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gifrop_classify.R
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gifrop_classify.R
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args = commandArgs(trailingOnly=TRUE)
setwd(args[1])
# CHANGE THIS TO GIFROP_CLASSIFY.R
suppressPackageStartupMessages(library(dplyr, quietly = TRUE, warn.conflicts = FALSE))
suppressPackageStartupMessages(library(tidyr, quietly = TRUE, warn.conflicts = FALSE))
suppressPackageStartupMessages(library(readr, quietly = TRUE, warn.conflicts = FALSE))
suppressPackageStartupMessages(library(tibble, quietly = TRUE, warn.conflicts = FALSE))
# suppressPackageStartupMessages(library(ggplot2, quietly = TRUE, warn.conflicts = FALSE))
# suppressPackageStartupMessages(library(purrr, quietly = TRUE, warn.conflicts = FALSE))
# suppressPackageStartupMessages(library(igraph))
### ONLY FOR HERE FOR TESTING ###
# setwd('/home/julian/Documents/gifrop_examples/test5')
# setwd('/project/fsep_004/jtrachsel/klima/assembly/both/second_flye_polish/pananal/plasmids/pan/')
# setwd('/home/julian/Documents/gifrop/test_data/pan')
#getwd()
## read in island info data ##
island_info <- read_csv('./gifrop_out/my_islands/island_info.csv', col_types = c('cccddddcddlccc'))
# current_directory <- getwd()
# seq_dat_path <- paste0(current_directory, '/gifrop_out/sequence_data/')
gff_files <- list.files(path = './gifrop_out/sequence_data/', pattern = 'short.gff', full.names = TRUE)
# This creates a vector of column specifications to be passed to the read_csv function
# I had trouble with some of the locus tag column types being guessed as logical
pan_cols <- c('ccciidiiiiciii')
locus_tag_cols <- rep_len('c', length(gff_files)) %>% paste(sep = '', collapse = '')
all_cols <- paste(pan_cols, locus_tag_cols, sep = '', collapse = '')
# this is where the gene, presence/absense is read in
gpa <- read_csv('./gene_presence_absence.csv', col_types = all_cols)
# READ IN ABRICATE RESULTS #
pwd <- getwd()
print('reading in abricate files...')
# read in plasmidfinder results and bind together
plasfiles <- list.files(path = './gifrop_out/my_islands/abricate/', pattern = 'plasmidfinder', full.names = TRUE)
plasfinders <- lapply(plasfiles, read_tsv, col_types = c('ccddcccccddcccc')) #
plasfinders <- bind_rows(plasfinders)
# concatenate all plasmid genes and produce plasmid type per island
plasmid_types <- plasfinders %>%
mutate(gene_percent=paste(GENE,'<', `%COVERAGE`,'%', '>', sep = '')) %>%
group_by(SEQUENCE) %>%
summarise(plasmid_type=paste(gene_percent, sep = '~', collapse = '~'),
.groups='drop') %>%
transmute(island_ID=SEQUENCE, plasmid_type=plasmid_type)
# read in vfdb results and bind together
vfdbfiles <- list.files(path = './gifrop_out/my_islands/abricate/', pattern = 'vfdb', full.names = TRUE)
vfdbs <- lapply(vfdbfiles, read_tsv, col_types = c('ccddcccccddcccc'))
vfdbs <- bind_rows(vfdbs)
# concatenate all vfdb genes and produce plasmid type per island
vir_types <- vfdbs%>%
mutate(gene_percent=paste(GENE,'<', `%IDENTITY`,'%', '>', sep = '')) %>%
group_by(SEQUENCE) %>%
summarise(vir_type=paste(gene_percent, sep = '~', collapse = '~'),
.groups='drop') %>%
transmute(island_ID=SEQUENCE, vir_type=vir_type)
