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Molecular Diagnosis (MD) of Acute Lymphoblastic Leukemia (ALL): An integrative ALL classification system based on RNA-seq.

R 99.14% Perl 0.86%
acute-lymphoblastic-leukemia classification machine-learning visualization

md-all's Issues

Multiple Samples Mode fails with given test samples

Hi @ZunsongHu,
I was testing the MD-ALL shiny app using the multiple sample mode combining the MEF2D and PAX5 alt sample given. It failed.
Running the samples in the single sample mode works fine.
Below please find the info from R-studio and the sessionInfo().
Thanks in advance for your help!
Best, Dagmar

> run_shiny_MDALL()

Listening on http://127.0.0.1:6930
Rows: 2 Columns: 5── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): id, count, VCF, fusioncatcher, cicero
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Rows: 2 Columns: 5── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): id, count, VCF, fusioncatcher, cicero
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Rows: 2 Columns: 5── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: "\t"
chr (5): id, count, VCF, fusioncatcher, cicero
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Joining with `by = join_by(feature)`Joining with `by = join_by(feature)`Running vst
Done vst

All features found, no need to do imputation.
Joining with `by = join_by(feature)`Run Phenograph: Used Feature N= 1000 ; Used Sample N= 1822 ; Neighbor_k= 10 
Run Rphenograph starts:
  -Input data of 1822 rows and 1000 columns
  -k is set to 10
  Finding nearest neighbors...DONE ~ 2.541 s
  Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.063 s
  Build undirected graph from the weighted links...DONE ~ 0.08 s
  Run louvain clustering on the graph ...DONE ~ 0.045 s
Run Rphenograph DONE, totally takes 2.72899999999936s.
  Return a community class
  -Modularity value: 0.9249285 
  -Number of clusters: 27Joining with `by = join_by(obs)`Joining with `by = join_by(cluster)`Warning: Setting row names on a tibble is deprecated.Run Phenograph: Used Feature N= 1058 ; Used Sample N= 1822 ; Neighbor_k= 10 
Run Rphenograph starts:
  -Input data of 1822 rows and 1058 columns
  -k is set to 10
  Finding nearest neighbors...DONE ~ 2.63 s
  Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.064 s
  Build undirected graph from the weighted links...DONE ~ 0.082 s
  Run louvain clustering on the graph ...DONE ~ 0.042 s
Run Rphenograph DONE, totally takes 2.81799999999566s.
  Return a community class
  -Modularity value: 0.92436 
  -Number of clusters: 27Joining with `by = join_by(obs)`Joining with `by = join_by(cluster)`Warning: Setting row names on a tibble is deprecated.Joining with `by = join_by(ENSG)`[1] "Normalization for sample: TestSample completed"
[1] "Preparing file with snv information for: TestSample"
Reading in vcf file..
Extracting depth..
Extracting reference allele and alternative allele depths..
Needed information from vcf extracted
Finished reading vcf
[1] "Estimating chromosome arm CNV: TestSample"

