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#### 5. Outliers and batch effects ####

# This section normalises and transforms the count data so that it can be plotted on a PCA plot and a heatmap

## USER INPUT
# Choose the groups you want to plot in a PCA/Heatmap. You can select any 2 or more of the groups (or all of the groups) you have in your 'groups' column of your metadata table
# To see what groups are present, run the following:
unique(meta$group)
# Now add which groups you want to plot (i.e. replace the groupnames below, and add more, separated by a comma and in "quotes", as needed). NOTE: R is case-sensitive, so these group names must be named EXACTLY the same as in the metadata table.
plotgroups <- c("Differentiated_cells", "Basal_cells")

# Pull out only the counts from the above groups
groupcounts <- counts[meta$group %in% plotgroups]

# Normalise counts by library size, using DeSeq2's estimateSizeFactors() function. Note that DeSeq2 does this internally during DEG calling. The normalisation below is done separately for PCA and density plotting.
# Set up the initial DeSeq2 experimental parameters.
condition <- factor(1:length(groupcounts))
# Set up the column data. A data frame of sample ID's and conditions
coldata <- data.frame(row.names=colnames(groupcounts), condition)
# Set up the DeSeq2 data set structure
f <- DESeqDataSetFromMatrix(countData = groupcounts, colData = coldata, design= ~ condition)
# Estimate the size factors. See DeSeq2 manual for details
f <- estimateSizeFactors(f)
# Size factors can be viewed by: sizeFactors(f)

# Multiply each row (sample) by the corresponding size factor
subcount_norm <- as.matrix(groupcounts) %*% diag(sizeFactors(f))
# Re-add column names
colnames(subcount_norm) <- colnames(groupcounts)

## Remove low coverage transcripts (mean count < 10) ##

# Find the mean of each row (and output as a data frame object)
means <- as.data.frame(rowMeans(subcount_norm))
# Then join the means data with the counts
means <- cbind(means, subcount_norm)
# Then subset out only genes with mean > 10
data <- subset(means, means[ , 1] > 10)
# Remove the means column
data <- data[,-1]

# Transform data
data_log <- vst(round(as.matrix(data)))
# Transformation can create some infinite values. Can't generate PCA data on these. Can see how many by: sum(sapply(data_log, is.infinite))
# To remove infinite rows, use 'is.finte' or '!is.infinite'
data_log <- data_log[is.finite(rowSums(data_log)),]
colnames(data_log) <- colnames(groupcounts)

### Set up the PCA plot base data ###

# We're using the FactoMineR package to generate PCA plots (http://factominer.free.fr/index.html)

# Need to transpose the data first
data_log_t <- t(data_log)
# Add the group data
data_log_t_vars <- data.frame(meta$group[meta$group %in% plotgroups], data_log_t)
# Generate the PCA data using FactoMineR package
res.pca <- PCA(data_log_t_vars, quali.sup = 1, graph=FALSE)

## Set up the dendogram/heatmaps base data ##

# Calculate the distance matrix:
distance_matrix <- as.matrix(dist(t(data_log)))