snp_autoSVD | R Documentation |
Fast truncated SVD with initial pruning and that iteratively removes
long-range LD regions. Some variants are removing due to the initial clumping,
then more and more variants are removed at each iteration. You can access the
indices of the remaining variants with attr(*, "subset")
. If some of the
variants removed are contiguous, the regions are reported in attr(*, "lrldr")
.
snp_autoSVD(
G,
infos.chr,
infos.pos = NULL,
ind.row = rows_along(G),
ind.col = cols_along(G),
fun.scaling = snp_scaleBinom(),
thr.r2 = 0.2,
size = 100/thr.r2,
k = 10,
roll.size = 50,
int.min.size = 20,
alpha.tukey = 0.05,
min.mac = 10,
max.iter = 5,
is.size.in.bp = NULL,
ncores = 1,
verbose = TRUE
)
bed_autoSVD(
obj.bed,
ind.row = rows_along(obj.bed),
ind.col = cols_along(obj.bed),
fun.scaling = bed_scaleBinom,
thr.r2 = 0.2,
size = 100/thr.r2,
k = 10,
roll.size = 50,
int.min.size = 20,
alpha.tukey = 0.05,
min.mac = 10,
max.iter = 5,
ncores = 1,
verbose = TRUE
)
G |
A FBM.code256
(typically |
infos.chr |
Vector of integers specifying each SNP's chromosome. |
infos.pos |
Vector of integers specifying the physical position
on a chromosome (in base pairs) of each SNP. |
ind.row |
An optional vector of the row indices (individuals) that
are used. If not specified, all rows are used. |
ind.col |
An optional vector of the column indices (SNPs) that are used.
If not specified, all columns are used. |
fun.scaling |
A function with parameters
Default uses binomial scaling.
You can also provide your own |
thr.r2 |
Threshold over the squared correlation between two SNPs.
Default is |
size |
For one SNP, window size around this SNP to compute correlations.
Default is |
k |
Number of singular vectors/values to compute. Default is |
roll.size |
Radius of rolling windows to smooth log-p-values.
Default is |
int.min.size |
Minimum number of consecutive outlier SNPs
in order to be reported as long-range LD region. Default is |
alpha.tukey |
Default is |
min.mac |
Minimum minor allele count (MAC) for variants to be included.
Default is |
max.iter |
Maximum number of iterations of outlier detection.
Default is |
is.size.in.bp |
Deprecated. |
ncores |
Number of cores used. Default doesn't use parallelism. You may use nb_cores. |
verbose |
Output some information on the iterations? Default is |
obj.bed |
Object of type bed, which is the mapping of some bed file.
Use |
If you don't have any information about SNPs, you can try using
infos.chr = rep(1, ncol(G))
,
size = ncol(G)
(if SNPs are not sorted),
roll.size = 0
(if SNPs are not sorted).
A named list (an S3 class "big_SVD") of
d
, the singular values,
u
, the left singular vectors,
v
, the right singular vectors,
niter
, the number of the iteration of the algorithm,
nops
, number of Matrix-Vector multiplications used,
center
, the centering vector,
scale
, the scaling vector.
Note that to obtain the Principal Components, you must use predict on the result. See examples.
ex <- snp_attachExtdata()
G <- ex$genotypes
obj.svd <- snp_autoSVD(G,
infos.chr = ex$map$chromosome,
infos.pos = ex$map$physical.position)
str(obj.svd)
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