---
title: "Guided tutorial to COTAN V.2"
author: 
- name: "Silvia Giulia Galfrè"
  affiliation: "Department of Computer Science, University of Pisa"

package: COTAN
output: 
    BiocStyle::html_document
vignette: >
  %\VignetteIndexEntry{Guided tutorial to COTAN V.2}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8} 
---

```{r, include = FALSE}
knitr::opts_chunk[["set"]](
  collapse = TRUE,
  comment = "#>"
#  fig.width = 5L,
#  fig.height = 5L
)

```

```{r message=FALSE, warning=FALSE}
library(COTAN)
library(zeallot)
library(data.table)
library(Rtsne)
library(qpdf)
library(GEOquery)
library(rlang)

options(parallelly.fork.enable = TRUE)
```

## Introduction

This tutorial contains the same functionalities as the first release of the
COTAN tutorial but done using the new and updated functions.

## Get the data-set

Download the data-set for `"mouse cortex E17.5"`.

```{r eval=TRUE, include=TRUE}
dataDir <- tempdir()

GEO <- "GSM2861514"
fName <- "GSM2861514_E175_Only_Cortical_Cells_DGE.txt.gz"
dataSetFile <- file.path(dataDir, GEO, fName)

if (!file.exists(dataSetFile)) {
  getGEOSuppFiles(GEO, makeDirectory = TRUE,
                  baseDir = dataDir, fetch_files = TRUE,
                  filter_regex = fName)
}
sample.dataset <- read.csv(dataSetFile, sep = "\t", row.names = 1L)
```

Define a directory where the output will be stored.

```{r}
outDir <- dataDir

# Log-level 2 was chosen to showcase better how the package works
# In normal usage a level of 0 or 1 is more appropriate
setLoggingLevel(2L)

# This file will contain all the logs produced by the package
# as if at the highest logging level
setLoggingFile(file.path(outDir, "vignette_v2.log"))

message("COTAN uses the `torch` library when asked to `optimizeForSpeed`")
message("Run the command 'options(COTAN.UseTorch = FALSE)'",
        " in your session to disable `torch` completely!")

# this command does check whether the torch library is properly installed
c(useTorch, deviceStr) %<-% COTAN:::canUseTorch(TRUE, "cuda")
if (useTorch) {
  message("The `torch` library is availble and ready to use")
  if (deviceStr == "cuda") {
    message("The `torch` library can use the `CUDA` GPU")
  } else {
    message("The `torch` library can only use the CPU")
    message("Please ensure you have the `OpenBLAS` libraries",
            " installed on the system")
  }
}

rm(useTorch, deviceStr)
```

# Analytical pipeline

Initialize the `COTAN` object with the row count table and
the metadata for the experiment.

```{r}
cond <- "mouse_cortex_E17.5"

obj <- COTAN(raw = sample.dataset)
obj <- initializeMetaDataset(obj,
                             GEO = GEO,
                             sequencingMethod = "Drop_seq",
                             sampleCondition = cond)

logThis(paste0("Condition ", getMetadataElement(obj, datasetTags()[["cond"]])),
        logLevel = 1L)
```

Before we proceed to the analysis, we need to clean the data.
The analysis will use a matrix of raw UMI counts as the input.
To obtain this matrix, we have to remove any potential cell doublets or
multiplets, as well as any low quality or dying cells.

## Data cleaning

We can check the library size (UMI number) with an empirical cumulative
distribution function

```{r}
ECDPlot(obj, yCut = 700L)
```

```{r}
cellSizePlot(obj)
```

```{r}
genesSizePlot(obj)
```

During the cleaning, every time we want to remove cells or genes
we can use the `dropGenesCells()`function.

