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t1d_project.r
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t1d_project.r
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## Custom metadata loading function (define and pass to proc.project() when default
## implementation load.meta.default() is not sufficient)
load.meta.t1d <- function(file.name,batch=NULL,aggr.var=NULL) {
meta =read.delim(file.name, header=TRUE,stringsAsFactors=T, sep="\t")
make.global(meta)
names(meta) =
replace.col.names(
names(meta),
c("YAP_Aliq_ID","Sample.Name","family.ID", "Subject.ID",
"gender", "autoantibody.status", "T1D.status",
"year.of.birth","date.collected","date.of.diagnosis",
"timestamp","sample.type"),
c("AliquotID","SampleID", "FamilyID", "SubjectID",
"gender", "AA", "T1D",
"YearOfBirth","Specimen.Collection.Date","Date.of.Diagnosis",
"Timestamp","Specimen.Type")
)
meta = meta[!duplicated(meta$SampleID),]
meta$gender = unlist( lapply( meta$gender, substr, 1,1 ))
meta$Batch = 4
## We set Batch=0 downstream as a special value, it should not be
## present already
stopifnot(sum(meta$Batch==0) == 0)
meta$BatchRepeat=as.factor(1)
meta$Specimen.Collection.Date =
as.date(as.character(meta$Specimen.Collection.Date),order="mdy")
meta$age = as.numeric(
meta$Specimen.Collection.Date
-
as.date(paste(meta$YearOfBirth,"-06-01",sep=""),order="ymd")
)/365
##day is fake
meta$Date.of.Diagnosis = as.date(as.character(meta$Date.of.Diagnosis),order="mdy")
meta$YearsSinceDiagnosis = as.numeric(
meta$Specimen.Collection.Date
-
meta$Date.of.Diagnosis
)/365
meta$YearsSinceDiagnosis.quant = quantcut.ordered(meta$YearsSinceDiagnosis)
meta$T1D[meta$T1D=="Unknown"] = "Control"
meta$Timestamp =
strptime(as.character(meta$Timestamp),format = "%m/%d/%Y %H:%M")
meta$BioSampleID = paste(meta$SubjectID,meta$Specimen.Type,as.numeric(meta$Timestamp),sep="_")
make.global(meta)
meta$age.quant = quantcut.ordered(meta$age)
meta$A1C = as.double(as.character(meta$A1C))
#meta$A1C[is.na(meta$A1C)] = 6.2
meta$A1C.quant = quantcut.ordered(meta$A1C)
##DESeq2 fails to automatically construct model matrix with ordered factor, so
##we create a separate unordered factor for for DESeq2 (with with the same order
##of levels)
meta$A1C.quart = factor(meta$A1C,ordered=F)
meta = arrange(meta,SubjectID,Timestamp)
## Filtering and aggregation of rows has to be done before we start marking repeats for throwing
## out repeats
if(!is.null(batch)) {
report$add.p(paste(c("Filtering metadata by batch:",batch),collapse=" "))
batch.mask = (meta$Batch %in% batch)
meta = meta[batch.mask,]
}
## That would leave only AA defined samples or T1D patients (under
## an assumption that T1D patients are always AA positive even if AA is not measured)
#meta = meta[meta$AA!="Unknown" | meta$T1D == "T1D",]
## Subject is at least first repeat, but bio sample is first occurence, in time order
## (e.g. second visit)
isBioRepeatFirst = duplicated(meta$SubjectID) & ! duplicated(meta$BioSampleID)
BioSampleIDRep = meta$BioSampleID[isBioRepeatFirst]
meta$isBioRepeat = meta$BioSampleID %in% BioSampleIDRep
## Now do the same as above, but ignore failed samples when looking
## for bio repeats
meta_qa = meta[meta$Sample.QA=="PASS",]
isBioRepeatFirst = duplicated(meta_qa$SubjectID) & ! duplicated(meta_qa$BioSampleID)
BioSampleIDRep = meta_qa$BioSampleID[isBioRepeatFirst]
## mark all failed samples or bio repeats following at least one good sample
meta$isBioRepeatOrFailed = (meta$BioSampleID %in% BioSampleIDRep) |
! (meta$Sample.QA=="PASS")
SampleIDRep = meta_qa$SampleID[duplicated(meta_qa$SubjectID)]
## mark all failed samples or repeats following at least one good sample
meta$isRepeatOrFailed = (meta$SampleID %in% SampleIDRep) |
! (meta$Sample.QA=="PASS")
##
## This section builds a difference in months between next samples and first sample for every subject
##
meta_keys = meta[,c("SubjectID","SampleID","BioSampleID","Timestamp","isBioRepeat","isBioRepeatOrFailed")]
meta_keys_non_rep = meta_keys[!meta$isBioRepeatOrFail,]
#join() will keep duplicate names, so we rename the names, except SubjectID merge key
names(meta_keys_non_rep) = paste(names(meta_keys_non_rep),".first",sep="")
names(meta_keys_non_rep)[1] = "SubjectID"
meta_keys_1 = join(meta_keys,meta_keys_non_rep,by=c("SubjectID"),match="first",type="left")
stopifnot(meta$SampleID==meta_keys_1$SampleID)
stopifnot(is.na(meta_keys_1$Timestamp.first) |
(meta_keys_1$Timestamp >= meta_keys_1$Timestamp.first) |
! (meta$Sample.QA=="PASS")) #should not see otherwise
meta_keys_1$MonthsAfterFirstBioSample = as.numeric(meta_keys_1$Timestamp - meta_keys_1$Timestamp.first,units="days")/30
