---
title: "User guide for performing automatic gating with `cytometree`"
author: "Chariff Alkhassim, Boris Hejblum"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: yes
vignette: >
  %\VignetteIndexEntry{User guide for performing automatic gating with `cytometree`}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r knitrsetup, include=FALSE}
knitr::opts_chunk$set(tidy = TRUE)
knitr::knit_hooks$set(small.mar = function(before, options, envir) {
    if (before) par(mar = c(0, 0, 0, 0))  # no margin
})
```



# Introduction to `cytometree`

`cytometree` is a package that implements a binary tree algorithm for the analysis of cytometry data.

Its core algorithm is based on the construction of a binary tree, whose nodes represents 
cell subpopulations.

## Binary tree construction

1. At each node, observed cells (or "events") and markers are modeled by both a normal distribution (so *unimodal*), and a mixture of 2 normal distributions (so *bimodal*).

2. Splitting of the events at each node is done according to a normalized difference of AIC between 
the two distributional fit (unimodal or bimodal), allowing to pick the marker that best splits those data.

3. When AIC normalized differences $D$ are not significant anymore, the tree has been constructed, and the cells have been automatically gated (i.e. partitioned). 

## Post-hoc annotation

Given the unsupervised nature of the binary tree, some of the available markers may not be used to find the different cell populations present in a given sample. To recover a complete annotation, we defined, as a post processing procedure, an annotation method which allows the user to distinguish two (or three) expression levels per marker.


# Examples of analysis with `cytometree`

## Diffuse large B-cell lymphoma dataset with 3 markers

In this example, we will use a diffuse large B-cell lymphoma dataset (from the [FlowCAP-I challenge](http://flowcap.flowsite.org/), with only 3 markers.

### Automatic gating step

First, we need to load the package `cytometree` and we can have a look at the data:

```{r, message=FALSE}
library(cytometree)
data(DLBCL)
dim(DLBCL)
head(DLBCL)
```
We have 3 markers measured `FL1`, `FL2`, `FL4` as well as the `label` obtained from manual gating, and `r nrow(DLBCL)` cells.

```{r}
# getting the only the cell events with the 3 markers measured:
cellevents <- DLBCL[,c("FL1", "FL2", "FL4")]
# storing the maanual gating reference from FlowCAP-I:
manual_labels <- DLBCL[,"label"]
```

The function `CytomeTree` builds the binary tree according to our algorithm, that gives the automatic gating (given in the resulting `labels` attribute):
```{r, message=FALSE, results='hide'}
# Build the binary tree:
Tree <- CytomeTree(cellevents, minleaf = 1, t = 0.1)
# Retreive the resulting partition (i.e. automatic gating):
Tree_Partition <- Tree$labels
```
One can fiddle with the `t` threshold to change the desired depth of the tree.

The function `plot_graph` plots a graph representing this binary tree, showing which markers were used in which order to splits the events:
```{r, fig.width = 4, fig.height=4, small.mar=TRUE}
# Plot a graph of the tree (with specific graphical parameters):
plot_graph(Tree, edge.arrow.size = 0.3, Vcex = 0.8, vertex.size = 30)
```

The function `plot_nodes` allows to graphically evaluate the fit of the gaussian family distribution at each node. It can also be used to investigate a particular node:
```{r, small.mar=TRUE, fig.width=6.5, fig.height=6}
# Plot the distribution fit for each node:
plot_nodes(Tree)
```
```{r}
# Plot the distribution fit for a particular node:
plot_nodes(Tree, "FL4.1")
```


### Post-processing annotation of automatically gated subpopulations

The function `Annotation` annotates the subpopulations partitioned by the binary tree:

```{r}
# Run the annotation algorithm:
Annot <- Annotation(Tree,plot=FALSE)
Annot$combinations
```

The post-processing annotation can be compared to the incomplete annotation obtained from the tree alone:
```{r}
# Annotation gotten from the tree:
Tree$annotation
```

This completed annotation eases the search for specific cell types of interest, where `1` represents a positive measurement of a marker, while `0` corresponds to its absence:
```{r}

# seeked cell type: FL2+FL4+
pheno_ex1 <- RetrievePops(Annot, phenotypes = list(rbind(c("FL2", 1), c("FL4", 1))))
pheno_ex1$phenotypesinfo