Drop cells with too many reads as they are probably doublets or multiplets
```{r}
cellsSizeThr <- 6000L
obj <- addElementToMetaDataset(obj, "Cells size threshold", cellsSizeThr)

cells_to_rem <- getCells(obj)[getCellsSize(obj) > cellsSizeThr]
obj <- dropGenesCells(obj, cells = cells_to_rem)

cellSizePlot(obj)
```

To drop cells by gene number: high genes count might also indicate doublets...

```{r}
genesSizeHighThr <- 3000L
obj <- addElementToMetaDataset(obj, "Num genes high threshold", genesSizeHighThr)

cells_to_rem <- getCells(obj)[getNumExpressedGenes(obj) > genesSizeHighThr]
obj <- dropGenesCells(obj, cells = cells_to_rem)

genesSizePlot(obj)
```

Drop cells with too low genes expression as they are probably dead

```{r}
genesSizeLowThr <- 500L
obj <- addElementToMetaDataset(obj, "Num genes low threshold", genesSizeLowThr)

cells_to_rem <- getCells(obj)[getNumExpressedGenes(obj) < genesSizeLowThr]
obj <- dropGenesCells(obj, cells = cells_to_rem)

genesSizePlot(obj)
```


Cells with a too high percentage of mitochondrial genes are
likely dead (or at the last problematic) cells. So we drop them!

```{r}
c(mitPlot, mitSizes) %<-% mitochondrialPercentagePlot(obj, genePrefix = "^Mt")

mitPlot
```

```{r}
mitPercThr <- 1.0
obj <- addElementToMetaDataset(obj, "Mitoc. perc. threshold", mitPercThr)

cells_to_rem <- rownames(mitSizes)[mitSizes[["mit.percentage"]] > mitPercThr]
obj <- dropGenesCells(obj, cells = cells_to_rem)

c(mitPlot, mitSizes) %<-% mitochondrialPercentagePlot(obj, genePrefix = "^Mt")

plot(mitPlot)
```


If we do not want to consider the mitochondrial genes we can remove them
before starting the analysis.

```{r}
genes_to_rem <- getGenes(obj)[grep("^Mt", getGenes(obj))]
cells_to_rem <- getCells(obj)[which(getCellsSize(obj) == 0L)]

obj <- dropGenesCells(obj, genes_to_rem, cells_to_rem)
```

We want also to log the current status.

```{r}
logThis(paste("n cells", getNumCells(obj)), logLevel = 1L)
```

The `clean()` function estimates all the parameters for the data. Therefore, we have to run it again every time we remove any genes or cells from the data.

```{r}
obj <- addElementToMetaDataset(obj, "Num drop B group", 0)

obj <- clean(obj)

c(pcaCellsPlot, pcaCellsData, genesPlot,
  UDEPlot, nuPlot, zoomedNuPlot) %<-% cleanPlots(obj)

pcaCellsPlot
```

```{r}
genesPlot
```

We can observe here that the red cells are really enriched in hemoglobin genes so we prefer to remove them. They can be extracted from the `pcaCellsData` object and removed.

```{r eval=TRUE, include=TRUE}
cells_to_rem <- rownames(pcaCellsData)[pcaCellsData[["groups"]] == "B"]
obj <- dropGenesCells(obj, cells = cells_to_rem)

obj <- addElementToMetaDataset(obj, "Num drop B group", 1)

obj <- clean(obj)

c(pcaCellsPlot, pcaCellsData, genesPlot, UDEPlot, nuPlot, zoomedNuPlot) %<-%
  cleanPlots(obj)

pcaCellsPlot
```

To color the PCA based on `nu` (so the cells' efficiency)

```{r}
UDEPlot
```

UDE (color) should not correlate with principal components! This is very important.

