Description Usage Arguments Details Value References See Also Examples
View source: R/user_functions.R
This function tests for associations between gene expression
or exon abundance (Y
)
and genetic or epigenetic alterations (X
).
Using the locations of genes (Yloc
),
and the locations of genetic
or epigenetic alterations (Xloc
),
the expression of each gene is tested for associations with
alterations on the same chromosome that are closer to the gene
than a given distance (window
).
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Y |
RNA-Seq data:
numeric matrix with |
Yloc |
location RNA-Seq:
numeric vector of length |
X |
genomic profile:
numeric matrix with |
Xloc |
location covariates:
numeric vector of length |
window |
maximum distance: non-negative real number |
Ychr |
chromosome RNA-Seq:
factor of length |
Xchr |
chromosome covariates:
factor of length |
offset |
numeric vector of length |
group |
confounding variable:
factor of length |
perm |
number of iterations: positive integer |
nodes |
number of cluster nodes for parallel computation |
phi |
dispersion parameters: vector of length |
kind |
computation : number between 0 and 1 |
Note that Yloc
, Xloc
and window
must
be given in the same unit, usually in base pairs.
If Yloc
indicates interval locations,
and window
is zero,
then only covariates between the start and end location
of the gene are of interest.
Typically window
is larger than one million base pairs.
If Y
and X
include data from a single chromosome,
Ychr
and Xchr
are redundant.
If Y
or X
include data
from multiple chromosomes,
Ychr
and Xchr
should be specified
in order to prevent confusion between chromosomes.
For the simultaneous analysis of
multiple genomic profiles
X
should be a list of numeric matrices with
n
columns (samples),
Xloc
a list of numeric vectors,
and window
a list of non-negative real numbers.
If provided, Xchr
should be alist of of numeric vectors.
The offset
is meant to account for
different libary sizes.
By default the offset
is calculated based on Y
.
Different library sizes can be ignored by
setting the offset
to rep(1,n)
.
The user can provide the confounding variable group
.
Note that each level of group
must appear at least twice
in order to allow stratified permutations.
Efficient alternatives to classical permutation (kind=1
)
are the method of control variates (kind=0
)
and permutation in chunks (0 < kind
< 1)
details.
The function returns a dataframe, with the p-values in the first row and the test statistics in the second row.
A Rauschenberger, MA Jonker, MA van de Wiel, and RX Menezes (2016). "Testing for association between RNA-Seq and high-dimensional data", BMC Bioinformatics. 17:118. html pdf (open access)
RX Menezes, M Boetzer, M Sieswerda, GJB van Ommen, and JM Boer (2009). "Integrated analysis of DNA copy number and gene expression microarray data using gene sets", BMC Bioinformatics. 10:203. html pdf (open access)
The function omnibus
tests for associations
between an overdispersed response variable
and a high-dimensional covariate set.
The function proprius
calculates the contributions
of individual samples or covariates to the test statistic.
All other function of the R package
globalSeq
are internal
.
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