# read in ncbi resistance results
resfiles <- list.files(path = './gifrop_out/my_islands/abricate/', pattern = 'ncbi', full.names = TRUE)
resfinders <- lapply(resfiles, read_tsv, col_types = c('ccddcccccddcccc'))
resfinders <- bind_rows(resfinders) %>% filter(`%COVERAGE` > 66)
# concatenate all resfinder genes and produce res_type for all islands
res_types <- resfinders%>%
mutate(gene_percent=paste(GENE,'<', `%IDENTITY`,'%', '>', sep = '')) %>%
group_by(SEQUENCE) %>%
summarise(res_type=paste(gene_percent, sep = '~', collapse = '~'),
.groups='drop') %>%
transmute(island_ID=SEQUENCE, res_type=res_type)
# virotypes
virofiles <- list.files(path = './gifrop_out/my_islands/abricate/', pattern = 'viroseqs', full.names = TRUE)
virofinders <- lapply(virofiles, read_tsv, col_types = c('ccddcccccddcccc')) # check this coltypes
virofinders <- bind_rows(virofinders) %>% filter(`%COVERAGE` > 66)
# concatenate all resfinder genes and produce viro_type for all islands
viro_types <- virofinders%>%
mutate(gene_percent=paste(GENE,'<', `%IDENTITY`,'%', '>', sep = '')) %>%
group_by(SEQUENCE) %>%
summarise(viro_type=paste(gene_percent, sep = '~', collapse = '~'),
.groups='drop') %>%
transmute(island_ID=SEQUENCE, viro_type=viro_type)
# megares/bacmet : metals and biocides
megares_files <- list.files(path = './gifrop_out/my_islands/abricate/', pattern = 'megares', full.names = TRUE)
megares <- bind_rows(lapply(megares_files, read_tsv, col_types = c('ccddcccccddcccc'))) %>%
filter(`%COVERAGE` > 66)
megares_types <- megares%>%
mutate(gene_percent=paste(GENE,'<', `%IDENTITY`,'%', '>', sep = '')) %>%
group_by(SEQUENCE) %>%
summarise(megares_type=paste(gene_percent, sep = '~', collapse = '~')) %>%
transmute(island_ID=SEQUENCE, megares_type=megares_type)
#
# might need to check in on things here # in case island_IDs got garbled somehow
allbricates <- bind_rows(plasfinders, vfdbs, resfinders, virofinders, megares) %>%
mutate(island_ID=SEQUENCE) %>%
select(island_ID, everything(), -SEQUENCE)
print('Done reading in abricate files')
# this block creates a 'resistance type' by concatenating all the detected resistances into a string.
res_info <- allbricates %>%
filter(!is.na(RESISTANCE)) %>% group_by(island_ID) %>%
mutate(RESISTANCE=paste(unique(sort(RESISTANCE)), collapse = '|', sep = '|')) %>%
select(island_ID, RESISTANCE) %>% unique()
# this block creates a broad 'island type'
# if an island has a hit to one of the five database types it gets assigned that type
# all types are then concatenated to form the final island type
island_types <- allbricates %>%
select(island_ID, DATABASE) %>%
unique() %>%
mutate(DATABASE=case_when(
DATABASE == 'plasmidfinder' ~ 'plasmid',
DATABASE == 'ncbi' ~ 'AMR',
DATABASE == 'vfdb' ~ 'virulence',
DATABASE == 'PHAGE' ~ 'phage',
DATABASE == 'BacMet' ~ 'metals/biocides')) %>%
group_by(island_ID) %>%
mutate(island_type = paste(DATABASE, sep = '_', collapse = '_')) %>%
ungroup() %>% select(island_ID, island_type) %>%
unique() %>%
left_join(res_info)
island_info %>%
left_join(island_types) %>%
left_join(res_types) %>%
left_join(vir_types) %>%
left_join(plasmid_types) %>%
left_join(viro_types) %>%
left_join(megares_types) %>%
write_csv('./gifrop_out/classified_island_info.csv')
print('Done with island classification')