#One can look for several cell types at once:
phenotypes <- list()
# FL2+FL4-
phenotypes[[1]] <- rbind(c("FL2", 1), c("FL4", 0))
# FL2-FL4+
phenotypes[[2]] <- rbind(c("FL2", 0), c("FL4", 1))
# Retreive corresponding cell populations:
PhenoInfos <- RetrievePops(Annot, phenotypes)
PhenoInfos$phenotypesinfo
```

### Comparison between manual and automatic gating

Finally, we can compare the automatic gating obtained using `cytometree` to the original manual gating (which had `r sum(manual_labels==0)` events inconsistently gated across different operators performing the manual gating on those very same data - so those `r sum(manual_labels==0)` cells were identified as outliers in the reference label and were kept out of the evaluation criteria in the [FlowCAP-I challenge](http://flowcap.flowsite.org/)):

```{r, echo=FALSE, small.mar=TRUE, fig.width=6.5, fig.height=4, message=FALSE}
# F-measure ignoring cells labeled 0 as in FlowCAP-I.
# Use FmeasureC() in any other case.
Fmeas <- cytometree::FmeasureC_no0(ref=manual_labels, pred=Tree_Partition)
# Scatterplots.
library(ggplot2)
library(viridis)
# Ignoring cells labeled 0 as in FlowCAP-I.
# Building the data frame to scatter plot the data.
FL1 <- cellevents[, "FL1"]
FL2 <- cellevents[, "FL2"]
FL4 <- cellevents[, "FL4"]
n <- length(FL1)

man_lab <- factor(as.character(manual_labels))
levels(man_lab) <- c("outliers (manual gating)", "pop1", "pop2")

FL4pos <- RetrievePops(Annot, phenotypes = list(cbind("FL4", 1)))
auto_lab <- factor(as.character(FL4pos$Mergedleaves))
levels(auto_lab) <- c("pop2", "pop1")
Labels <- factor(c(as.character(man_lab), as.character(auto_lab)))

method <- as.factor(c(rep("FlowCap-I manual gating", n), rep("CytomeTree auto gating", n)))
scatter_df <- data.frame("FL2" = FL2, "FL4" = FL4, "Label" = Labels, "method" = method)

p <- ggplot2::ggplot(scatter_df,  ggplot2::aes_string(x = "FL2", y="FL4",colour="Label"))+
 ggplot2::geom_point(alpha = 1,cex = 1)+ 
 ggplot2::scale_colour_manual(values = c("grey", viridis::viridis(4))) +
 ggplot2::facet_wrap(~ method) +
 ggplot2::theme_bw() +
 ggplot2::theme(legend.position="bottom") +
 ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(size=3)))+
 ggplot2::ggtitle(paste("F-measure (manual gating as reference - removing the outliers) =", round(Fmeas, 3)))
p
```

## PBMC sample from the Human Immunology Project

In this example, we will use a dataset from the HIPC ([Human Immunology Project Consortium](https://www.immuneprofiling.org/) program where a PBMC sample from patient 12828 was analyzed by Stanford. 

### Automatic gating step

First, we need to load the package `cytometree` and we can have a look at the data:


```{r, message=FALSE}
data(HIPC)
dim(HIPC)
head(HIPC)
```
We have 6 markers measured ` CCR7`, `CD4`, `CD45RA`, `HLADR`, `CD38`, `CD8` as well as the `label` obtained from manual gating, and `r nrow(HIPC)` cell.

