bootstrapNullAlternativeModel() returns error
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Tobias ▴ 30
@tobias-24288
Last seen 7 weeks ago
Switzerland

Hi TPP2D package maintainers,

I am trying to analyse data from a 2D-TPP experiment. Unfortunately, the following step returns an error:

> null_model_B2 <- bootstrapNullAlternativeModel(df = preproc_df, params_df = model_params_df, B = 2)
[1] "Warning: You have specificed B < 20, it is recommended to use at least B = 20 in order to obtain reliable results."
  |===================================================================================================| 100%

Error: BiocParallel errors
  1 remote errors, element index: 6885
  0 unevaluated and other errors
  first remote error: subscript out of bounds
In addition: Warning message:
In max(nObs) : no non-missing arguments to max; returning -Inf

The vignette code runs without problems (using the package example data). Here is my session info:

> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

Random number generation:
 RNG:     L'Ecuyer-CMRG
 Normal:  Inversion
 Sample:  Rejection

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     

other attached packages:
[1] TPP2D_1.10.0 dplyr_1.0.8  readr_2.1.2

loaded via a namespace (and not attached):
 [1] zip_2.2.0           Rcpp_1.0.8.3        pillar_1.7.0        compiler_4.1.2      bitops_1.0-7       
 [6] iterators_1.0.14    tools_4.1.2         bit_4.0.4           lifecycle_1.0.1     tibble_3.1.6       
[11] gtable_0.3.0        pkgconfig_2.0.3     rlang_1.0.2         openxlsx_4.2.5      foreach_1.5.2      
[16] rstudioapi_0.13     DBI_1.1.2           cli_3.2.0           parallel_4.1.2      withr_2.5.0        
[21] stringr_1.4.0       generics_0.1.2      vctrs_0.4.0         hms_1.1.1           bit64_4.0.5        
[26] grid_4.1.2          tidyselect_1.1.2    glue_1.6.2          R6_2.5.1            fansi_1.0.3        
[31] BiocParallel_1.28.3 vroom_1.5.7         limma_3.50.1        tidyr_1.2.0         tzdb_0.3.0         
[36] ggplot2_3.3.5       purrr_0.3.4         magrittr_2.0.3      scales_1.1.1        codetools_0.2-18   
[41] ellipsis_0.3.2      MASS_7.3-56         assertthat_0.2.1    colorspace_2.0-3    utf8_1.2.2         
[46] stringi_1.7.6       RCurl_1.98-1.6      munsell_0.5.0       doParallel_1.0.17   crayon_1.5.1
TPP2D • 110 views
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The input data to the function looks like:

> preproc_df
# A tibble: 314,317 × 16
   representative `Total Peptides` `Total Spectral Co…` clustername temperature experiment label RefCol  conc
   <chr>                     <dbl>                <dbl> <chr>             <dbl> <chr>      <chr> <chr>  <dbl>
 1 GATD3B                       13                   31 GATD3B               37 T1_2       126   128C       5
 2 NBDY                          3                    6 NBDY                 37 T1_2       126   128C       5
 3 UBA6                         31                   52 UBA6                 37 T1_2       126   128C       5
 4 ESYT2                        22                   45 ESYT2                37 T1_2       126   128C       5
 5 UHRF1BP1L                    21                   26 UHRF1BP1L            37 T1_2       126   128C       5
 6 SHTN1                        48                   95 SHTN1                37 T1_2       126   128C       5
 7 TMEM223                       4                    6 TMEM223              37 T1_2       126   128C       5
 8 SLC22A23                      4                    4 SLC22A23             37 T1_2       126   128C       5
 9 MEX3A                         3                    3 MEX3A                37 T1_2       126   128C       5
10 ILVBL                        18                   44 ILVBL                37 T1_2       126   128C       5
# … with 314,307 more rows, and 7 more variables: raw_value <dbl>, raw_rel_value <dbl>, log_conc <dbl>,
#   rel_value <dbl>, value <dbl>, log2_value <dbl>, protein_id <chr>
> model_params_df
# A tibble: 6,885 × 19
   representative clustername  nObs min_qupm max_qupm nCoeffsH0 rssH0 parH0  estimateH0 residualsH0 nCoeffsH1
   <chr>          <chr>       <int>    <dbl>    <dbl>     <int> <dbl> <list> <list>     <list>          <int>
 1 A2M            A2M            50      Inf     -Inf        10 2.66  <dbl>  <dbl [50]> <dbl [50]>         24
 2 A2ML1          A2ML1          20      Inf     -Inf         4 5.06  <dbl>  <dbl [20]> <dbl [20]>         12
 3 AAAS           AAAS           40      Inf     -Inf         8 4.27  <dbl>  <dbl [40]> <dbl [40]>         20
 4 AACS           AACS           50      Inf     -Inf        10 0.427 <dbl>  <dbl [50]> <dbl [50]>         24
 5 AADAT          AADAT          40      Inf     -Inf         8 0.172 <dbl>  <dbl [40]> <dbl [40]>         20
 6 AAGAB          AAGAB          50      Inf     -Inf        10 0.416 <dbl>  <dbl [50]> <dbl [50]>         24
 7 AAK1           AAK1           50      Inf     -Inf        10 3.32  <dbl>  <dbl [50]> <dbl [50]>         24
 8 AAMDC          AAMDC          50      Inf     -Inf        10 0.656 <dbl>  <dbl [50]> <dbl [50]>         24
 9 AAMP           AAMP           50      Inf     -Inf        10 0.324 <dbl>  <dbl [50]> <dbl [50]>         24
10 AAR2           AAR2           50      Inf     -Inf        10 0.489 <dbl>  <dbl [50]> <dbl [50]>         24
# … with 6,875 more rows, and 8 more variables: rssH1 <dbl>, parH1 <list>, estimateH1 <list>,
#   residualsH1 <list>, pEC50H1 <dbl>, slopeH1 <dbl>, pEC50_slopeH1 <dbl>, detected_effectH1 <chr>

