plot6.png)

Save results

```{r save results, message = F, eval = F}
saveRDS(pbmc, file = "fls/pbmcs_signac.rds")
saveRDS(celltypes, file = "fls/celltypes.rds")
saveRDS(celltypes_fast, file = "fls/celltypes_fast.rds")
```


```{r save.times, include = FALSE, eval = F}
write.csv(x = t(as.data.frame(all_times)), file = "fls/tutorial_times_signac-Seurat_pbmcs.csv")
```

<details>
  <summary>**Session Info**</summary>
```{r, echo=FALSE}
sessionInfo()
```
</details>