import.Rmd

biom_file_path <- "data/Aggregated_humanization2.biom"
sample_meta_file_path <- "data/Mapping_file_ADHD_aggregated.csv"
tree_file_path <- "data/Data_humanization_phylo_aggregation.tre"

library(mia)

# Imports the data
se <- loadFromBiom(biom_file_path)


names(rowData(se)) <- c("Kingdom", "Phylum", "Class", "Order", 
                        "Family", "Genus")

# Goes through the whole DataFrame. Removes '.*[kpcofg]__' from strings, where [kpcofg] 
# is any character from listed ones, and .* any character.
rowdata_modified <- BiocParallel::bplapply(rowData(se), 
                                           FUN = stringr::str_remove, 
                                           pattern = '.*[kpcofg]__')

# Genus level has additional '\"', so let's delete that also
rowdata_modified <- BiocParallel::bplapply(rowdata_modified, 
                                           FUN = stringr::str_remove, 
                                           pattern = '\"')

# rowdata_modified is a list, so it is converted back to DataFrame format. 
rowdata_modified <- DataFrame(rowdata_modified)

# And then assigned back to the SE object
rowData(se) <- rowdata_modified


# We use this to check what type of data it is
# read.table(sample_meta_file_path)

# It seems like a comma separated file and it does not include headers
# Let us read it and then convert from data.frame to DataFrame
# (required for our purposes)
sample_meta <- DataFrame(read.table(sample_meta_file_path, sep = ",", header = FALSE))

# Add sample names to rownames
rownames(sample_meta) <- sample_meta[,1]

# Delete column that included sample names
sample_meta[,1] <- NULL

# We can add headers
colnames(sample_meta) <- c("patient_status", "cohort", "patient_status_vs_cohort", "sample_name")

# Then it can be added to colData
colData(se) <- sample_meta

# Convert to tse format
tse <- as(se, "TreeSummarizedExperiment")

# Reads the tree file
tree <- ape::read.tree(tree_file_path)

# Add tree to rowTree
rowTree(tse) <- tree