Since the error message mentions index 6885, I guess the problematic entry is:

> model_params_df[6885,]
# A tibble: 1 × 19
  representative clustername  nObs min_qupm max_qupm nCoeffsH0 rssH0 parH0  estimateH0  residualsH0 nCoeffsH1
  <chr>          <chr>       <int>    <dbl>    <dbl>     <int> <dbl> <list> <list>      <list>          <int>
1 NA             NA            130      Inf     -Inf        10  5.27 <dbl>  <dbl [130]> <dbl [130]>        24
# … with 8 more variables: rssH1 <dbl>, parH1 <list>, estimateH1 <list>, residualsH1 <list>, pEC50H1 <dbl>,
#   slopeH1 <dbl>, pEC50_slopeH1 <dbl>, detected_effectH1 <chr>

The model does not have a representative or clustername. The corresponding entries in the preproc_df:

> preproc_df[is.na(preproc_df$clustername),]
# A tibble: 130 × 16
   representative `Total Peptides` `Total Spectral Co…` clustername temperature experiment label RefCol  conc
   <chr>                     <dbl>                <dbl> <chr>             <dbl> <chr>      <chr> <chr>  <dbl>
 1 NA                           11                   35 NA                   37 T1_2       126   128C    5   
 2 NA                            8                    9 NA                   37 T1_2       126   128C    5   
 3 NA                           11                   35 NA                   37 T1_2       127N  128C    1   
 4 NA                            8                    9 NA                   37 T1_2       127N  128C    1   
 5 NA                           11                   35 NA                   37 T1_2       127C  128C    0.1 
 6 NA                            8                    9 NA                   37 T1_2       127C  128C    0.1 
 7 NA                           11                   35 NA                   37 T1_2       128N  128C    0.01
 8 NA                            8                    9 NA                   37 T1_2       128N  128C    0.01
 9 NA                           11                   35 NA                   37 T1_2       128C  128C    0   
10 NA                            8                    9 NA                   37 T1_2       128C  128C    0   
# … with 120 more rows, and 7 more variables: raw_value <dbl>, raw_rel_value <dbl>, log_conc <dbl>,
#   rel_value <dbl>, value <dbl>, log2_value <dbl>, protein_id <chr>
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Interestingly, I can not really explain how this model 6885 was generated. preproc_df is based on the output of import2Dataset() named import_dfand that contains only one representative having missing clustername:

> import_df %>% filter(is.na(clustername))
# A tibble: 130 × 13
   representative     `Total Peptides` `Total Spectra…` clustername temperature experiment label RefCol  conc
   <chr>                         <dbl>            <dbl> <chr>             <dbl> <chr>      <chr> <chr>  <dbl>
 1 A6NIZ1|Q6ZSR9|Q86…               11               35 NA                   37 T1_2       126   128C    5   
 2 A6NIZ1|Q6ZSR9|Q86…                8                9 NA                   37 T1_2       126   128C    5   
 3 A6NIZ1|Q6ZSR9|Q86…               11               35 NA                   37 T1_2       127N  128C    1   
 4 A6NIZ1|Q6ZSR9|Q86…                8                9 NA                   37 T1_2       127N  128C    1   
 5 A6NIZ1|Q6ZSR9|Q86…               11               35 NA                   37 T1_2       127C  128C    0.1 
 6 A6NIZ1|Q6ZSR9|Q86…                8                9 NA                   37 T1_2       127C  128C    0.1 
 7 A6NIZ1|Q6ZSR9|Q86…               11               35 NA                   37 T1_2       128N  128C    0.01
 8 A6NIZ1|Q6ZSR9|Q86…                8                9 NA                   37 T1_2       128N  128C    0.01
 9 A6NIZ1|Q6ZSR9|Q86…               11               35 NA                   37 T1_2       128C  128C    0   
10 A6NIZ1|Q6ZSR9|Q86…                8                9 NA                   37 T1_2       128C  128C    0   
# … with 120 more rows, and 4 more variables: raw_value <dbl>, raw_rel_value <dbl>, log_conc <dbl>,
#   rel_value <dbl>
> unique(import_df[is.na(import_df$clustername),]$representative)
[1] "A6NIZ1|Q6ZSR9|Q86TA4"

All other cases are complete:

> summary(complete.cases(import_df))
   Mode   FALSE    TRUE 
logical     130  314227
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