Last updated: 2019-07-02

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Knit directory: FLASHvestigations/

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File Version Author Date Message
Rmd 52e6cfe Jason Willwerscheid 2019-07-02 wflow_publish(“analysis/flashier_features.Rmd”)
html 9ecf083 Jason Willwerscheid 2019-01-15 Build site.
Rmd b6e2dc2 Jason Willwerscheid 2019-01-15 workflowr::wflow_publish(“analysis/flashier_features.Rmd”)
html 7429ab8 Jason Willwerscheid 2019-01-12 Build site.
Rmd 429cae7 Jason Willwerscheid 2019-01-12 workflowr::wflow_publish(“analysis/flashier_features.Rmd”)
  • Handles sparse matrices (of class Matrix), tensors (3-dimensional arrays), and low-rank matrix representations (as returned by, for example, svd, irlba, rsvd, and softImpute).
  • Implements a full range of variance structures, including “kronecker” and “noisy.” In general, the estimated residual variance can be an arbitrary rank-one matrix or tensor.
  • For simple variance structures (including “constant” and “by row”/“by column”), no \(n \times p\) matrix is ever formed (so, for example, a matrix of residuals is never explicitly formed). This yields a large improvement in memory usage and runtime for very large data matrices. (Benchmarking results are here.)
  • Speeds up backfits by dropping factors once they are no longer improving the objective very much (so, instead of updating every factor each iteration, only factors that are still changing are updated). Other backfit options are also available. The “montaigne” option always goes after the factor that most recently produced the largest improvement. While it produces much rougher fits, it can greatly reduce the number of backfit iterations.
  • Simplifies the user interface. Everything is done via a single function with a small number of parameters, and the latter are more user-friendly. In particular, a new prior.family parameter replaces the less friendly ebnm.fn and ebnm.param.
  • In constrast, the “workhorse” function gives many more options. One that I especially like allows the user to write an arbitrary function whose output will be displayed during optimization (allowing the user to inspect the progress of optimization however they like).
  • Uses a home-grown initialization function rather than softImpute. The new function is much faster than softImpute for large matrices (see the benchmarking results).
  • Instead of sampling the full \(LF'\) matrix, the sampler now just samples \(L\) and \(F\) separately. This reduces memory usage by a factor of \(\min(n, p)\). (With large data matrices, the flashr sampler is basically useless because every sample takes up as much memory as the data matrix itself.) 

sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.2.0 Rcpp_1.0.1      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.3 git2r_0.25.2    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.4.3   fs_1.2.7        whisker_0.3-2   rmarkdown_1.12 
[13] tools_3.5.3     stringr_1.4.0   glue_1.3.1      xfun_0.6       
[17] yaml_2.2.0      compiler_3.5.3  htmltools_0.3.6 knitr_1.22