March 29, 2015
[LMM] literature overview: performance
March 27, 2015
[LMM] literature overview: approximate methods
March 15, 2015
[FaST-LMM] Proximal contamination
March 13, 2015
[FaST-LMM] REML estimate
March 11, 2015
[FaST-LMM] comparison with PyLMM (continued)
March 10, 2015
[FaST-LMM] comparison with PyLMM (practice)
March 9, 2015
[FaST-LMM] comparison with PyLMM (theory)
March 3, 2015
[FaST-LMM] fastlmm/inference/lmm_cov.py, part 2
February 27, 2015
[FaST-LMM] high-level overview, part 2
February 25, 2015
[FaST-LMM] high-level overview of the codebase, part 1
February 18, 2015
February 16, 2015
[FaST-LMM] fastlmm/inference/lmm_cov.py, part 1
LMM(select) + PCs
The authors claim that under some circumstances this method gives as good results as
The algorithm is described in a supplementary note. That note also contains a brief definition of Probabilistic PCA. A more comprehensible description is found in http://www.robots.ox.ac.uk/~cvrg/hilary2006/ppca.pdf.
The components are computed by
The rest is not implemented in Python yet. Instead, the C++ executable
Another available procedure is searching for pairs of correlated SNPs. This is done by module
Main function there is
The purpose of having such function (instead of having a function which runs on a pair of SNPs) is that these jobs can be distributed on a cluster and thus the granularity has to be somewhat coarse-grained.
The job first computes the matrix $X$, containing
Then, for each pair, two models are compared.
In the null model, the $ X$ matrix contains only covariates and SNP values of the pair.
The alternative model adds to that the additional column with the product of the SNP values. (If there’s no correlation between the two SNPs, it will have mean close to zero.)
For both models, log-likelihood is computed, and likelihood-ratio test is performed to obtain p-value.
Testing sets of SNPs
Not only pairs, but also sets of SNPs can be checked for correlations. This functionality is available in the module
TODO: find description of the algorithm