## NA will be at records that did not pass QA. Set to 0.
meta_keys_1$MonthsAfterFirstBioSample[is.na(meta_keys_1$MonthsAfterFirstBioSample)] = 0
##
## uncheck those bio repeats that passed QA and were made less than 9 mon after the first sampling - they
## are testing intentional repeats and should be aggregated just like batch repeats when analyzing
## unpaired group differences
##
meta_keys_1$isAnnualRepeatOrFailed = meta_keys_1$isBioRepeatOrFailed &
! (
(meta$Sample.QA=="PASS") &
meta_keys_1$MonthsAfterFirstBioSample < 9
)
make.global(meta_keys_1)
meta$isAnnualRepeatOrFailed = meta_keys_1$isAnnualRepeatOrFailed
meta$MonthsAfterFirstBioSample = meta_keys_1$MonthsAfterFirstBioSample
meta$TimestampMonth = as.numeric(meta$Timestamp - min(meta$Timestamp),units="days")/30
meta$Timestamp = as.numeric(meta$Timestamp)
meta$TimestampDate = as.Date(timeDate(meta$Timestamp))
meta$TimestampMonth.quant = quantcut.ordered(meta$TimestampMonth)
meta$HLA_status = as.character(meta$HLA_status)
meta$HLA_status[meta$HLA_status=="UNKNOWN"] = NA
meta$HLA_status[meta$HLA_status=="NO RISK"] = "HLA_NO_RISK"
meta$HLA_status[meta$HLA_status=="RISK"] = "HLA_RISK"
meta$HLA_status = factor(meta$HLA_status)
meta$HLA_status = relevel(meta$HLA_status,"HLA_NO_RISK")
meta$FamilyID.T1D = paste(meta$FamilyID,meta$T1D,sep=".")
row.names(meta) = meta$SampleID
##pass filter argument and filter here before aggregation but after repeats etc are marked
if(!is.null(aggr.var)) {
meta = aggregate.by.meta.data(meta,
group_col=aggr.var,
col_ignore=names(meta))
}
##Setting Control to base level plays nice with DESeq2 defaults
meta$T1D = relevel(meta$T1D,"Control")
return (meta)
