run_admixture() renamed its outputs to a doubly-nested,
non-existent path when path was relative (it
setwd()s into path, then built the rename
target with file.path(path, ...) again). The rename failed
silently, so successive same-K runs overwrote each other’s
.Q/.P. path is now resolved to an
absolute path and the rename is verified (warns on failure).as_snpmatrix() used a shifted byte encoding, so
genotype 0 was stored as missing, missing as
BB, and heterozygotes/alt-homozygotes were off by one. Any
snpStats statistic (call rate, MAF, HWE, GRM/PCA) computed on its output
was therefore wrong. It now uses the canonical SnpMatrix
encoding (0x00 = missing,
0/1/2 -> 0x01/0x02/0x03), matching
getGeno().check.snp.same.position() was defined twice; the
shadowing copy lacked guards. It is now a single vectorized definition
that groups SNPs by chromosome + position, safe for single-SNP
chromosomes and missing positions.check.identical.samples(), pairs2sets()
and check.mendelian.inconsistencies() no longer error on
empty or single-element inputs (1:n / 2:n
off-by-one guards). check.identical.samples() also extracts
pairs vectorially instead of growing a data.frame in a nested loop.check.snp.no.position() now treats a SNP as unmapped
when its position is missing (NA), blank, non-numeric, or
zero (previously only == 0, which also returned spurious
NA names when positions were missing).
qcSNPs() uses the same check consistently before the
same-position filter.check.identical.samples.by.block() now returns the
pairs identical across every block (data.frame), instead of
only the last block’s pairs, and is guarded against fewer than two
samples.get.correl.fc() (result unchanged).qcSNPs() drops the missing_ind and
missing_snp arguments, which were never implemented. Use
min_snp_cr for per-SNP call rate and
qcSamples(smp_cr = ...) for per-individual call rate.combineSNPData() now assembles the combined genotype
matrix through rbindSnpFlexible() instead of a duplicated
inline implementation. The returned object is byte-identical to before
(covered by a new equivalence test suite); only the intermediate “Adding
N missing SNPs” message is no longer emitted.rbindSnpFlexible(), rbind_SnpMatrix() and
cbind_SnpMatrix() are now internal helpers (no longer
exported). Use combineSNPData() for the supported,
high-level combining workflow.testthat suite, including a broad
characterization battery that checks combineSNPData()
against a reference copy of the pre-refactor implementation across
overlapping/partial/disjoint/multi-object/randomized inputs and edge
cases.runPCA() runs the genotype PCA on
a SNPDataLong object without any clustering, returning a
prcomp-like object and the selected top principal
components. It uses the same PCA engine as
runAnticlusteringPCA() (standardised SNPs, Gram-matrix
eigendecomposition, optional matrix-free RSpectra fast
path), so scores are directly comparable.
runAnticlusteringPCA() now calls runPCA()
internally.doPCA() is deprecated in favour of
runPCA(). It still works but emits a deprecation message.
doPCA() used an unscaled GRM (snpStats::xxt)
on a raw SnpMatrix; runPCA() is the
recommended, standardised PCA on a SNPDataLong.runAnticlusteringPCA() is now dramatically faster
and lighter on memory for wide genotype data. When there are more SNPs
than individuals (the usual case), PCA is computed from the small n x n
Gram matrix instead of a full SVD on the n x p matrix, avoiding the
construction of the huge p x n rotation matrix (which could be several
GB and was never used). The genotype matrix is also no longer converted
to a data.frame before PCA. Scores and standard deviations
are unchanged. runAnticlusteringPCA() no longer uses
anticlust’s removed features
argument.
runAnticlusteringPCA(): when a fixed number of PCs
is requested and the optional RSpectra package is
installed, only the top n_pcs components are computed with
a matrix-free solver, so the n x n Gram matrix is never formed at all.
This is the dominant remaining cost for very wide data. Falls back to
the Gram-matrix eigen decomposition when
RSpectra is absent or a proportion of variance is requested
(n_pcs < 1).
runAnticlusteringPCA() gains an
anticlust_method argument. The default
"exchange" preserves current behaviour; "fast"
uses anticlust::fast_anticlustering, which scales to large
numbers of individuals.
getGeno(): the chromosome column of the returned map
is now always named Chromosome (previously it kept the
original header name, which could be Chr or
Chromosome). This fixes a
match.names ... names do not match error in
combineSNPData() / import_geno_list() when
combining datasets whose SNP_Map.txt files used different
chromosome column names.
getGeno() now builds the SnpMatrix
directly from the data.table::fread output instead of
calling snpStats::read.snps.long. The latter’s internal
search does not scale to very large long-format
FinalReport.txt files (millions of lines / hundreds of
samples): it silently read only the first sample and reported all
remaining rows as “not found”, producing a matrix in which every sample
but one was empty. Building the matrix from fread is
reliable regardless of file size and is also robust to malformed lines –
empty or unreadable confidence (GC Score) fields simply parse to
NA and are treated as no calls, so no line repair or
temporary file is required and the original file on disk is never
modified.
combineSNPData(): fixed spurious
"object has no names" warning from snpStats
when filling missing SNPs with NA. The SnpMatrix block is
now constructed with dimnames set at creation
time.
SNPDataLong: relaxed validation of the
xref_path slot to accept character vectors of any length
(one entry per individual), resolving an error in
import_geno_list() when datasets contained more than one
individual.
qcSNPs(): fixed a warning (max
returning -Inf) and potential incorrect removal of all SNPs
in a same-position group when all MAF values were NA. The
first SNP in the group is now kept in that case.