All features found, no need to do imputation.
Joining with `by = join_by(feature)`Run Phenograph: Used Feature N= 1000 ; Used Sample N= 1822 ; Neighbor_k= 10 
Run Rphenograph starts:
  -Input data of 1822 rows and 1000 columns
  -k is set to 10
  Finding nearest neighbors...DONE ~ 2.566 s
  Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.064 s
  Build undirected graph from the weighted links...DONE ~ 0.081 s
  Run louvain clustering on the graph ...DONE ~ 0.046 s
Run Rphenograph DONE, totally takes 2.75700000000143s.
  Return a community class
  -Modularity value: 0.9249751 
  -Number of clusters: 27Joining with `by = join_by(obs)`Joining with `by = join_by(cluster)`Warning: Setting row names on a tibble is deprecated.Run Phenograph: Used Feature N= 1058 ; Used Sample N= 1822 ; Neighbor_k= 10 
Run Rphenograph starts:
  -Input data of 1822 rows and 1058 columns
  -k is set to 10
  Finding nearest neighbors...DONE ~ 2.634 s
  Compute jaccard coefficient between nearest-neighbor sets...DONE ~ 0.064 s
  Build undirected graph from the weighted links...DONE ~ 0.079 s
  Run louvain clustering on the graph ...DONE ~ 0.042 s
Run Rphenograph DONE, totally takes 2.81899999999951s.
  Return a community class
  -Modularity value: 0.923829 
  -Number of clusters: 26Joining with `by = join_by(obs)`Joining with `by = join_by(cluster)`Warning: Setting row names on a tibble is deprecated.Joining with `by = join_by(ENSG)`[1] "Normalization for sample: TestSample completed"
[1] "Preparing file with snv information for: TestSample"
Reading in vcf file..
Extracting depth..
Extracting reference allele and alternative allele depths..
Needed information from vcf extracted
Finished reading vcf
[1] "Estimating chromosome arm CNV: TestSample"
Joining with `by = join_by(feature)`Reading in vcf file..
Extracting depth..
Extracting reference allele and alternative allele depths..
Needed information from vcf extracted
Finished reading vcf
Joining with `by = join_by(mutation)`Joining with `by = join_by(fusion_ordered)`Joining with `by = join_by(fusion_ordered)`Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>
Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>
Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>
Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>
Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>
Warning: Error in if: argument is of length zero  3: runApp
  2: print.shiny.appobj
  1: <Anonymous>

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

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

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tibble_3.2.1                readr_2.1.4                 shinyFeedback_0.4.0         shinydashboard_0.7.2        shinyjs_2.1.0               shiny_1.8.0                
 [7] umap_0.2.10.0               cowplot_1.1.2               ggrepel_0.9.4               SingleR_2.0.0               Seurat_5.0.1                SeuratObject_5.0.1         
[13] sp_2.1-2                    Rphenograph_0.99.1          igraph_1.6.0                ggplot2_3.4.4               DESeq2_1.38.3               SummarizedExperiment_1.28.0
[19] Biobase_2.58.0              MatrixGenerics_1.10.0       matrixStats_1.2.0           GenomicRanges_1.50.2        GenomeInfoDb_1.34.9         IRanges_2.32.0             
[25] S4Vectors_0.36.2            BiocGenerics_0.44.0         stringr_1.5.1               dplyr_1.1.4                 MDALL_2.0                  