The next part is used to remove the cells with efficiency too low.

```{r}
plot(nuPlot)
```

We can zoom on the smallest values and, if COTAN detects a clear elbow,
we can decide to remove the cells.

```{r}
plot(zoomedNuPlot)
```

We also save the defined threshold in the metadata and re-run the estimation

```{r}
UDELowThr <- 0.28
obj <- addElementToMetaDataset(obj, "Low UDE cells' threshold", UDELowThr)

obj <- addElementToMetaDataset(obj, "Num drop B group", 2)

cells_to_rem <- getCells(obj)[getNu(obj) < UDELowThr]
obj <- dropGenesCells(obj, cells = cells_to_rem)
```

Repeat the estimation after the cells are removed

```{r}
obj <- clean(obj)

c(pcaCellsPlot, pcaCellsData, genesPlot, UDEPlot, nuPlot, zoomedNuPlot) %<-%
  cleanPlots(obj)

plot(pcaCellsPlot)
```

```{r}
scatterPlot(obj)
```

```{r}
logThis(paste("n cells", getNumCells(obj)), logLevel = 1L)
```

## COTAN analysis

In this part, all the contingency tables are computed
and used to get the statistics necessary to `COEX` evaluation and storing

```{r}
obj <- proceedToCoex(obj, calcCoex = TRUE,
                     optimizeForSpeed = TRUE, cores = 6L,
                     saveObj = TRUE, outDir = outDir)
```

When `saveObj == TRUE`, in the previous step, this step can be skipped
as the `COTAN` object has already been saved in the `outDir`.

```{r eval=FALSE, include=TRUE}
# saving the structure
saveRDS(obj, file = file.path(outDir, paste0(cond, ".cotan.RDS")))
```

## Automatic run

It is also possible to run directly a single function
if we don't want to clean anything.

```{r eval=FALSE, include=TRUE}
obj2 <- automaticCOTANObjectCreation(
  raw = sample.dataset,
  GEO = GEO,
  sequencingMethod = "Drop_seq",
  sampleCondition = cond,
  calcCoex = TRUE, cores = 6L, optimizeForSpeed = TRUE,
  saveObj = TRUE, outDir = outDir)
```

# Analysis of the elaborated data

## GDI

To calculate the `GDI` we can run:

```{r}
quantP <- calculateGDI(obj)
head(quantP)
```

The next function can either plot the `GDI` for the dataset directly or
use the pre-computed dataframe.
It marks the given threshold 1.43 (in red) and
the 50% and 75% quantiles (in blue).
We can also specify some gene sets (three in this case) that
we want to label explicitly in the `GDI` plot.

```{r}
genesList <- list(
  "NPGs" = c("Nes", "Vim", "Sox2", "Sox1", "Notch1", "Hes1", "Hes5", "Pax6"),
  "PNGs" = c("Map2", "Tubb3", "Neurod1", "Nefm", "Nefl", "Dcx", "Tbr1"),
  "hk"   = c("Calm1", "Cox6b1", "Ppia", "Rpl18", "Cox7c", "Erh", "H3f3a",
             "Taf1", "Taf2", "Gapdh", "Actb", "Golph3", "Zfr", "Sub1",
             "Tars", "Amacr")
)

# needs to be recalculated after the changes in nu/dispersion above
#obj <- calculateCoex(obj, actOnCells = FALSE)

GDIPlot(obj, GDIIn = quantP, cond = cond,
        genes = genesList, GDIThreshold = 1.43)
```

The percentage of cells expressing the gene in the third column of this
data-frame is reported.

## Heatmaps

To perform the Gene Pair Analysis, we can plot a heatmap of the `COEX` values
between two gene sets.
We have to define the different gene sets (`list.genes`) in a list.
Then we can choose which sets to use in the function parameter sets
(for example, from 1 to 3).
We also have to provide an array of the file name prefixes for each condition
(for example, "mouse_cortex_E17.5").
In fact, this function can plot genes relationships across many different
conditions to get a complete overview.

```{r}
heatmapPlot(obj, genesLists = genesList)
```