```{r}
# getting the only the cell events with the 3 markers measured:
cellevents <- HIPC[,c("CCR7", "CD4", "CD45RA", "HLADR" ,"CD38" ,"CD8")]
# storing the maanual gating reference from FlowCAP-I:
manual_labels <- HIPC[,"label"]
```
The function `CytomeTree` builds the binary tree according to our algorithm, that gives the automatic gating (given in the resulting `labels` attribute):
```{r, message=FALSE, results='hide'}
# Build the binary tree:
Tree <- CytomeTree(cellevents, minleaf = 1, t = 0.2)
# Retreive the resulting partition (i.e. automatic gating):
Tree_Partition <- Tree$labels
```
One can fiddle with the `t` threshold to change the desired depth of the tree.

The function `plot_graph` plots a graph representing this binary tree, showing which markers were used in which order to splits the events:
```{r, fig.width = 6.2, fig.height = 6.2, small.mar = TRUE}
# Plot a graph of the tree (with specific graphical parameters):
plot_graph(Tree, edge.arrow.size = 0.4, Vcex = 0.45)
```

The function `plot_nodes` allows to graphically evaluate the fit of the gaussian family distribution at each node. It can also be used to investigate a particular node:
```{r, echo=FALSE, small.mar=TRUE, fig.width=6.5, fig.height=4, message=FALSE}
# Plot the fit for 2 specific nodes:
plot_nodes(Tree, list("CD4.1", "CD45RA.7"))
```

### Post-processing annotation of automatically gated subpopulations

The function `Annotation` annotates the subpopulations partitioned by the binary tree:
```{r}
# Run the annotation algorithm:
Annot <- Annotation(Tree,plot=FALSE)
Annot$combinations
```

This completed annotation eases the search for specific cell types of interest, where `1` represents a positive measurement of a marker, while `0` corresponds to its absence:
```{r, fig.width=5, fig.height=5}
#One can look for several cell types at once:
phenotypes <- list()
# CD8-CD4+CCR7-CD45RA+
CD4effector <-  rbind(c("CD8", 0), 
                      c("CD4", 1), 
                      c("CCR7", 0), 
                      c("CD45RA", 1))
phenotypes[[1]] <- CD4effector

# CD8-CD4+CCR7+CD45RA+
CD4naive <-     rbind(c("CD8", 0), 
                      c("CD4", 1), 
                      c("CCR7", 1), 
                      c("CD45RA", 1))
phenotypes[[2]] <- CD4naive

# CD8-CD4+CCR7+CD45RA-
CD4CM <-        rbind(c("CD8", 0), 
                      c("CD4", 1), 
                      c("CCR7", 1), 
                      c("CD45RA", 0))
phenotypes[[3]] <- CD4CM

# CD8-CD4+CCR7+CD45RA+
CD4effectorM <- rbind(c("CD8", 0), 
                      c("CD4", 1), 
                      c("CCR7", 0), 
                      c("CD45RA", 0))
phenotypes[[4]] <- CD4effectorM

# Retreive corresponding cell populations:
PhenoInfos <- RetrievePops(Annot, phenotypes)
# One can find informations about the seeked phenotypes in 
PhenoInfos$phenotypesinfo
# According to PhenoInfos$phenotypesinfo, CD8-CD4+CCR7-CD45RA+ population is labeled 12
# According to PhenoInfos$phenotypesinfo, CD8-CD4+CCR7+CD45RA+ population is labeled 5
# According to PhenoInfos$phenotypesinfo, CD8-CD4+CCR7+CD45RA- population is labeled 4
# According to PhenoInfos$phenotypesinfo, CD8-CD4+CCR7+CD45RA+ population is comprised
# of 4 clusters labeled 8,9,10, 11. They were merged into a new cluster labeled 13.

# The new partition, with merged clusters is to be found in PhenoInfos$Mergedleaves.
auto_labels_merged <- PhenoInfos$Mergedleaves

# Get the indices of the subpopulation comprised of classes labeled 12, 5, 4,13.
subpopulation_indices <- auto_labels_merged %in% c(12, 5, 4, 13)

# Scatter plot the data.
CD45RA <- cellevents[subpopulation_indices, "CD45RA"]
CCR7 <- cellevents[subpopulation_indices, "CCR7"]
auto_lab <- factor(auto_labels_merged[subpopulation_indices])
levels(auto_lab) <- viridis::viridis(length(levels(auto_lab)))
auto_lab <- as.character(auto_lab)
plot(CD45RA, CCR7, col = auto_lab, main = "Automatic gating")
```

## Semi-supervised gating

It is possible to force the markers which will be used to gate the data first,
using the `force_first_markers` argument of the `CytomeTree()` function.
Once those forced markers are used, automatic behavior of the algorithm is 
resumed for the remaining markers.

```{r, message=FALSE, results='hide'}
# Build the binary tree, forcing the first node to be CD4, and the second ones HLADR
Tree <- CytomeTree(cellevents, minleaf = 1, t = 0.2, force_first_markers = c("CD4", "HLADR"))
```
```{r, fig.width = 6.2, fig.height = 6.2, small.mar = TRUE}
# Plot a graph of the tree (with specific graphical parameters):
plot_graph(Tree, edge.arrow.size = 0.4, Vcex = 0.45)
```