}
## This function should carry out analysis specific to metadata fields by themselves, without
## relation to the abundance profiles. You can write it to do nothing (empty body).
summary.meta.t1d <- function(m_a) {
report$add.header("Summary of metadata variables")
report$add.header("Summary of metadata variables after filtering samples")
meta = m_a$attr
report$add.printed(summary(meta),caption="Summary of metadata variables")
xtabs.formulas = list("~T1D","~T1D+FamilyID","~FamilyID","~SubjectID","~AA+T1D","~HLA_status+T1D")
for(xtabs.formula in xtabs.formulas) {
fact.xtabs = xtabs(as.formula(xtabs.formula),data=meta,drop.unused.levels=T)
report$add(fact.xtabs,caption=paste("Sample cross tabulation",xtabs.formula))
report$add.printed(summary(fact.xtabs))
}
with(meta,{
report$add(aov(age~T1D),
caption="ANOVA for age and cohort")
report$add(qplot(T1D,age,geom="violin"),
caption="Violin plot for age and cohort")
#summary(glht(lm(age~visit,data=meta[meta$Sample.type=="patient",]),linfct="visit=0"))
#summary(glht(lmer(age~visit+(visit|Sample.type),data=meta),linfct="visit=0"))
report$add(ggplot(meta,aes(x=age,y=YearsSinceDiagnosis,color=T1D))+
geom_point()+
stat_smooth(method="loess", se = T,degree=1,size=1),
caption="Plot for age and time since diagnosis with Loess trend line")
})
with(meta,{
report$add(cor.test(YearsSinceDiagnosis,
Timestamp,
method="spearman"),
caption="Spearman RHO for metadata variables")
report$add(cor.test(YearsSinceDiagnosis,
age,
method="spearman"),
caption="Spearman RHO for metadata variables")
report$add(cor.test(YearsSinceDiagnosis,
A1C,
method="spearman"),
caption="Spearman RHO for metadata variables")
})
}
new_ordination.task <- function(main.meta.var) {
within(mgsat.16s.task.template$test.counts.task$ordination.task, {
distance="euclidean"
ord.tasks = list(
list(
ordinate.task=list(
method="RDA"
##other arguments to phyloseq:::ordinate
),
plot.task=list(
type="samples",
color=main.meta.var
##other arguments to phyloseq:::plot_ordination
)
),
list(
ordinate.task=list(
method="RDA",
formula=main.meta.var
##other arguments to phyloseq:::ordinate
),
plot.task=list(
type="samples",
color=main.meta.var
##other arguments to phyloseq:::plot_ordination
)
)
)
})
}
## This function must generate a lits with analysis tasks
gen.tasks.t1d <- function() {
get.taxa.meta.aggr.base<-function(m_a) {
m_a = subset.m_a(m_a,subset=(m_a$attr$Sample.QA=="PASS"))
report$add.p(paste("After filtering for QAed samples:",nrow(m_a$count)))
m_a = subset.m_a(m_a,subset=(!m_a$attr$isRepeatOrFailed))
report$add.p(paste("After filtering for repeated samples per subject:",nrow(m_a$count)))
return (m_a)
}
task0 = within( mgsat.16s.task.template, {
#DEBUG:
taxa.levels = c(2,3,4,5,6,7,"otu")
#taxa.levels = c(2)
descr = "All samples, aggregated by AliquotID"
main.meta.var = "T1D"
read.data.task.yap = within(read.data.task, {
taxa.summary.file = NA
otu.shared.file="yap/*.shared"
cons.taxonomy.file="yap/*.seq.taxonomy"
taxa.summary.file.otu = NA
})
read.data.task.yap.stirrups = within(read.data.task, {
taxa.summary.file = NA
otu.shared.file="yap/stirrups/*.shared"
cons.taxonomy.file="yap/stirrups/*.fixed.seq.taxonomy"
taxa.summary.file.otu = NA
})
read.data.task.mothur.cdhit = within(read.data.task, {
taxa.summary.file = "stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pds.wang.tax.summary"
})
read.data.task = within(read.data.task.yap.stirrups, {
meta.file="aliq_id_to_metadata_20150403.tsv"
#meta.file="aliq_id_to_metadata_for_T1D_YAP_run_20140922_batches.tsv"
load.meta.method=load.meta.t1d
load.meta.options=list(aggr.var="AliquotID")
count.filter.options = list()
# count.filter.options = within(list(), {
# drop.unclassified=T
# min_quant_mean_frac=0.25
# min_quant_incidence_frac=0.25
# #min_max=30
# min_mean=10
# })
otu.count.filter.options=list()
})
get.taxa.meta.aggr<-function(taxa.meta) {
return (get.taxa.meta.aggr.base(taxa.meta))
}
summary.meta.method=summary.meta.t1d
test.counts.task = within(test.counts.task, {
