Description Usage Arguments Details Value Author(s) References See Also Examples
Significantly responsive items are selected using one of the three proposed methods: a quadratic trend test, a linear trend test or an ANOVAbased test.
1 2 3 4 5  itemselect(omicdata, select.method = c("quadratic", "linear", "ANOVA"),
FDR = 0.05, max.ties.prop = 0.2)
## S3 method for class 'itemselect'
print(x, nfirstitems = 20, ...)

omicdata 
An object of class 
select.method 

FDR 
The threshold in term of FDR (False Discovery Rate) for selecting responsive items. 
max.ties.prop 
The maximal tolerated proportion of tied values for each item, above which the item cannot be selected (must be in ]0, 0.5], and by default fixed at 0.2  see details for a description of this filtering step). 
x 
An object of class 
nfirstitems 
The maximum number of selected items to print. 
... 
further arguments passed to print function. 
The selection of responsive items is performed using the limma
package
for microarray and continuous omics data (such as metabolomics), the DESeq2
package for
RNAseq data and the lm
function for continuous anchoring data.
Three methods are proposed (as described below). Within limma
those methods are implemented using functions lmFit
,
eBayes
and topTable
with pvalues ajusted for multiple
testing using the BenjaminiHochberg method, with the false discovery rate given in
input (argument FDR
).
Within DESeq2
those methods
are implemented using functions DESeqDataSetFromMatrix
,
DESeq
and results
with pvalues ajusted for multiple
testing using the BenjaminiHochberg method, with the false discovery rate given in
input (argument FDR
).
For continuous anchoring data, the lm
and anova
functions are used
to fit the model and compare it to the null model, and the pvalues are then corrected using
the function p.adjust
with the BenjaminiHochberg method.
The ANOVA_based test ("ANOVA"
) is classically used for
selection of omics data in the general case but it requires many replicates per dose
to be efficient, and is thus not really suited for a doseresponse design.
The linear trend test ("linear"
) aims at detecting monotonic trends from doseresponse designs,
whatever the number of replicates per dose.
As proposed by Tukey (1985), it tests the global significance of
a linear model describing the response as a function of the dose in rankscale.
The quadratic trend test ("quadratic"
)
tests the global significance of a quadratic model describing the response as a function of the dose in rankscale.
It is a variant of the linear trend method that aims at detecting monotonic and non monotonic trends from a doseresponse designs, whatever the number of replicates per dose (default chosen method).
After the use of one this previously described tests,
a filter based on the proportion of tied values is also performed whatever the type of data, assuming
tied values correspond to a minimal common value at which non detections were imputed.
All items having a proportion of such tied minimal values above the input argument
max.ties.prop
are eliminated from the selection.
itemselect
returns an object of class "itemselect"
, a list with 5 components:
adjpvalue 
the vector of the pvalues adjusted by the BenjaminiHochberg method for selected items (adjpvalue inferior to FDR) sorted in ascending order 
selectindex 
the corresponding vector of row indices of selected items in the object omicdata 
omicdata 
The corresponding object of class 
select.method 
The selection method given in input. 
FDR 
The threshold in term of FDR given in input. 
The print of a "itemselect"
object gives the number of selected items and the
identifiers of the 20 most responsive items.
MarieLaure DelignetteMuller
Tukey JW, Ciminera JL and Heyse JF (1985), Testing the statistical certainty of a response to increasing doses of a drug. Biometrics, 295301.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, and Smyth, GK (2015), limma powers differential expression analyses for RNAsequencing and microarray studies. Nucleic Acids Research 43, e47.
Love MI, Huber W, and Anders S (2014), Moderated estimation of fold change and dispersion for RNAseq data with DESeq2. Genome biology, 15(12), 550.
See lmFit
, eBayes
and topTable
for details about the used functions of the limma
package and
DESeqDataSetFromMatrix
,
DESeq
and results
for details about the used functions of the DESeq2
package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  # (1) an example on a microarray data set (a subsample of a greater data set)
#
datafilename < system.file("extdata", "transcripto_sample.txt", package="DRomics")
(o < microarraydata(datafilename, check = TRUE, norm.method = "cyclicloess"))
# 1.a using the quadratic trend test
#
(s_quad < itemselect(o, select.method = "quadratic", FDR = 0.05))
print(s_quad, nfirstitems = 30)
# to get the names of all the selected items
(selecteditems < s_quad$omicdata$item[s_quad$selectindex])
# 1.b using the linear trend test
#
(s_lin < itemselect(o, select.method = "linear", FDR = 0.05))
# 1.c using the ANOVAbased test
#
(s_ANOVA < itemselect(o, select.method = "ANOVA", FDR = 0.05))
# 1.d using the quadratic trend test with a smaller false discovery rate
#
(s_quad.2 < itemselect(o, select.method = "quadratic", FDR = 0.001))

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