Last updated: 2023-09-12

Checks: 7 0

Knit directory: gsmash/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220606)

The command set.seed(20220606) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 224d670

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 224d670. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/GO_ORA_montoro.Rmd
    Untracked:  analysis/GO_ORA_pbmc_purified.Rmd
    Untracked:  analysis/fit_ebpmf_sla_2000.Rmd
    Untracked:  analysis/multiplicative_additive.Rmd
    Untracked:  analysis/poisson_deviance.Rmd
    Untracked:  analysis/sla_data.Rmd
    Untracked:  chipexo_rep1_reverse.rds
    Untracked:  data/Citation.RData
    Untracked:  data/SLA/
    Untracked:  data/abstract.txt
    Untracked:  data/abstract.vocab.txt
    Untracked:  data/ap.txt
    Untracked:  data/ap.vocab.txt
    Untracked:  data/sla_2000.rds
    Untracked:  data/sla_full.rds
    Untracked:  data/text.R
    Untracked:  data/tpm3.rds
    Untracked:  output/driving_gene_pbmc.rds
    Untracked:  output/pbmc_gsea.rds
    Untracked:  output/plots/
    Untracked:  output/tpm3_fit_fasttopics.rds
    Untracked:  output/tpm3_fit_stm.rds
    Untracked:  output/tpm3_fit_stm_slow.rds
    Untracked:  sla.rds

Unstaged changes:
    Modified:   analysis/PMF_splitting.Rmd
    Modified:   analysis/fit_ebpmf_sla.Rmd
    Modified:   analysis/index.Rmd
    Modified:   code/poisson_STM/structure_plot.R
    Modified:   code/poisson_mean/pois_log_normal_mle.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/smallman_example.Rmd) and HTML (docs/smallman_example.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 224d670 DongyueXie 2023-09-12 wflow_publish("analysis/smallman_example.Rmd")

Summary

I don’t quite get the point of this example. There is no “true” L and F. Not sure what to tell.

Introduction

Try examples studied in Luke Smallman’s thesis.

sim_data1 = function(n,seed=12345){
  set.seed(seed)
  v1 = rpois(n,25)
  v2 = rpois(n,30)
  v3 = v1 + 3*v2
  lambda = cbind(v1,v1,v1,v1,v2,v2,v2,v2,v3,v3)
  Y = matrix(rpois(n*10,lambda),nrow=n)
  return(list(lambda=lambda,Y=Y))
}
datax=  sim_data1(100)

NMF

res = NNLM::nnmf(datax$Y,k=2,loss = 'mkl')
plot(res$W[,1])

plot(res$W[,2])

plot(res$H[1,])

plot(res$H[2,])

topic model

res = fastTopics::fit_topic_model(datax$Y,2)
Initializing factors using Topic SCORE algorithm.
Initializing loadings by running 10 SCD updates.
Fitting rank-2 Poisson NMF to 100 x 10 dense matrix.
Running 100 EM updates, without extrapolation (fastTopics 0.6-142).
Refining model fit.
Fitting rank-2 Poisson NMF to 100 x 10 dense matrix.
Running 100 SCD updates, with extrapolation (fastTopics 0.6-142).
plot(res$L[,1])

plot(res$L[,2])

plot(res$F[,1])

plot(res$F[,2])

GLMPCA

res = glmpca::glmpca(datax$Y,2,'poi',sz=1)
plot(res$loadings[,1])

plot(res$loadings[,2])

plot(res$factors[,1])

plot(res$factors[,2])

log + ebmf

res = flashier::flash(log(datax$Y),var_type = 2,greedy_Kmax = 2,backfit = T)
Adding factor 1 to flash object...
Adding factor 2 to flash object...
Wrapping up...
Done.
Backfitting 2 factors (tolerance: 1.49e-05)...
  Difference between iterations is within 1.0e+00...
  Difference between iterations is within 1.0e-01...
  Difference between iterations is within 1.0e-02...
  Difference between iterations is within 1.0e-03...
  Difference between iterations is within 1.0e-04...
Wrapping up...
Done.
Nullchecking 2 factors...
Done.
plot(res$L_pm[,1])