#count.filter.feature.options = list()
count.filter.feature.options = within(list(), {
drop.unclassified=T
min_quant_mean_frac=0.25
min_quant_incidence_frac=0.25
#min_max=30
min_mean=10
})
norm.count.task = within(norm.count.task, {
#method="norm.ihs.prop"
#method.args = list(theta=2000)
#method="norm.rlog.dds"
#method.args=list(dds=NA) #signals to pull Deseq2 object
method="norm.clr"
method.args=list()
})
adonis.task = within(adonis.task, {
dist.metr="euclidean"
col.trans=NULL
norm.count.task=NULL
data.descr="normalized counts"
})
heatmap.abund.task = within(heatmap.abund.task,{
trans.clust=NULL
stand.clust=NULL
dist.metr="euclidian"
trans.show=NULL
stand.show="range"
cluster.row.cuth=10
})
heatmap.combined.task = within(heatmap.combined.task, {
hmap.width=1000
hmap.height=hmap.width*0.8
attr.annot.names=c(main.meta.var,"age","TimestampMonth")
clustering_distance_rows="pearson"
km.abund=0
km.diversity=0
})
ordination.task = new_ordination.task(main.meta.var)
})
})
task1 = within( task0, {
descr = "All samples, aggregated by AliquotID"
#taxa.levels = c(2)
do.summary.meta = F
do.tests = T
summary.meta.task = within(summary.meta.task, {
meta.x.vars = c("TimestampMonth")
group.vars = c(main.meta.var)
})
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = T
do.genesel = T
do.stabsel = T
do.glmer = F
#do.divrich = c()
do.plot.profiles.abund=T
do.heatmap.abund=T
divrich.task = within(divrich.task,{
#n.rar.rep=4
is.raw.count.data=T
group.attr = main.meta.var
counts.glm.task = within(list(),{
formula.rhs = main.meta.var
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = main.meta.var
})
genesel.task = within(genesel.task, {
group.attr = main.meta.var
})
stabsel.task = within(stabsel.task, {
resp.attr=main.meta.var
})
adonis.task = within(adonis.task, {
tasks = list(
list(formula.rhs="T1D",
strata=NULL,
descr="Association with the patient/control status unpaired"),
list(formula.rhs="T1D",
strata="FamilyID",
descr="Association with the patient/control status paired by family"),
list(formula.rhs="T1D*age",
strata=NULL,
descr="Association with the patient/control status and age unpaired"),
list(formula.rhs="age*T1D",
strata=NULL,
descr="Association with the patient/control status and age unpaired")
)
})
glmer.task = within(glmer.task, {
tasks = list(list(
descr.extra = "",
formula.rhs = paste(main.meta.var,"(1|SampleID)",sep="+"),
linfct=c("T1DT1D = 0")
))
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(main.meta.var))
feature.meta.x.vars=c("YearsSinceDiagnosis","TimestampDate","age")
do.profile=T
do.feature.meta=F
})
heatmap.abund.task = within(heatmap.abund.task,{
attr.annot.names=c(main.meta.var)
})
})
})
task.species = within( task1, {
taxa.levels = c("7")
read.data.task = within(read.data.task, {
#count.filter.options = list()
count.filter.options = within(count.filter.options, {
keep.names = function(count,count_norm,...) {
colnames(count)[grepl("Streptococcus*",colnames(count))]
}
#min_max=30
#min_mean=10
})
taxa.levels.mix = 1
})
})
task1.1 = within( task1, {
descr = "All samples, aggregated by AliquotID, control for age and paired GeneSelector"
do.summary.meta = F
do.tests = T
summary.meta.task = within(summary.meta.task, {
meta.x.vars = c("TimestampMonth")
group.vars = c(main.meta.var)
})
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = F
do.genesel = T
do.stabsel = F
do.glmer = F
#do.divrich = c()
do.plot.profiles.abund=F
do.heatmap.abund=F
divrich.task = within(divrich.task,{
#n.rar.rep=4
is.raw.count.data=T
group.attr = main.meta.var
counts.glm.task = within(list(),{
formula.rhs = paste("age",main.meta.var,sep="+")
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = paste("age",main.meta.var,sep="+")
})
genesel.task = within(genesel.task, {
group.attr = main.meta.var
genesel.param = within(genesel.param, {
block.attr = "FamilyID"
type="paired"
})
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(main.meta.var))
feature.meta.x.vars=c("YearsSinceDiagnosis","TimestampDate","age")
do.profile=T
do.feature.meta=F