loaded via a namespace (and not attached):
  [1] scattermore_1.2           ModelMetrics_1.2.2.2      R.methodsS3_1.8.2         ragg_1.2.7                tidyr_1.3.0.9000          bit64_4.0.5              
  [7] knitr_1.45                R.utils_2.12.3            irlba_2.3.5.1             DelayedArray_0.24.0       data.table_1.14.10        rpart_4.1.19             
 [13] KEGGREST_1.38.0           hardhat_1.3.0             RCurl_1.98-1.13           generics_0.1.3            ScaledMatrix_1.6.0        callr_3.7.3              
 [19] usethis_2.2.2             RSQLite_2.3.4             RANN_2.6.1                future_1.33.0             bit_4.0.5                 tzdb_0.4.0               
 [25] spatstat.data_3.0-3       lubridate_1.9.3           httpuv_1.6.13             fontawesome_0.5.2         gower_1.0.1               xfun_0.41                
 [31] hms_1.1.3                 jquerylib_0.1.4           LiblineaR_2.10-23         evaluate_0.23             promises_1.2.1            fansi_1.0.6              
 [37] DBI_1.1.3                 geneplotter_1.76.0        htmlwidgets_1.6.4         spatstat.geom_3.2-7       purrr_1.0.2               ellipsis_0.3.2           
 [43] RSpectra_0.16-1           annotate_1.76.0           deldir_2.0-2              sparseMatrixStats_1.10.0  vctrs_0.6.5               remotes_2.4.2.1          
 [49] ROCR_1.0-11               abind_1.4-5               caret_6.0-94              cachem_1.0.8              withr_2.5.2               progressr_0.14.0         
 [55] vroom_1.6.5               sctransform_0.4.1         goftest_1.2-3             cluster_2.1.4             dotCall64_1.1-1           lazyeval_0.2.2           
 [61] crayon_1.5.2              spatstat.explore_3.2-5    labeling_0.4.3            recipes_1.0.9             pkgconfig_2.0.3           nlme_3.1-160             
 [67] pkgload_1.3.3             nnet_7.3-18               devtools_2.4.5            rlang_1.1.2               globals_0.16.2            lifecycle_1.0.4          
 [73] miniUI_0.1.1.1            fastDummies_1.7.3         rsvd_1.0.5                randomForest_4.7-1.1      polyclip_1.10-6           RcppHNSW_0.5.0           
 [79] lmtest_0.9-40             Matrix_1.6-4              zoo_1.8-12                ggridges_0.5.5            processx_3.8.3            png_0.1-8                
 [85] viridisLite_0.4.2         bitops_1.0-7              R.oo_1.25.0               pROC_1.18.5               KernSmooth_2.23-20        spam_2.10-0              
 [91] Biostrings_2.66.0         blob_1.2.4                DelayedMatrixStats_1.20.0 parallelly_1.36.0         spatstat.random_3.2-2     beachmat_2.14.2          
 [97] scales_1.3.0              memoise_2.0.1             magrittr_2.0.3            plyr_1.8.9                ica_1.0-3                 zlibbioc_1.44.0          
[103] compiler_4.2.2            Allspice_1.0.7            RColorBrewer_1.1-3        fitdistrplus_1.1-11       cli_3.6.2                 XVector_0.38.0           
[109] urlchecker_1.0.1          listenv_0.9.0             patchwork_1.1.3           pbapply_1.7-2             ps_1.7.5                  MASS_7.3-58.1            
[115] tidyselect_1.2.0          stringi_1.8.3             textshaping_0.3.7         yaml_2.3.8                BiocSingular_1.14.0       askpass_1.2.0            
[121] locfit_1.5-9.8            grid_4.2.2                sass_0.4.8                tools_4.2.2               timechange_0.2.0          future.apply_1.11.0      
[127] parallel_4.2.2            rstudioapi_0.15.0         foreach_1.5.2             gridExtra_2.3             prodlim_2023.08.28        farver_2.1.1             
[133] Rtsne_0.17                digest_0.6.33             BiocManager_1.30.22       lava_1.7.3                Rcpp_1.0.11               later_1.3.2              
[139] RcppAnnoy_0.0.21          httr_1.4.7                AnnotationDbi_1.60.2      colorspace_2.1-0          XML_3.99-0.16             fs_1.6.3                 
[145] tensor_1.5                reticulate_1.34.0         splines_4.2.2             uwot_0.1.16               spatstat.utils_3.0-4      plotly_4.10.3            
[151] sessioninfo_1.2.2         systemfonts_1.0.5         xtable_1.8-4              jsonlite_1.8.8            timeDate_4032.109         ipred_0.9-14             
[157] R6_2.5.1                  profvis_0.3.8             pillar_1.9.0              htmltools_0.5.7           mime_0.12                 glue_1.6.2               
[163] fastmap_1.1.1             BiocParallel_1.32.6       class_7.3-20              codetools_0.2-18          pkgbuild_1.4.3            utf8_1.2.4               
[169] lattice_0.20-45           bslib_0.6.1               spatstat.sparse_3.0-3     curl_5.2.0                leiden_0.4.3.1            openssl_2.1.1            
[175] survival_3.4-0            rmarkdown_2.25            desc_1.4.3                munsell_0.5.0             GenomeInfoDbData_1.2.9    iterators_1.0.14         
[181] reshape2_1.4.4            gtable_0.3.4

Cell types deconvolution info

Hi authors,
Compliments for the great tool!
Reading the paper in which you present MD-ALL I focused on the deconvolution of bulk RNAseq data using CIBERSORTx and a custom single cell reference.
I understood that you re-performed filtering on a 166K cells matrix from 1-Million Immune Cells Project; how did you obtain the final reference matrix? Did you loaded it in a repo or database or is it accessible?
I would also like to perform some tests on deconvolution and try to use different reference matrices.
Thanks a lot in advance for our help.

Alberto

R package?

Many thanks for this very useful app.

Do you have any plan to make it available as an R package to make its usage more flexible?
Alternatively, could you give easy access to the reference RNA-seq and scRNA-seq datasets as R objects (deseqdataset and singlecellexperiment)?

Thanks again!

Adrien

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