### END READ IN ABRICATE STUFF ###
### TEST ZONE # COMMENT OUT
# Island PA heatmap? #
# pseudo #
# for all secondary clusters that have variability in the number of genes
# make
# heatmap where rows are genes and columns are individual islands
# histogram of number of genes
## this dataframe tries to assess how consistent all islands within a cluster are
# looks at how variable the number of genes is for all islands in a cluster
# it seems like ((max_genes - min_genes) / min_genes) gives a pretty good indication
# of variability in the cluster
# THESE ARE THE CLUSTER QUALITIES OF THE quat_clusters NOW
# cluster_qual <-
# clust_info %>% group_by(primary_cluster, secondary_cluster, tertiary_cluster, quat_cluster) %>%
# summarise(mean_genes = mean(num_genes),
# med_genes = median(num_genes),
# var_genes = var(num_genes),
# sd_genes = sd(num_genes),
# min_genes = min(num_genes),
# max_genes = max(num_genes),
# num_occur = length(num_genes),
# maxmin_divmin = (max_genes - min_genes)/min_genes)
#
#
#
# # variable_clusters <- cluster_qual %>% filter(maxmin_divmin != 0)
#
#
# filt_helper <- function(test_vec, int_vec){
# res <- any(int_vec %in% test_vec)
# return(res)
# }
#######
# This might be nice but is broken when there are some islands with only 1 gene on them.
# this makes a heatmap of all the genes in a specified cluster (or vector of clusters) in each ISLAND
#
# gene_by_island_heatmap <- function(gpa_clust, QUAT_CLUST){
# # gpa clust need to be the gene_presence_absence.csv file with the added clustering information
# # in addition the 'secondary_cluster' column needs to be a list formatted column
# # secondary cluster can be a vector of secondary clusters you want to see together
# # ISSUE!! currently genes from other sclusts are pulled in, if a gene is in two different sclusts
# # it will show up in these heatmaps but will not have annotation info with it.
# # need to include another filtering step to remove islands not belonging to Sclust at hand
#
#
# anno <- clust_info %>%
# filter(quat_cluster %in% QUAT_CLUST) %>%
# select(island_ID, island_type) %>%
# column_to_rownames(var = 'island_ID')
#
# PA <- gpa_clust %>%
# filter(map_lgl(.x=all_Qclusters, .f=filt_helper, QUAT_CLUST)) %>%
# unnest(cols = all_islands) %>%
# select(Gene, all_islands) %>%
# group_by(all_islands, Gene) %>%
# tally() %>%
# ungroup() %>%
# spread(key = Gene, value = n, fill = 0) %>%
# column_to_rownames(var = 'all_islands') %>%
# as.matrix() %>%
# t() # to get genes as rows and cols as islands
#
# QUAT_CLUST <- paste(QUAT_CLUST, collapse = '_', sep = '_')
# MAIN=paste('Presence/absence of genes among islands in quaternary cluster',QUAT_CLUST)
# FILENAME=paste('./gifrop_out/Qclust_', QUAT_CLUST,'_gene_heatmap.jpeg', sep = '')
#
# width=ncol(PA)/5
# if (width < 6){
# width <- 6
# }
#
# height=nrow(PA)/10
#
# if (height < 6){
# height <- 6
# }
#
#
# pheatmap(PA, filename = FILENAME,
# height = height,
# width = width,
# main=MAIN,
# annotation_col = anno)
#
# }
#
# imperfect_clusters <- cluster_qual %>%
# filter(maxmin_divmin > 0) %>%
# pull(quat_cluster)
#
# lapply(imperfect_clusters, gene_by_island_heatmap, gpa_clust = gpa_clust)
# dev.off()
# gene_by_island_heatmap(gpa_clust = gpa_clust, SECONDARY_CLUSTER = 9)
########
# gene_by_island_heatmap(gpa_clust = gpa_clust, SECONDARY_CLUSTER = 120)
# THIS NEEDS TO BE UPDATED TO QUAT CLUSTERS
## secondary cluster by genome heatmaps here
# anno <- clust_info %>%
# select(genome_name, quat_cluster, island_type) %>%
# group_by(quat_cluster) %>%
# summarise(consensus_type=paste(unique(island_type), sep = '~', collapse = '~')) %>%
# left_join(cluster_qual) %>%
# transmute(quat_cluster = quat_cluster,
# # primary_cluster = factor(primary_cluster),
# cluster_variability = maxmin_divmin,
# consensus_type = consensus_type) %>%
#
# column_to_rownames(var = 'quat_cluster')
#
#
# anno_length <- max(nchar(anno$consensus_type))/4
#
# # this produces a heat map of the number of times islands from each secondary cluster show up in each genome
# quat_clust_by_genome <- clust_info %>%
# select(genome_name, quat_cluster) %>%
# group_by(genome_name, quat_cluster) %>%
# tally() %>%
# spread(key=genome_name, value = n, fill = 0) %>%
# column_to_rownames(var = 'quat_cluster') %>%
# as.matrix()
#
#
# width=(ncol(quat_clust_by_genome)+anno_length)/4
# if (width < 6){
# width <- 6
# }
#
# height=nrow(quat_clust_by_genome)/10
#
# if (height < 6){
# height <- 6
# }
#
#
# pheatmap(quat_clust_by_genome,
# annotation_row = anno,
# height=height,
# width=width,
# filename = './gifrop_out/quat_clusters_by_genome.jpeg',
# main='Presence of genomic island clusters in each genome',
# sub='highly variable clusters are indicated in green',
# fontsize = 5)
# dev.off()
#
# # COPY FOR TERTIARY
# # COPTY FOR QUATERNARY
#
#