})
ordination.task = new_ordination.task("TimestampMonth")
})
})
task1.3 = within( task0, {
descr = "All samples, aggregated by AliquotID, Within/Between distance test"
taxa.levels = c(2,3,6,"otu")
#taxa.levels = c(2)
do.summary.meta = F
do.tests = T
get.taxa.meta.aggr<-function(m_a) {
m_a = task0$get.taxa.meta.aggr(m_a)
m_a = aggregate.by.meta.data.m_a(m_a,group_col="FamilyID.T1D")
meta = m_a$attr
meta$T1D = factor(meta$T1D)
meta$FamilyID = factor(meta$FamilyID)
meta.aggr = join(meta,
ddply(meta,"FamilyID",summarise,
has.sibling=("T1D" %in% T1D) & ("Control" %in% T1D)),
by="FamilyID",
match="first")
stopifnot(!any(is.na(meta.aggr$has.sibling)) &&
nrow(meta.aggr)==nrow(meta))
m_a$attr = meta.aggr
m_a = subset.m_a(m_a,subset=(m_a$attr$has.sibling))
return(m_a)
}
test.counts.task = within(test.counts.task, {
do.deseq2 = F
do.adonis = F
do.genesel = F
do.stabsel = F
do.glmer = F
do.divrich = c()
do.extra.method = taxa.levels
do.plot.profiles.abund=F
do.heatmap.abund=F
extra.method.task = within(extra.method.task, {
func = function(m_a,m_a.norm,res.tests,norm.count.task.extra) {
test.dist.matr.within.between(m_a=m_a,
group.attr=main.meta.var,
block.attr="FamilyID",
n.perm=4000,
norm.count.task=norm.count.task.extra
)
}
norm.count.task.extra = within(norm.count.task, {
})
})
})
})
task1.4 = within( task1, {
descr = "All samples, aggregated by AliquotID, last quartile of collection timestamp"
taxa.levels = c(6,"otu")
#taxa.levels = c(2)
do.summary.meta = F
do.tests = T
get.taxa.meta.aggr<-function(m_a) {
m_a = task1$get.taxa.meta.aggr(m_a)
m_a = subset.m_a(m_a,subset=(m_a$attr$TimestampMonth.quant==
levels(m_a.here$attr$TimestampMonth.quant)[4]))
return(m_a)
}
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = T
do.genesel = F
do.stabsel = F
do.glmer = F
do.divrich = c()
do.plot.profiles.abund=F
do.heatmap.abund=F
})
})
task2 = within( task0, {
do.summary.meta = F
do.tests = T
descr = "Patient samples only, association with years since diagnosis"
get.taxa.meta.aggr<-function(m_a) {
m_a = get.taxa.meta.aggr.base(m_a)
m_a = subset.m_a(m_a,subset=(m_a$attr$T1D=="T1D"))
return (m_a)
}
main.meta.var = "YearsSinceDiagnosis.quant"
main.meta.var.cont = "YearsSinceDiagnosis"
summary.meta.task = within(summary.meta.task, {
meta.x.vars = c("TimestampMonth")
group.var = c(main.meta.var)
})
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = T
do.genesel = F
do.stabsel = T
do.glmer = F
#do.divrich = c()
do.plot.profiles.abund=T
do.heatmap.abund=T
divrich.task = within(divrich.task,{
#n.rar.rep=4
is.raw.count.data=T
group.attr = main.meta.var
counts.glm.task = within(list(),{
formula.rhs = main.meta.var.cont
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = main.meta.var.cont
})
genesel.task = within(genesel.task, {
group.attr = main.meta.var
})
stabsel.task = within(stabsel.task, {
resp.attr=main.meta.var.cont
args.fitfun = within(args.fitfun, {
family="gaussian"
})
})
adonis.task = within(adonis.task, {
tasks = list(
list(formula.rhs=main.meta.var.cont,
strata=NULL,
descr=sprintf("Association with %s",main.meta.var.cont))
)
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(main.meta.var))
feature.meta.x.vars=c("age")
do.profile=T
do.feature.meta=F
})
heatmap.abund.task = within(heatmap.abund.task,{
attr.annot.names=c(main.meta.var.cont)
})
heatmap.combined.task = within(heatmap.combined.task, {
attr.annot.names=c(main.meta.var,"age","A1C")
})
ordination.task = new_ordination.task(main.meta.var)
})
})
task3 = within( task2, {
do.summary.meta = F
do.tests = T
descr = "Patient samples only, association with A1C"
main.meta.var = "A1C.quant"
main.meta.var.cont = "A1C"
get.taxa.meta.aggr<-function(m_a) {
m_a = task2$get.taxa.meta.aggr(m_a)
m_a = subset.m_a(m_a,subset=(!is.na(m_a$attr$A1C.quant)))
return (m_a)
}
test.counts.task = within(test.counts.task, {
divrich.task = within(divrich.task,{
group.attr = main.meta.var
counts.glm.task = within(list(),{
formula.rhs = main.meta.var.cont
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = main.meta.var.cont