We can also plot a general heatmap of `COEX` values based on some markers like
the following one.

```{r}
genesHeatmapPlot(obj, primaryMarkers = c("Satb2", "Bcl11b", "Vim", "Hes1"),
                 pValueThreshold = 0.001, symmetric = TRUE)
```

```{r eval=FALSE, include=TRUE}
genesHeatmapPlot(obj, primaryMarkers = c("Satb2", "Bcl11b", "Fezf2"),
                 secondaryMarkers = c("Gabra3", "Meg3", "Cux1", "Neurod6"),
                 pValueThreshold = 0.001, symmetric = FALSE)
```

## Get data tables

Sometimes we can also be interested in the numbers present directly in the
contingency tables for two specific genes. To get them we can use two functions:

`contingencyTables()` to produce the observed and expected data

```{r}
print("Contingency Tables:")
contingencyTables(obj, g1 = "Satb2", g2 = "Bcl11b")

print("Corresponding Coex")
getGenesCoex(obj)["Satb2", "Bcl11b"]
```

Another useful function is `getGenesCoex()`. This can be used to extract
the whole or a partial `COEX` matrix from a `COTAN` object.

```{r}
# For the whole matrix
coex <- getGenesCoex(obj, zeroDiagonal = FALSE)
coex[1000L:1005L, 1000L:1005L]
```

```{r}
# For a partial matrix
coex <- getGenesCoex(obj, genes = c("Satb2", "Bcl11b", "Fezf2"))
coex[1000L:1005L, ]
```

## Establishing genes' clusters

`COTAN` provides a way to establish genes' clusters given some lists of markers

```{r eval=TRUE, include=TRUE}
layersGenes <- list(
  "L1"   = c("Reln",   "Lhx5"),
  "L2/3" = c("Satb2",  "Cux1"),
  "L4"   = c("Rorb",   "Sox5"),
  "L5/6" = c("Bcl11b", "Fezf2"),
  "Prog" = c("Vim",    "Hes1")
)
c(gSpace, eigPlot, pcaGenesClDF, treePlot) %<-%
  establishGenesClusters(obj, groupMarkers = layersGenes,
                         numGenesPerMarker = 25L, kCuts = 5L)

plot(eigPlot)
```

```{r eval=TRUE, include=TRUE}
plot(treePlot)
```

```{r eval=TRUE, include=TRUE}
UMAPPlot(pcaGenesClDF[, 1L:10L],
         clusters = rlang::set_names(pcaGenesClDF[["hclust"]],
                                     rownames(pcaGenesClDF)),
         elements = layersGenes,
         title = "Genes' clusters UMAP Plot")
```

## Uniform Clustering

It is possible to obtain a cell clusterization based on the concept of 
*uniformity* of expression of the genes across the cells.
That is the cluster satisfies the null hypothesis of the `COTAN` model:
the genes expression is not dependent on the cell in consideration.

There are two functions involved into obtaining a proper clusterization:
the first is `cellsUniformClustering` that uses standard tools clusterization
methods, but then discards and re-clusters any *non-uniform* cluster.
Please not that the most important parameter for the users is the
`GDIThreshold`: it defines how strict is the uniformity check,
good values are between 1.42 (more strict) and 1.43 (less strict).

```{r eval=FALSE, include=TRUE}
c(splitClusters, splitCoexDF) %<-%
  cellsUniformClustering(obj, GDIThreshold = 1.43, initialResolution = 0.8,
                         optimizeForSpeed = TRUE, cores = 6L,
                         saveObj = TRUE, outDir = outDir)
obj <- addClusterization(obj, clName = "split",
                         clusters = splitClusters, coexDF = splitCoexDF)
```

In the case one already has an existing clusterization, it is possible to
calculate the *DEA* `data.frame` and add it to the `COTAN` object.
```{r eval=TRUE, include=TRUE}
data("vignette.split.clusters", package = "COTAN")
splitClusters <- vignette.split.clusters

splitCoexDF <- DEAOnClusters(obj, clusters = splitClusters)

obj <- addClusterization(obj, clName = "split", clusters = splitClusters,
                         coexDF = splitCoexDF, override = FALSE)
```

