Correlation matrix
Get significant (Pearson) correlations between nearby SNPs of the same chromosome (p-values are computed using a two-sided t-test).
snp_cor( Gna, ind.row = rows_along(Gna), ind.col = cols_along(Gna), size = 500, alpha = 1, thr_r2 = 0, fill.diag = TRUE, infos.pos = NULL, ncores = 1 ) bed_cor( obj.bed, ind.row = rows_along(obj.bed), ind.col = cols_along(obj.bed), size = 500, alpha = 1, thr_r2 = 0, fill.diag = TRUE, infos.pos = NULL, ncores = 1 )
Gna |
A FBM.code256
(typically |
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. |
size |
For one SNP, window size around this SNP to compute correlations.
Default is |
alpha |
Type-I error for testing correlations.
Default is |
thr_r2 |
Threshold to apply on squared correlations. Default is |
fill.diag |
Whether to fill the diagonal with 1s (the default) or to keep it as 0s. |
infos.pos |
Vector of integers specifying the physical position
on a chromosome (in base pairs) of each SNP. |
ncores |
Number of cores used. Default doesn't use parallelism. You may use nb_cores. |
obj.bed |
Object of type bed, which is the mapping of some bed file.
Use |
The (Pearson) correlation matrix. This is a sparse symmetric matrix.
test <- snp_attachExtdata() G <- test$genotypes corr <- snp_cor(G, ind.col = 1:1000) corr[1:10, 1:10] # Sparsity length(corr@x) / length(corr)
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