})
genesel.task = within(genesel.task, {
group.attr = main.meta.var
})
stabsel.task = within(stabsel.task, {
resp.attr=main.meta.var.cont
})
adonis.task = within(adonis.task, {
tasks = list(
list(formula.rhs=main.meta.var.cont,
strata=NULL,
descr=sprintf("Association with %s",main.meta.var.cont))
)
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(main.meta.var))
})
heatmap.abund.task = within(heatmap.abund.task,{
attr.annot.names=c(main.meta.var.cont)
})
ordination.task = new_ordination.task(main.meta.var)
})
})
task3.1 = within( task3, {
do.summary.meta = F
do.tests = T
descr = "Patient samples only, association with age and A1C"
main.meta.var = "A1C.quant"
main.meta.var.cont = "A1C"
control.meta.var = "age.quant"
control.meta.var.cont = "age"
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = T
do.genesel = F
do.stabsel = F
do.divrich = c()
do.plot.profiles.abund=F
do.heatmap.abund=F
divrich.task = within(divrich.task,{
group.attr = NULL
counts.glm.task = within(list(),{
formula.rhs = paste(control.meta.var.cont,main.meta.var.cont,sep="+")
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = paste(control.meta.var.cont,main.meta.var.cont,sep="+")
})
adonis.task = within(adonis.task, {
tasks = list(
list(formula.rhs=paste(control.meta.var.cont,main.meta.var.cont,sep="+"),
strata=NULL,
descr=sprintf("Association with %s and %s",control.meta.var.cont,main.meta.var.cont))
)
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(control.meta.var,main.meta.var))
})
})
})
task3.2 = within( task3, {
do.summary.meta = F
do.tests = T
descr = "Patient samples only, association with A1C, testing top quartile vs bottom quartile"
main.meta.var = "A1C.quant"
main.meta.var.cont = "A1C"
control.meta.var = "age.quant"
control.meta.var.cont = "age"
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = F
do.genesel = F
do.stabsel = F
do.divrich = c()
do.plot.profiles.abund=F
do.heatmap.abund=F
deseq2.task = within(deseq2.task, {
formula.rhs = "A1C.quart"
})
})
})
task.hla = within( task0, {
#taxa.levels = c(6)
do.summary.meta = T
do.tests = T
descr = "Association with HLA risk status"
get.taxa.meta.aggr<-function(m_a) {
m_a = get.taxa.meta.aggr.base(m_a)
m_a = subset.m_a(m_a,subset=(!is.na(m_a$attr$HLA_status)))
return (m_a)
}
main.meta.var = "HLA_status"
summary.meta.task = within(summary.meta.task, {
group.vars = c(main.meta.var)
})
test.counts.task = within(test.counts.task, {
do.deseq2 = T
do.adonis = T
do.genesel = T
do.stabsel = T
do.glmer = F
#do.divrich = c()
do.plot.profiles.abund=T
do.heatmap.abund=T
divrich.task = within(divrich.task,{
group.attr = main.meta.var
counts.glm.task = within(list(),{
formula.rhs = main.meta.var
})
})
deseq2.task = within(deseq2.task, {
formula.rhs = main.meta.var
})
genesel.task = within(genesel.task, {
group.attr = main.meta.var
})
stabsel.task = within(stabsel.task, {
resp.attr=main.meta.var
args.fitfun = within(args.fitfun, {
family="binomial"
})
})
adonis.task = within(adonis.task, {
tasks = list(
list(formula.rhs=main.meta.var,
strata=NULL,
descr=sprintf("Association with %s",main.meta.var))
)
})
plot.profiles.task = within(plot.profiles.task, {
id.vars.list = list(c(main.meta.var),c(main.meta.var,"T1D"))
})
heatmap.abund.task = within(heatmap.abund.task,{
attr.annot.names=c(main.meta.var,"T1D")
})
heatmap.combined.task = within(heatmap.combined.task, {
attr.annot.names=c(main.meta.var,"T1D","age")
})
ordination.task = new_ordination.task(main.meta.var)
})
})
task.hla.species = within( task.hla, {
taxa.levels = c("7")
read.data.task = within(read.data.task, {
#count.filter.options = list()
count.filter.options = within(count.filter.options, {
keep.names = function(count,count_norm,...) {
colnames(count)[grepl("Streptococcus*",colnames(count))]
}
#min_max=30
#min_mean=10
})
taxa.levels.mix = 1
})
})
task.hla.control = within( task0, {
do.summary.meta = F
do.tests = T
descr = "Association with HLA risk status, controls only"
get.taxa.meta.aggr<-function(m_a) {