lnmf

[nesilin]

Hi Zack!

Thanks for developing RcppML and for providing such clear posts about NMF. I would like to run Linked NMF in my cohort data to obtain shared factors. Before that, I was going through the example at the end of your post of Linked NMF (https://www.zachdebruine.com/post/linked-nmf-for-signal-source-separation/) and realized you updated the aml dataset. I tried the following to get a quick idea of how it works. I obtained the following error:

library(RcppML)
library(singlet)

data(aml)

# make sample names unique
aml$metadata_h$new_names <- paste(aml$metadata_h$samples, aml$metadata_h$category, sep="-")
colnames(aml$data) <- aml$metadata_h$new_names

# make list of datasets
data_examp <- list(
  aml$data[, which(grepl( "AML" , colnames(aml$data)))],
  aml$data[, which(grepl( "Control" , colnames(aml$data)))]
)

# run linked nmf
lnmf_model <- lnmf(data_examp, k_wh = 3, k_uv = c(2, 2))

Error in getClass(Class, where = topenv(parent.frame())): “lnmf” is not a defined class
Traceback:

1. lnmf(data_examp, k_wh = 3, k_uv = c(2, 2))
2. new("lnmf", w = w, u = u, v = v, h = h, d_wh = d_wh, d_uv = d_uv, 
 .     misc = model@misc)
3. getClass(Class, where = topenv(parent.frame()))
4. stop(gettextf("%s is not a defined class", dQuote(Class)), domain = NA)

These are the package versions

> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/isentis/software/anaconda3/envs/ines_r4.1.1c/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] singlet_0.99.27    dplyr_1.0.9        SeuratObject_4.0.2 Seurat_4.0.5      
[5] RcppML_0.5.6      

loaded via a namespace (and not attached):
  [1] fgsea_1.20.0          Rtsne_0.16            colorspace_2.0-3     
  [4] deldir_1.0-6          ellipsis_0.3.2        ggridges_0.5.3       
  [7] IRdisplay_1.0         base64enc_0.1-3       spatstat.data_3.0-1  
 [10] leiden_0.4.2          listenv_0.8.0         ggrepel_0.9.1        
 [13] fansi_1.0.3           codetools_0.2-18      splines_4.1.1        
 [16] knitr_1.40            polyclip_1.10-0       IRkernel_1.1         
 [19] jsonlite_1.8.0        ica_1.0-3             cluster_2.1.4        
 [22] rgeos_0.5-9           png_0.1-7             uwot_0.1.10          
 [25] shiny_1.7.2           sctransform_0.3.4     spatstat.sparse_3.0-1
 [28] msigdbr_7.5.1         compiler_4.1.1        httr_1.4.4           
 [31] assertthat_0.2.1      Matrix_1.4-1          fastmap_1.1.0        
 [34] lazyeval_0.2.2        limma_3.50.3          cli_3.3.0            
 [37] later_1.3.0           htmltools_0.5.3       tools_4.1.1          
 [40] igraph_1.3.4          gtable_0.3.0          glue_1.6.2           
 [43] RANN_2.6.1            reshape2_1.4.4        fastmatch_1.1-3      
 [46] Rcpp_1.0.9            scattermore_0.8       vctrs_0.4.1          
 [49] babelgene_22.9        nlme_3.1-159          lmtest_0.9-40        
 [52] spatstat.random_3.1-4 xfun_0.32             stringr_1.4.1        
 [55] globals_0.16.1        mime_0.12             miniUI_0.1.1.1       
 [58] lifecycle_1.0.1       irlba_2.3.5           goftest_1.2-3        
 [61] future_1.22.1         MASS_7.3-58.1         zoo_1.8-10           
 [64] scales_1.2.1          spatstat.core_2.4-4   promises_1.2.0.1     
 [67] spatstat.utils_3.0-2  parallel_4.1.1        RColorBrewer_1.1-3   
 [70] reticulate_1.25       pbapply_1.5-0         gridExtra_2.3        
 [73] ggplot2_3.3.6         rpart_4.1.16          stringi_1.7.8        
 [76] BiocParallel_1.28.3   repr_1.1.3            rlang_1.0.4          
 [79] pkgconfig_2.0.3       matrixStats_0.62.0    evaluate_0.16        
 [82] lattice_0.20-45       ROCR_1.0-11           purrr_0.3.4          
 [85] tensor_1.5            patchwork_1.1.2       htmlwidgets_1.5.4    
 [88] cowplot_1.1.1         tidyselect_1.1.2      parallelly_1.32.1    
 [91] RcppAnnoy_0.0.19      plyr_1.8.7            magrittr_2.0.3       
 [94] R6_2.5.1              generics_0.1.3        pbdZMQ_0.3-5         
 [97] DBI_1.1.3             mgcv_1.8-40           pillar_1.8.1         
[100] fitdistrplus_1.1-8    sp_1.5-0              survival_3.4-0       
[103] abind_1.4-5           tibble_3.1.8          future.apply_1.9.0   
[106] crayon_1.5.1          uuid_1.1-0            KernSmooth_2.23-20   
[109] utf8_1.2.2            spatstat.geom_3.1-0   plotly_4.10.0        
[112] grid_4.1.1            data.table_1.14.2     digest_0.6.29        
[115] xtable_1.8-4          tidyr_1.2.0           httpuv_1.6.5         
[118] munsell_0.5.0         viridisLite_0.4.1    

Thank you!

[zdebruine]

Got it, I'll try to address this issue early next week. You will see a significant change to the way we do LNMF however, as we have developed much better theoretical methods that complement the method for integration.

[KaBach]

Hey Zack,
Thanks also from my side for the great pacakge and the clear posts. I am receiving the exact same error here with my own data ("lnmf" is not a defined class) version 0.5.6. Any idea as to when you'll find time to address the issue?

Thanks!