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lomb.R
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lomb.R
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#https:/adriaaulaICM/bbmo_niche_sea/blob/master/src/analysis/seasonality_asvs.R
library(tidyverse)
library(phyloseq)
library(lubridate)
library(lomb)
bl.phy <- viral_physeq
# data_transformation <- function(physeq){
# require(tidyverse)
# require(phyloseq)
#
#
#
# # Relative abundance of OTUs
# phy.relab = transform_sample_counts(physeq, function(x) x / sum(x))
#
# # Log10 abundance of OTUs
# phy.log = transform_sample_counts(physeq, function(x) log10(x+1))
#
# # Rarefied dataset
# phy.raref = rarefy_even_depth(physeq,rngseed = 42)
#
# # Log centered ratio (Aitchison)
# geoMean = function(x, na.rm=TRUE){
# exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
# }
#
# phy.clr <- transform_sample_counts(physeq, function(x) x+1) %>%
# transform_sample_counts(function(x) log(x/geoMean(x)))
#
# print('if this is not close to zero or zero, something is wrong! Beware')
# print(sum(otu_table(phy.clr)[1,]))
#
# print("Starting a psmelt of a large dataset, this could take a while!")
#
# return(list( relab = phy.relab,
# log = phy.log,
# raref = phy.raref,
# clr = phy.clr))
# }
#
# physeq.list <- data_transformation(bl.phy)
#
# bl.phy.relab <- physeq.list$relab
# bl.phy.raref <- physeq.list$raref
# bl.phy.log <- physeq.list$log
# bl.phy.clr <- physeq.list$clr
# (virps_helli <- transform(viral_physeq, transform = "hellinger", target = "OTU"))
# bl.phy.asinh <- transform_sample_counts(bl.phy, function(x) asinh(x))
# physeq.list <- NULL
psmelt_dplyr = function(physeq) {
#Implementation of the psmelt from phyloseq with dplyr
# It is indeed faster, without further complications or differences.
# (well, in fact, its way more prone to give errors if the output is not well established,
# it doesn't check anything)
sd = data.frame(sample_data(physeq)) %>%
rownames_to_column("Sample")
TT = data.frame(as(tax_table(physeq),'matrix')) %>%
rownames_to_column("OTU")
if(taxa_are_rows(physeq)){
otu.table = data.frame(as(otu_table(physeq),"matrix"),
check.names = FALSE) %>%
rownames_to_column("OTU")
} else {
otu.table = data.frame(t(as(otu_table(physeq),"matrix")),
check.names = FALSE) %>%
rownames_to_column("OTU")
}
all <- otu.table %>%
left_join(TT, by = "OTU") %>%
gather_("Sample", "Abundance", setdiff(colnames(otu.table), "OTU")) %>%
left_join(sd, by = "Sample") %>%
select(Sample, Abundance, OTU, everything())
return(all)
}
tsdf.0.2 <- bl.phy %>%
psmelt_dplyr() %>%
mutate(decimaldat = decimal_date(Date))
head(tsdf.0.2)
tail(tsdf.0.2)
#troubleshooting why lomb was not working
tsdf.0.2$OTU %>% head()
tsdf.0.2 %>% select(OTU, decimaldat, Abundance) %>%
group_by(OTU) %>%
summarize( howmany0 = sum(Abundance == 0)) %>%
arrange(-howmany0)
# ~4000+ ASVs that have abundance of 0 in 166 samples
ASVs0 <- tsdf.0.2 %>%
select(OTU, decimaldat, Abundance) %>%
group_by(OTU) %>%
summarize( howmany0 = sum(Abundance == 0)) %>%
arrange(-howmany0) %>%
filter(howmany0 == 166) %>%
pull(OTU)
lomb.02 <- tsdf.0.2 %>%
filter(! OTU %in% ASVs0) %>%
split(.$OTU) %>%
map(~randlsp( x =.x$Abundance,
times = .x$decimaldat,
type = 'period',
plot = F))
par(mar=c(1,1,1,1)) #fix error: Error in plot.new() : figure margins too large
lomb.sea.02 <- tibble( asv = names(lomb.02),
pval = map_dbl(lomb.02, ~.x$p.value),
peak = map_dbl(lomb.02, ~.x$peak),
interval = map(lomb.02, ~.x$peak.at),
int.min = map_dbl(interval, ~.[[2]]),
int.max = map_dbl(interval, ~.[[1]])) %>%
mutate( qval = fdrtool::fdrtool(pval, statistic = 'pvalue')$qval) %>%
filter(qval <= 0.01, peak >= 10, int.max <= 2)
head(lomb.sea.02)
head(lomb.season <- as.data.frame(lomb.sea.02[,c(1, 2,7)]))
write.csv(lomb.season, "lomb_seasonality.csv")
write_rds(lomb.02, 'data/analysis/lomball.rds')
write_rds(lomb.sea.02, 'data/analysis/lombsea.rds')
results.lomb02 <- lomb.02[lomb.sea.02 %>% pull(asv)]
# Strip problems
lil.strip <- theme(strip.background = element_blank(),
strip.text.x =element_text(margin = margin(.05, 0, .1, 0, "cm")))
periodoplots <- map(results.lomb02, ~tibble( scanned = .x$scanned,
power = .x$power)) %>%
bind_rows(.id = 'asv') %>%
split(.$asv) %>%
map(~ggplot(.x, aes(scanned, power)) +
geom_line(aes(group = asv)) +
facet_wrap(~asv) +
lil.strip)
periodoplots$ASV_302