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S-VAR specifications

Information


Unique identifier OMICS_25384
Name S-VAR
Interface Web user interface
Restrictions to use None
Input data An amino acid sequence.
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes

S-VAR citations

 (12)
library_books

Variable selection models for genomic selection using whole genome sequence data and singular value decomposition

2017
PMCID: 5745964
PMID: 29281962
DOI: 10.1186/s12711-017-0369-3

[…] ese components. At this point, it is possible to omit some of the components with small singular values in S, which reflect noise on the estimates of X. The variance of the effects of the components is:Var(s)=Var(V′b)=V′Vσb2=Iσb2.When applied to s, Henderson’s mixed model equations (MME) [] become:1N00S2+Iλbμ^s^=1′ySU′y,where S2=SU′US (since U′U=I), X′1=0 (a vector of zeros), since the genotypes a […]

call_split

MicroRNA expression patterns in canine mammary cancer show significant differences between metastatic and non metastatic tumours

2017
BMC Cancer
PMCID: 5678797
PMID: 29115935
DOI: 10.1186/s12885-017-3751-1
call_split See protocol

[…] formed using BRB-ArrayTools, Version 4.3.2 (developed by Dr. Richard Simon and BRB-Array Tools Development Team). For the unsupervised analysis, the variances of microRNAs were calculated using Excel’s VAR.S function. For the supervised analysis, Significance Analysis of Microarrays (SAM) test, which sets estimate of False Discovery Rate for multiple testing, was applied. Results of the analyses a […]

call_split

The impact of rate heterogeneity on inference of phylogenetic models of trait evolution

2016
PMCID: 5217074
PMID: 27653965
DOI: 10.1111/jeb.12979
call_split See protocol

[…] which assesses model ability to account for temporal variation (positive slopes show rate overestimations late in the phylogeny and underestimations early on), (v) against the variances of contrasts (S.VAR), signalling if models account for variation related to branch lengths (positive slopes show rate underestimation on long branches and overestimation on short ones), and (vi) against the weighte […]

library_books

On the Apportionment of Population Structure

2016
PLoS One
PMCID: 4978449
PMID: 27505172
DOI: 10.1371/journal.pone.0160413

[…] is a discrete random variable taking the values of μW = E[W], μY = E[Y], μZ = E[Z] with corresponding probabilities α, β, γ respectively, and where C is some constant. Hence at the limit n→∞ we have, S=Var(UXYZ)=α(μW−μ)2+β(μY−μ)2+γ(μZ−μ)2(8) μ=αμW+βμY+γμZ and S = 0 if and only if the three means are equal, i.e., μW = μY = μZ.Now consider three sequences of random variables Wi, Yi, Zi, i:1…n, inste […]

library_books

Linear Increments with Non‐monotone Missing Data and Measurement Error

2016
PMCID: 5111617
PMID: 27867251
DOI: 10.1111/sjos.12225

[…] We assume throughout Sections  and  that there is no measurement error, that is, e t=0. Let μ=E{(Y1⊤,…,YT⊤,X⊤)⊤} and Σ=Var{(Y1⊤,…,YT⊤,X⊤)⊤}. Let μ t denotes the subvector of μ corresponding to Y t (t = 1,…,T + 1), where Y T + 1 means X. Similarly, let Σ s,t denotes the submatrix of Σ corresponding to (Y s,Y t) (1≤s, […]

library_books

Fluctuations, Correlations and the Estimation of Concentrations inside Cells

2016
PLoS One
PMCID: 4786111
PMID: 26962863
DOI: 10.1371/journal.pone.0151132

[…] not fit within any of the two limits, knowing the behavior of the ACFs for both of them gives an indication of what may happen in between []. In these limits the ACFs are approximately of the form: G(s)=var(N(s))∑iWi(s)Φi(s)(τ)(32) with Φi(s)(0)=1, ∑iWi(s)=1 and Φi(s)(τ) characterized by a single correlation time, τi. Thus, the asymptotic correlation time, , studied in [, , ], is a weighted averag […]

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