It is possible to have some statistics about the *clusterization*
```{r eval=TRUE, include=TRUE}
c(summaryData, summaryPlot) %<-%
  clustersSummaryPlot(obj, clName = "split", plotTitle = "split summary")

summaryData
```

The `ExpGenes` column contains the number of genes that are expressed in any
cell of the relevant *cluster*, while the `ExpGenes25` column contains the number of genes expressed in at the least 25% of the cells of the relevant *cluster*
```{r eval=TRUE, include=TRUE}
plot(summaryPlot)
```


It is possible to visualize how relevant are the *marker genes'* `lists` with
respect to the given *clusterization*
```{r eval=TRUE, include=TRUE}
c(., scoreDF) %<-% clustersMarkersHeatmapPlot(obj, groupMarkers = layersGenes,
                                              clName = "split", kCuts = 5L)
```

However since the algorithm that creates the clusters is not geared directly
to achieve cluster uniformity, there might be clusters that can be merged back
together and still be uniform. This is the purpose of the function 
`mergeUniformCellsClusters` that takes a clusterization and tries to merge
cluster pairs after checking that together the pair forms a uniform cluster.
In order to avoid the massive possible checks the function relies on a *related*
distance the find the cluster pairs that will be most likely merged. 
Again the most important parameter for the users is the `GDIThreshold`, it is 
supposed to be the same as the one used to generate the clusterization in the
first place, but if needed it can be slightly relaxed in order to reduce the
number of clusters.

```{r eval=FALSE, include=TRUE}
c(mergeClusters, mergeCoexDF) %<-%
  mergeUniformCellsClusters(obj, clusters = splitClusters, GDIThreshold = 1.43,
                            optimizeForSpeed = TRUE, cores = 6L,
                            saveObj = TRUE, outDir = outDir)

# merges are:
#  1 <- '-1' + 1 + 4
#  2 <- 2 + 3 + 8
#  3 <- 5
#  4 <- 6
#  5 <- 7

obj <- addClusterization(obj, clName = "merge",
                         clusters = mergeClusters, coexDF = mergeCoexDF)
```

In the case one already has an existing clusterization, it is possible to
calculate the *DEA* `data.frame` and add it to the `COTAN` object.
```{r eval=TRUE, include=TRUE}
data("vignette.merge.clusters", package = "COTAN")
mergeClusters <- vignette.merge.clusters

mergeCoexDF <- DEAOnClusters(obj, clusters = mergeClusters)

obj <- addClusterization(obj, clName = "merge",
                         clusters = mergeClusters, coexDF = mergeCoexDF)
```


In the following graph, it is possible to check that the found clusters align
very well to the expression of the layers' genes defined above...

```{r eval=TRUE, include=TRUE}
c(.,scoreDF) %<-% clustersMarkersHeatmapPlot(obj, groupMarkers = layersGenes,
                                             clName = "merge", kCuts = 5L)
```

## Vignette clean-up stage

The next few lines are just to clean.

```{r}
if (file.exists(file.path(outDir, paste0(cond, ".cotan.RDS")))) {
  #Delete file if it exists
  file.remove(file.path(outDir, paste0(cond, ".cotan.RDS")))
}
if (file.exists(file.path(outDir, paste0(cond, "_times.csv")))) {
  #Delete file if it exists
  file.remove(file.path(outDir, paste0(cond, "_times.csv")))
}
if (dir.exists(file.path(outDir, cond))) {
  unlink(file.path(outDir, cond), recursive = TRUE)
}
if (dir.exists(file.path(outDir, GEO))) {
  unlink(file.path(outDir, GEO), recursive = TRUE)
}

# stop logging to file
setLoggingFile("")
file.remove(file.path(outDir, "vignette_v2.log"))

options(parallelly.fork.enable = FALSE)
```

```{r}
Sys.time()
```

```{r}
sessionInfo()
```
