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biodiversity-metrics
Commit 0393da18 authored by Eric Coissac's avatar Eric Coissac
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Initial commit of the project

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DESCRIPTION 0 → 100644
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Package: MetabarSchool
Type: Package
Title: A Tutorial package on Biodiversity indices and metabarcoding
Version: 1.0
Date: 2019-01-31
Author: Eric Coissac
Maintainer: Eric Coissac <eric.coissac@metabarcoding.org>
Description: More about what it does (maybe more than one line)
License: What license is it under?
NAMESPACE 0 → 100644
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exportPattern("^[[:alpha:]]+")
R/entropy.R 0 → 100644
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#' @author Eric Coissac
#' @export
H.q = function(x,q=1) {
sum(x * log.q(1/x,q),na.rm = TRUE)
}
#' @author Eric Coissac
#' @export
D.q = function(x,q=1) {
exp.q(H.q(x,q),q)
}
#' @author Eric Coissac
#' @export
H.spectrum = function(x,q=1) {
sapply(q,function(Q) H.q(x,Q))
}
#' @author Eric Coissac
#' @export
D.spectrum = function(x,q=1) {
sapply(q,function(Q) D.q(x,Q))
}
R/generalized_log.R 0 → 100644
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#' @importFrom Rdpack reprompt
#'
NULL
#' Generalized logaritmic function.
#'
#' \deqn{x \longmapsto 1 : \log(x) \approx x-1}
#'
#'
#' @references
#' \insertRef{Tsallis:94:00}{ROBITools2}
#'
#'
#' @author Eric Coissac
#' @export
log.q = function(x,q=1) {
if (q==1)
log(x)
else (x^(1-q)-1)/(1-q)
}
#' Generalized exponential function.
#'
#'
#' @references
#' \insertRef{Tsallis:94:00}{ROBITools2}
#'
#' @author Eric Coissac
#' @export
exp.q = function(x,q=1,base=exp(1)) {
if (q==1)
exp(x)
else
(1 + (1-q)*x)^(1/(1-q))
}
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#' @export
mode <- function(x) {
d <- density(x)
d$x[which.max(d$y)]
}
#' @export
tag_bad_pcr = function(samples,counts,plot = TRUE) {
counts = decostand(counts,method = "hellinger")
bc = aggregate(counts,
by=list(factor(as.character(samples))),
mean)
bc.name = as.character(bc[,1])
bc = bc[-1]
rownames(bc)=bc.name
bc = bc[as.character(samples),]
d = sqrt(rowSums((counts - bc)^2))
names(d) = as.character(samples)
d.m = mode(d)
d.sd = sqrt(sum((d[d <= d.m] - d.m)^2)/sum(d <= d.m))
d.max = aggregate(d,
by = list(factor(as.character(samples))),
max)
d.max.names = d.max[,1]
d.max = d.max[,2]
names(d.max) = d.max.names
d.max = d.max[as.character(samples)]
d.len = aggregate(d,
by = list(factor(as.character(samples))),
length)
d.len.names = d.len[,1]
d.len = d.len[,2]
names(d.len) = d.len.names
d.len = d.len[as.character(samples)]
keep = ((d < d.m + (d.sd*2)) | d!=d.max) & d.len > 1
selection = data.frame(samples = as.character(samples),
distance= d,
maximum = d.max,
repeats = d.len,
keep = keep,
stringsAsFactors = FALSE)
rownames(selection)=rownames(counts)
attributes(selection)$dist.mode = d.m
attributes(selection)$dist.sd = d.sd
if (plot) {
hist(d)
abline(v=d.m,lty=2,col="green")
abline(v=d.m + (d.sd*2),lty=2,col="red")
}
return(selection)
}
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RawData/litiere_ins_cl97_agg_filt_tax.tab 0 → 100644
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RawData/positifs.uniq.annotated.txt 0 → 100644
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RawData/prepare_data.R 0 → 100644
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positive = read.delim("RawData/positifs.uniq.annotated.txt",
sep="\t",
header = TRUE)
columns = names(positive)
columns.info = c("id", "dilution", "species_name", "taxid", "true", "sequence")
columns.counts= columns[grep("^sample\\.",columns)]
positive.count = t(positive[,columns.counts])
motus = as.data.frame(positive[,columns.info])
positive.motus = data.frame(dilution = as.numeric(motus$dilution)/2,
species = as.character(motus$species_name),
taxid = as.integer(motus$taxid),
true = motus$true == "True"
)
samples.names = rownames(positive.count)
samples = t(simplify2array(strsplit(samples.names,split="_")))
# [,1] [,2] [,3] [,4] [,5] [,6]
# [1,] "sample.TM" "POS" "d16" "1" "a" "A1"
# [2,] "sample.TM" "POS" "d16" "1" "a" "B1"
# [3,] "sample.TM" "POS" "d16" "1" "b" "A2"
# [4,] "sample.TM" "POS" "d16" "1" "b" "B2"
# [5,] "sample.TM" "POS" "d16" "2" "a" "A1"
# [6,] "sample.TM" "POS" "d16" "2" "a" "B1"
samples = as.data.frame(samples[,3:6])
