bed_projectPCA {bigsnpr} | R Documentation |
Projecting PCA
Description
Computing and projecting PCA of reference dataset to a target dataset.
Usage
bed_projectPCA(
obj.bed.ref,
obj.bed.new,
k = 10,
ind.row.new = rows_along(obj.bed.new),
ind.row.ref = rows_along(obj.bed.ref),
ind.col.ref = cols_along(obj.bed.ref),
strand_flip = TRUE,
join_by_pos = TRUE,
match.min.prop = 0.5,
build.new = "hg19",
build.ref = "hg19",
liftOver = NULL,
...,
verbose = TRUE,
ncores = 1
)
Arguments
obj.bed.ref |
Object of type bed, which is the mapping of the bed file of
the reference data. Use |
obj.bed.new |
Object of type bed, which is the mapping of the bed file of
the target data. Use |
k |
Number of principal components to compute and project. |
ind.row.new |
Rows to be used in the target data. Default uses them all. |
ind.row.ref |
Rows to be used in the reference data. Default uses them all. |
ind.col.ref |
Columns to be potentially used in the reference data. Default uses all the ones in common with target data. |
strand_flip |
Whether to try to flip strand? (default is |
join_by_pos |
Whether to join by chromosome and position (default), or instead by rsid. |
match.min.prop |
Minimum proportion of variants in the smallest data
to be matched, otherwise stops with an error. Default is |
build.new |
Genome build of the target data. Default is |
build.ref |
Genome build of the reference data. Default is |
liftOver |
Path to liftOver executable. Binaries can be downloaded at https://hgdownload.cse.ucsc.edu/admin/exe/macOSX.x86_64/liftOver for Mac and at https://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/liftOver for Linux. |
... |
Arguments passed on to
|
verbose |
Output some information on the iterations? Default is |
ncores |
Number of cores used. Default doesn't use parallelism. You may use nb_cores. |
Value
A list of 3 elements:
-
$obj.svd.ref
: big_SVD object computed from reference data. -
$simple_proj
: simple projection of new data into space of reference PCA. -
$OADP_proj
: Online Augmentation, Decomposition, and Procrustes (OADP) projection of new data into space of reference PCA.