---
title: Example workflow for processing of raw spectral cytometry files
author: 
- name: Jakob Theorell
  affiliation: 
  - Oxford Autoimmune Neurology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  - Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
date: "`r Sys.Date()`"
vignette: >
  %\VignetteIndexEntry{Example workflow for processing of raw spectral cytometry files}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}    
output: 
  BiocStyle::html_document:
    titlecaps: false
    toc_float: true
---

```{r style, echo=FALSE, results='hide', message=FALSE}
library(BiocStyle)
library(knitr)
library(flowCore)
opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)
knitr::opts_chunk$set(echo = TRUE)
```

# Introduction

In this document, I aim at showing a typical analysis of a spectral cytometry
file, including the construction of the spectral decomposition matrix, the
actual decomposition, correction of the resulting file (as there generally
are minor differences between the single-stained controls and the fully
stained sample) and finally converting the resulting flowFrame or flowSet to a 
dataframe that can be used for any downstream application. 
Note: This whole package is very much dependent on 
**flowCore**, and much of the functionality herein works as an extention of the
basic **flowCore** functionality. 

# Installation
This is how to install the package, if that has not already been done: 
```{r, eval = FALSE}
if(!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("flowSpecs")
```

# Example data description

The dataset that is used in this vinjette, and that is the example dataset in 
the package generally, is a PBMC sample stained with 12 fluorochrome-conjugated
antibodies against a wide range of leukocyte antigens. Included is also a set
of single-stained controls, that fill the same function with spectral cytometry
as in conventional ditto. The files were generated on a 44 channel Cytek Aurora®
instrument 2018-10-25. 
```{r}
library(flowSpecs)
library(flowCore)
data("unmixCtrls")
unmixCtrls
data('fullPanel')
fullPanel[,seq(4,7)]
```

As can be noted, flowSpecs adheres to flowCore standards, and thus uses 
flowFrames and flowSets as input to all user functions. 


# Construction of spectral unmixing matrix
To do this, we need the single-stained unmixing controls. As the fluorescent
sources can be of different kinds, such as from antibodies, fluorescent 
proteins, or dead cell markers, the specMatCalc function accepts any number 
of different such groups. However, the groups need to have a common part of
their names. If this was not the case during acquisition, the names of the fcs
files can always be changed afterwards. To check the names, run the 
sampleNames function from flowCore: 
```{r}
sampleNames(unmixCtrls)
```

This shows that we have three groups of samples: "Beads", "Dead" and "PBMC". The
two first are groups that define the fluorochromes from antibodies and the dead 
cell marker (which is pacific orange-NHS in this case). The last one, "PBMC", 
will be used for autofluorescence correction. For obvious reasons, the autofluo
control should always be from the same type of sample as the samples that will
be analyzed downstream. 
With this knowledge about the groups of samples, we can now create the matrix: 

```{r}
specMat <- specMatCalc(unmixCtrls, groupNames = c("Beads_", "Dead_"), 
                        autoFluoName = "PBMC_unstained.fcs")
str(specMat)
```
Here we can see that a matrix with the original fluorescence detector names
as column names, and the new fluorochrome/marker names as row names has been 
created. The function does a lot of preprocessing, with automatic gating of the
most dominant population, etc, to ensure the best possible resolution and 
consistency in the determination of the matrix. 

# Spectral unmixing

Now it is time to apply the newly constructed specMat to the fully stained 
sample. This is done in the following way: 

```{r}
fullPanelUnmix <- specUnmix(fullPanel, specMat)
fullPanelUnmix
```
Notable is that the names now have been exchanged for the fluorescent molecules
instead of the detector channels. The algorithm below this function is currently
least squares regression. 

# Transformation
As with all cytometry data, for correct interpretation, the data needs to be 
transformed using one of the lin-log functions. As the arcsinh function  is 
widely used and also has a single co-function that controls the level of 
compression aroud zero, it is used in this package. The function has a number
of built-in features, such as automatic detection of if the file comes from
mass or flow cytometry, and will give differenc cofactors accordingly. It is
however always the best practice to set the cofactors individually, to ensure
that no artifactual populations are created, which can happen, if there is 
too much resolution around zero. One automated strategy for this, which 
would make the arcTrans function unnecessary, is to use the [flowVS](https://www.bioconductor.org/packages/release/bioc/html/flowVS.html) package.

The arcTrans function requires the names of the variables that should be 
transformed to be specified.

```{r}
fullPanelTrans <- arcTrans(fullPanelUnmix, transNames = 
                            colnames(fullPanelUnmix)[6:18])
par(mfrow=c(1,2))
hist(exprs(fullPanelUnmix)[,7], main = "Pre transformation", 
     xlab = "AF700_CD4", breaks = 200)
hist(exprs(fullPanelTrans)[,7], main = "Post transformation", 
     xlab = "AF700_CD4", breaks = 200)
```
As can be seen in the histograms, the ranges, scales and resolution have now 
changed dramatically. 
(Biologically, the three peaks correspond to CD4- cells, CD4+myeloid cells and 
CD4+T-cells, respectively). 