names(samples) = c("dilution","repeats","PCR","Plate")
positive.samples = data.frame(dilution = 32%/%as.integer(substr(as.character(samples$dilution),2,10)),
repeats = interaction(samples[,2:4],drop = TRUE)
)
rownames(positive.samples)= samples.names
rownames(positive.count) = samples.names
rownames(positive.motus) = positive$id
colnames(positive.count) = positive$id
plants.16 = positive.motus[positive.motus$true,][,c(2,3,1)]
plants.16 = plants.16[order(1/plants.16$dilution),]
plants.16$log10.dilution = - seq_len(nrow(plants.16)) / log(10)*log(2)
plants.16$dilution = 1/(2^seq_len(nrow(plants.16)))
usethis::use_data(positive.samples,overwrite = TRUE)
usethis::use_data(positive.motus,overwrite = TRUE)
usethis::use_data(positive.count,overwrite = TRUE)
usethis::use_data(plants.16,overwrite = TRUE)
positive.clean = read.delim("RawData/positifs.uniq.annotated.clean.txt",
sep="\t",
header = TRUE)
columns = names(positive.clean)
columns.info = c("id", "dilution", "species_name", "taxid", "true", "sequence")
columns.counts= columns[grep("^sample\\.",columns)]
positive.clean.count = t(positive.clean[,columns.counts])
motus.clean = as.data.frame(positive.clean[,columns.info])
positive.clean.motus = data.frame(dilution = as.numeric(motus.clean$dilution)/2,
species = as.character(motus.clean$species_name),
taxid = as.integer(motus.clean$taxid),
true = motus.clean$true == "True"
)
samples.names = rownames(positive.clean.count)
samples = t(simplify2array(strsplit(samples.names,split="_")))
samples = as.data.frame(samples[,3:6])
names(samples) = c("dilution","repeats","PCR","Plate")
positive.clean.samples = data.frame(dilution = 32%/%as.integer(substr(as.character(samples$dilution),2,10)),
repeats = interaction(samples[,2:4],drop = TRUE)
)
rownames(positive.clean.samples)= samples.names
rownames(positive.clean.count) = samples.names
rownames(positive.clean.motus) = positive.clean$id
colnames(positive.clean.count) = positive.clean$id
usethis::use_data(positive.clean.samples,overwrite = TRUE)
usethis::use_data(positive.clean.motus,overwrite = TRUE)
usethis::use_data(positive.clean.count,overwrite = TRUE)
#
# Litter/Soil dataset
#
guiana = read.delim("RawData/litiere_ins_cl97_agg_filt_tax.tab",
header = TRUE,
sep="\t")
columns = names(guiana)
columns.info = c("id","best_identity.order_filtered_embl_r136_noenv_INS",
"taxid",
"phylum_name","order_name","class_name","family_name","genus_name","species_name",
"sequence")
columns.counts= columns[grep("^sample\\.",columns)]
samples.names = gsub(pattern = "sample.",
replacement = "",
columns.counts)
guiana.count = t(guiana[,columns.counts])
motus = as.data.frame(guiana[,columns.info])
guiana.motus = data.frame(id = paste("EUK",sprintf("%06d",1:nrow(motus)),sep=""),
best_id = motus$best_identity.order_filtered_embl_r136_noenv_INS,
taxid = as.integer(motus$taxid),
species = factor(as.character(motus$species_name)),
genus = factor(as.character(motus$genus_name)),
family = factor(as.character(motus$family_name)),
class = factor(as.character(motus$class_name)),
order = factor(as.character(motus$order_name)),
phylum = factor(as.character(motus$phylum_name)),
sequence = as.character(motus$sequence),
stringsAsFactors = FALSE
)
samples = read.delim("RawData/Litiere_sample_list.txt",header=TRUE)
guiana.samples = samples[samples.names,]
guiana.samples$sample = as.factor(sub("_r.$","",samples.names))
rownames(guiana.count) = samples.names
colnames(guiana.count) = guiana.motus$id
rownames(guiana.motus) = guiana.motus$id
usethis::use_data(guiana.samples,overwrite = TRUE)
usethis::use_data(guiana.motus,overwrite = TRUE)
usethis::use_data(guiana.count,overwrite = TRUE)
biodiversity-metrics.Rproj 0 → 100644
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Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: ISO-8859-1
RnwWeave: knitr
LaTeX: pdfLaTeX
AutoAppendNewline: Yes
BuildType: Package
PackageUseDevtools: Yes
PackageInstallArgs: --no-multiarch --with-keep.source
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figures/alpha_diversity.svg 0 → 100644
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