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Introduction

We fit EBNMF model on SLA data, but fix the intercept (of factors) as:

colMeans(data_matrix)

library(Matrix)
datax = readRDS('data/sla_2000.rds')
dim(datax$data)
[1] 3207 1676
sum(datax$data==0)/prod(dim(datax$data))
[1] 0.9718043
datax$data = Matrix(datax$data,sparse = TRUE)

Recall that the transformation for EBNMF for count data is

\[\log ( 1+ \frac{median(s_i)}{0.5}\frac{y_{ij}}{s_i}).\]

library(ebpmf)
Y_tilde = log_for_ebmf(datax$data)
f0 = colMeans(Y_tilde)
plot(f0)

Version Author Date
371c652 DongyueXie 2023-07-23

Then we subtract \(1f_0^T\) to remove this intercept.

Y = Y_tilde - rep(1,nrow(Y_tilde))%*%t(f0)

Then we fit EBNMF

library(flashier)
Loading required package: magrittr
fit = flash(Y,ebnm=ebnm::ebnm_point_exponential,greedy.Kmax = 6,var.type = 2,backfit = T)
Adding factor 1 to flash object...
Adding factor 2 to flash object...
Adding factor 3 to flash object...
Adding factor 4 to flash object...
Adding factor 5 to flash object...
Adding factor 6 to flash object...
Wrapping up...
Done.
Backfitting 6 factors (tolerance: 8.01e-02)...
  Difference between iterations is within 1.0e+02...
  Difference between iterations is within 1.0e+01...
  Difference between iterations is within 1.0e+00...
  Difference between iterations is within 1.0e-01...
Wrapping up...
Done.
Nullchecking 6 factors...
Done.
colnames(datax$data)[order(fit$F.pm[,1],decreasing = T)[1:10]]
 [1] "trial"     "clinic"    "treatment" "patient"   "random"    "outcom"   
 [7] "design"    "effect"    "arm"       "control"  
colnames(datax$data)[order(fit$F.pm[,2],decreasing = T)[1:10]]
 [1] "hazard"  "proport" "surviv"  "time"    "model"   "cox"     "covari" 
 [8] "failur"  "censor"  "baselin"
colnames(datax$data)[order(fit$F.pm[,3],decreasing = T)[1:10]]
 [1] "semiparametr" "estim"        "model"        "effici"       "nonparametr" 
 [6] "parametr"     "asymptot"     "propos"       "regress"      "paramet"     
colnames(datax$data)[order(fit$F.pm[,4],decreasing = T)[1:10]]
 [1] "markov"    "chain"     "mont"      "carlo"     "algorithm" "bayesian" 
 [7] "model"     "posterior" "prior"     "infer"    
colnames(datax$data)[order(fit$F.pm[,5],decreasing = T)[1:10]]
 [1] "absolut"  "deviat"   "clip"     "smooth"   "select"   "oracl"   
 [7] "variabl"  "penalti"  "properti" "consist" 
colnames(datax$data)[order(fit$F.pm[,6],decreasing = T)[1:10]]
 [1] "spline"      "smooth"      "regress"     "penal"       "function"   
 [6] "penalti"     "squar"       "polynomi"    "knot"        "nonparametr"

sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRblas.so
LAPACK: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

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

other attached packages:
[1] flashier_0.2.36 magrittr_2.0.3  ebpmf_2.2.2     Matrix_1.5-3   
[5] workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] mcmc_0.9-7         bitops_1.0-7       matrixStats_0.59.0
  [4] fs_1.5.0           progress_1.2.2     httr_1.4.5        
  [7] rprojroot_2.0.2    tools_4.1.0        bslib_0.4.2       
 [10] utf8_1.2.3         R6_2.5.1           irlba_2.3.5.1     
 [13] uwot_0.1.14        lazyeval_0.2.2     colorspace_2.1-0  
 [16] wavethresh_4.7.2   prettyunits_1.1.1  tidyselect_1.2.0  
 [19] ebpm_0.0.1.3       compiler_4.1.0     git2r_0.28.0      
 [22] glmnet_4.1-2       cli_3.6.1          quantreg_5.94     
 [25] SparseM_1.81       plotly_4.10.1      horseshoe_0.2.0   
 [28] sass_0.4.0         smashrgen_1.2.4    caTools_1.18.2    
 [31] scales_1.2.1       mvtnorm_1.1-2      SQUAREM_2021.1    
 [34] quadprog_1.5-8     pbapply_1.7-0      mixsqp_0.3-48     
 [37] stringr_1.5.0      digest_0.6.31      rmarkdown_2.9     
 [40] MCMCpack_1.6-3     deconvolveR_1.2-1  vebpm_0.4.8       
 [43] pkgconfig_2.0.3    htmltools_0.5.4    fastTopics_0.6-142
 [46] highr_0.9          fastmap_1.1.0      invgamma_1.1      
 [49] htmlwidgets_1.6.1  rlang_1.1.1        rstudioapi_0.13   
 [52] shape_1.4.6        jquerylib_0.1.4    generics_0.1.3    
 [55] jsonlite_1.8.4     dplyr_1.1.0        smashr_1.3-6      
 [58] Rcpp_1.0.10        munsell_0.5.0      fansi_1.0.4       
 [61] lifecycle_1.0.3    RcppZiggurat_0.1.6 stringi_1.6.2     
 [64] whisker_0.4        yaml_2.3.7         MASS_7.3-54       
 [67] Rtsne_0.16         grid_4.1.0         parallel_4.1.0    
 [70] promises_1.2.0.1   ggrepel_0.9.3      crayon_1.5.2      
 [73] lattice_0.20-44    cowplot_1.1.1      splines_4.1.0     
 [76] hms_1.1.2          knitr_1.33         pillar_1.8.1      
 [79] softImpute_1.4-1   codetools_0.2-18   glue_1.6.2        
 [82] evaluate_0.14      trust_0.1-8        data.table_1.14.8 
 [85] RcppParallel_5.1.7 foreach_1.5.1      vctrs_0.6.2       
 [88] nloptr_1.2.2.2     httpuv_1.6.1       MatrixModels_0.5-1
 [91] gtable_0.3.1       purrr_1.0.1        ebnm_1.0-11       
 [94] tidyr_1.3.0        ashr_2.2-54        cachem_1.0.5      
 [97] ggplot2_3.4.1      xfun_0.24          Rfast_2.0.7       
[100] coda_0.19-4        later_1.3.0        mr.ash_0.1-87     
[103] survival_3.2-11    viridisLite_0.4.1  truncnorm_1.0-8   
[106] tibble_3.2.1       iterators_1.0.13   ellipsis_0.3.2