# Investigation of possible unmixing artifacts 

An important step in the early processing of cytometry files is to investigate
if, or rather where, unmixing artifacts have arisen. There are multiple reasons
for the occurrence of such artifacts, but listing them are outside of the scope
of this vinjette. In the package, there is one function that is well suited for
for this task, and that is the oneVsAllPlot function. When used without 
specifying a marker, the function will create a folder and save all possible
combinations of markers to that folder. Looking at them gives a good overview
of the data. In this case, for the vinjette purpose, I am only plotting one 
of the multi-graphs. 

```{r}
oneVsAllPlot(fullPanelTrans, "AF647_IgM", saveResult = FALSE)
```

This shows a typical artifact between BV650_CD56 and AF647_IgM: it is 
biologically extremely unlikely that the higher expression one sees of CD56, 
the more extremely below zero do the values become for IgM_AF647. 

# Correction of artifacts

Now to one of the more controversial subjects of cytometry, that rightly causes
alarm amongst anyone concerned about reproducibility: the correction of 
artifacts. When this is done aided by fluorescence-minus-one controls (and an
automated function with that purpose is being considered for this package), it 
is less controversial, but even without them, one can follow a few rules, to
increase the usefulness of the data. It is namely important to note, that if 
artifacts, of the kind we will now start to correct, are left in the data, then
they are likely to cause incorrect interpretation of the results: 
As a rule of thumb, one can assume negative correlations for 
single-positive markers (i.e. positive for x but negative for y) to always be
artifacts, as true populations below a negative population cannot exist. Strong
positive correlations are unlikely, but occur in biology, so caution and 
biological considerations should be taken before any corrections of such are 
attempted, but they should nonetheless be considered, as leaving them in can
cause harm. 

Normally, when correcting flow cytometry results, one just changes the 
compensation matrix. In this case, however, where the compensation matrix is not
symmetrical, that becomes a non-trivial affair. For that reason, this package
introduces a correction matrix, which is a secondary, symmetric matrix only 
meant to be used on already unmixed files. It can for that reason take both
positive and negative values. 

When starting the correction phase, we have to create an empty correction
matrix. 

```{r}
corrMat <- corrMatCreate(specMat)
```

This is how this correction matrix is meant to be used: 
A value of 1 corresponds to +100% correction, a value of 0, to 0% correction. 
Thus: if the value 1 is added to the coordinate [x,y], then if event 1 has a 
value of 50 in marker x, then event 1 will get +50 in marker y. 

Practically, in our case, we see that it seems like BV650_CD56 is slightly 
"overunmixed" from AF647_IgM. This means that we should add a negative 
correction. Let us start with 0.1, or 10%. 

```{r}
corrMat["BV650_CD56", "AF647_IgM"] <- -0.1
fullPanelCorr <- correctUnmix(fullPanelUnmix, corrMat)
oneVsAllPlot(fullPanelCorr, "AF647_IgM", saveResult = FALSE)
```
Here, a few things can be noted. First, the correction function takes the non-
transformed file as input. Second, there is an automatic transformation within
this function, as it would be tedious, always having to rerun the arcTrans 
function during this phase, that is generally quite repetitive. 
Thrdly, we overdid it, as the population is now clearly "undermixed" instead,
with a considerable bleed-over of CD56+ cells into the IgM marker channel. 
Thus, we repeat it with a lower value. 

```{r}
corrMat["BV650_CD56", "AF647_IgM"] <- -0.03
fullPanelCorr <- correctUnmix(fullPanelUnmix, corrMat)
oneVsAllPlot(fullPanelCorr, "AF647_IgM", saveResult = FALSE)
```
This time, the result was satisfactory. There are other minor defects in the 
unmixing, however, such as between AF647_IgM and PE_X. This is typically the 
case, and as long as this needs to be done manually, it will use considerable
time, especialy for more complex panels. 
Notable is also that as we are just changing the correction matrix, and
redoing the analysis from the unmixed file every time, we do not need to take
the previous values into consideration. 

# Connecting to other non-flowCore compliant applications
Many clustering algorithms and similar take a matrix-like input. If data is
to be combined from multiple fcs files, and clustered together, the most 
convenient way might be to create a long data frame containing identifiers as
separate columns from the flowSet. The flowSpecs package contains a function to
do this. It works also for single flowFrames, but there it might be easier to
just extract the data with the exprs() function from flowCore. 

To set up our file for the task, we will convert it to a flowSet and change its
currently non-existent name to something useful. 

```{r}
fullPanelFs <- flowSet(fullPanelTrans)
sampleNames(fullPanelFs) <- "PBMC_full_panel_d1.fcs"
```

The function we are goning to use can chop up the name of the file into
multiple strings, if the right information is added in a gsub-compliant
format. These strings are then added as new columns to the resulting dataframe,
and if the fcs files have been systematically named (or their sampleNames 
changed to something systematic in accordance with the example above), we will
in this way be able to group the data based on the new categorizing columns.

```{r}
fullPanelDf <- flowSet2LongDf(fullPanelFs, idInfo =
        list("Tissue" = "|_full_panel_..\\.fcs", 
             "Donor" = "...._full_panel_|\\.fcs"))
str(fullPanelDf)
```
This dataframe can now be used in other applications. 

# Summary
In this vinjette, a typical spectral cytometry analysis is performed, which 
is currently the main objective with the package. However, a number of functions
for automatic gating, CyTOF fcs file cleanup, etc are in the pipe line and will
be added to the package in the coming months, together with new vinjettes. 

# Session information
```{r}
sessionInfo()
```
