Supervised categorical principal component analysis for genome-wide association analyses.
about
GBS-SNP-CROP: a reference-optional pipeline for SNP discovery and plant germplasm characterization using variable length, paired-end genotyping-by-sequencing data.MICADo - Looking for Mutations in Targeted PacBio Cancer Data: An Alignment-Free MethodPractical low-coverage genomewide sequencing of hundreds of individually barcoded samples for population and evolutionary genomics in nonmodel species.Using 2k + 2 bubble searches to find single nucleotide polymorphisms in k-mer graphs.Molecular genetic diversity and population structure of Ethiopian white lupin landraces: Implications for breeding and conservation.Challenges imposed by minor reference alleles on the identification and reporting of clinical variants from exome data.
P2860
Supervised categorical principal component analysis for genome-wide association analyses.
description
2014 nî lūn-bûn
@nan
2014 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Supervised categorical principal component analysis for genome-wide association analyses.
@ast
Supervised categorical principal component analysis for genome-wide association analyses.
@en
type
label
Supervised categorical principal component analysis for genome-wide association analyses.
@ast
Supervised categorical principal component analysis for genome-wide association analyses.
@en
prefLabel
Supervised categorical principal component analysis for genome-wide association analyses.
@ast
Supervised categorical principal component analysis for genome-wide association analyses.
@en
P2093
P2860
P1433
P1476
Supervised categorical principal component analysis for genome-wide association analyses.
@en
P2093
David Hadley
Hye-Seung Lee
Jianhua Z Huang
Xiaoning Qian
P2860
P2888
P356
10.1186/1471-2164-15-S1-S10
P407
P478
15 Suppl 1
P577
2014-01-24T00:00:00Z
P6179
1047787153