plot(res$L_pm[,2])

plot(res$F_pm[,1])

plot(res$F_pm[,2])

log + ebnmf

res = flashier::flash(log(datax$Y),var_type = 1,greedy_Kmax = 2,backfit = T,ebnm_fn = ebnm::ebnm_point_exponential)
Adding factor 1 to flash object...
Adding factor 2 to flash object...
Wrapping up...
Done.
Backfitting 2 factors (tolerance: 1.49e-05)...
  Difference between iterations is within 1.0e-04...
  Difference between iterations is within 1.0e-05...
Wrapping up...
Done.
Nullchecking 2 factors...
Done.
plot(res$L_pm[,1])

plot(res$F_pm[,1])

plot(res$F_pm[,2])

log + nmf

res = NNLM::nnmf(log(datax$Y),k=2,loss = 'mse')
plot(res$W[,1])

plot(res$W[,2])

plot(res$H[1,])

plot(res$H[2,])

svd on lambda

res = svd(datax$lambda)
plot(res$d)

plot(res$v[,1])

plot(res$v[,2])

svd on log lambda

res = svd(log(datax$lambda))
plot(res$d)

plot(res$v[,1])

plot(res$v[,2])

plot(res$v[,3])


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] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] mcmc_0.9-7         fs_1.5.0           progress_1.2.2     httr_1.4.5        
 [5] rprojroot_2.0.2    tools_4.1.0        bslib_0.4.2        utf8_1.2.3        
 [9] R6_2.5.1           irlba_2.3.5.1      uwot_0.1.14        lazyeval_0.2.2    
[13] colorspace_2.1-0   tidyselect_1.2.0   prettyunits_1.1.1  compiler_4.1.0    
[17] git2r_0.28.0       cli_3.6.1          quantreg_5.94      SparseM_1.81      
[21] plotly_4.10.1      horseshoe_0.2.0    sass_0.4.0         flashier_0.2.51   
[25] scales_1.2.1       SQUAREM_2021.1     quadprog_1.5-8     pbapply_1.7-0     
[29] mixsqp_0.3-48      stringr_1.5.0      digest_0.6.31      rmarkdown_2.9     
[33] MCMCpack_1.6-3     deconvolveR_1.2-1  pkgconfig_2.0.3    htmltools_0.5.4   
[37] fastTopics_0.6-142 fastmap_1.1.0      invgamma_1.1       highr_0.9         
[41] htmlwidgets_1.6.1  rlang_1.1.1        rstudioapi_0.13    jquerylib_0.1.4   
[45] generics_0.1.3     jsonlite_1.8.4     dplyr_1.1.0        magrittr_2.0.3    
[49] Matrix_1.5-3       Rcpp_1.0.10        munsell_0.5.0      fansi_1.0.4       
[53] lifecycle_1.0.3    stringi_1.6.2      whisker_0.4        yaml_2.3.7        
[57] MASS_7.3-54        Rtsne_0.16         grid_4.1.0         parallel_4.1.0    
[61] promises_1.2.0.1   ggrepel_0.9.3      crayon_1.5.2       lattice_0.20-44   
[65] cowplot_1.1.1      splines_4.1.0      hms_1.1.2          knitr_1.33        
[69] pillar_1.8.1       softImpute_1.4-1   glue_1.6.2         evaluate_0.14     
[73] trust_0.1-8        data.table_1.14.8  RcppParallel_5.1.7 vctrs_0.6.2       
[77] httpuv_1.6.1       MatrixModels_0.5-1 gtable_0.3.1       purrr_1.0.1       
[81] ebnm_1.0-54        tidyr_1.3.0        ashr_2.2-54        cachem_1.0.5      
[85] ggplot2_3.4.1      xfun_0.24          NNLM_0.4.4         coda_0.19-4       
[89] later_1.3.0        survival_3.2-11    viridisLite_0.4.1  truncnorm_1.0-8   
[93] tibble_3.2.1       glmpca_0.2.0       ellipsis_0.3.2