object 'hdac_targets' not found
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Entering edit mode
@59f497b6
Last seen 10 days ago
France

Hello everyone, I'd like to run each step of the workflow individually, so I followed the instructions of the vignette "introduction to the TPP package for analyzing Thermal Proteome Profiling data: Temperature range (TR) or concentration compound range (CCR) experiments" with the "hdacTR_smallExample" data to see how it works as shown bellow :

data("hdacTR_smallExample")
trData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = trData)
trDataNormalized <- normResults[["normData"]]
trDataHDAC <- lapply(trDataNormalized, function(d) d[Biobase::featureNames(d) %in% hdac_targets,])

Data import and normalization went well (see bellow), but when I tried to import and use Biobase, the console says that the object 'hdac_targets' is not found. should usually the program create this object or did I miss a step ?

> trData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
Importing data...

Comparisons will be performed between the following experiments:
Panobinostat_1_vs_Vehicle_1
Panobinostat_2_vs_Vehicle_2


The following valid label columns were detected:
126, 127L, 127H, 128L, 128H, 129L, 129H, 130L, 130H, 131L.

Importing TR dataset: Vehicle_1
Removing duplicate identifiers using quality column 'qupm'...
508 out of 508 rows kept for further analysis.
  -> Vehicle_1 contains 508 proteins.
  -> 504 out of 508 proteins (99.21%) suitable for curve fit (criterion: > 2 valid fold changes per protein).

Importing TR dataset: Vehicle_2
Removing duplicate identifiers using quality column 'qupm'...
509 out of 509 rows kept for further analysis.
  -> Vehicle_2 contains 509 proteins.
  -> 504 out of 509 proteins (99.02%) suitable for curve fit (criterion: > 2 valid fold changes per protein).

Importing TR dataset: Panobinostat_1
Removing duplicate identifiers using quality column 'qupm'...
508 out of 508 rows kept for further analysis.
  -> Panobinostat_1 contains 508 proteins.
  -> 504 out of 508 proteins (99.21%) suitable for curve fit (criterion: > 2 valid fold changes per protein).

Importing TR dataset: Panobinostat_2
Removing duplicate identifiers using quality column 'qupm'...
509 out of 509 rows kept for further analysis.
  -> Panobinostat_2 contains 509 proteins.
  -> 499 out of 509 proteins (98.04%) suitable for curve fit (criterion: > 2 valid fold changes per protein).


> normResults <- tpptrNormalize(data = trData)
Creating normalization set:
    1. Filtering by non fold change columns:
Filtering by annotation column(s) 'qssm' in treatment group: Vehicle_1
  Column qssm between 4 and Inf-> 312 out of 508 proteins passed.

312 out of 508 proteins passed in total.

Filtering by annotation column(s) 'qssm' in treatment group: Vehicle_2
  Column qssm between 4 and Inf-> 362 out of 509 proteins passed.

362 out of 509 proteins passed in total.

Filtering by annotation column(s) 'qssm' in treatment group: Panobinostat_1
  Column qssm between 4 and Inf-> 333 out of 508 proteins passed.

333 out of 508 proteins passed in total.

Filtering by annotation column(s) 'qssm' in treatment group: Panobinostat_2
  Column qssm between 4 and Inf-> 364 out of 509 proteins passed.

364 out of 509 proteins passed in total.

    2. Find jointP:
Detecting intersect between treatment groups (jointP).
-> JointP contains 261 proteins.

    3. Filtering fold changes:
Filtering fold changes in treatment group: Vehicle_1
  Column 7 between 0.4 and 0.6 -> 30 out of 261 proteins passed
  Column 9 between 0 and 0.3 -> 223 out of 261 proteins passed
  Column 10 between 0 and 0.2 -> 233 out of 261 proteins passed
22 out of 261 proteins passed in total.

Filtering fold changes in treatment group: Vehicle_2
  Column 7 between 0.4 and 0.6 -> 21 out of 261 proteins passed
  Column 9 between 0 and 0.3 -> 215 out of 261 proteins passed
  Column 10 between 0 and 0.2 -> 227 out of 261 proteins passed
14 out of 261 proteins passed in total.

Filtering fold changes in treatment group: Panobinostat_1
  Column 7 between 0.4 and 0.6 -> 34 out of 261 proteins passed
  Column 9 between 0 and 0.3 -> 217 out of 261 proteins passed
  Column 10 between 0 and 0.2 -> 224 out of 261 proteins passed
21 out of 261 proteins passed in total.

Filtering fold changes in treatment group: Panobinostat_2
  Column 7 between 0.4 and 0.6 -> 15 out of 261 proteins passed
  Column 9 between 0 and 0.3 -> 221 out of 261 proteins passed
  Column 10 between 0 and 0.2 -> 225 out of 261 proteins passed
10 out of 261 proteins passed in total.

Experiment with most remaining proteins after filtering: Vehicle_1
-> NormP contains 22 proteins.
-----------------------------------
Computing normalization coefficients:
1. Computing fold change medians for proteins in normP.
2. Fitting melting curves to medians.
-> Experiment with best model fit: Vehicle_1 (R2: 0.9919)
3. Computing normalization coefficients
Creating QC plots to illustrate median curve fits.
-----------------------------------
Normalizing all proteins in all experiments.
Normalization successfully completed!

> trDataNormalized <- normResults[["normData"]]
> trDataHDAC <- lapply(trDataNormalized, function(d) d[Biobase::featureNames(d) %in% hdac_targets,])
Error in FUN(X[[i]], ...) : object 'hdac_targets' not found
sessionInfo( )
R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default


locale:
[1] LC_COLLATE=French_France.utf8  LC_CTYPE=French_France.utf8    LC_MONETARY=French_France.utf8
[4] LC_NUMERIC=C                   LC_TIME=French_France.utf8    

time zone: Europe/Paris
tzcode source: internal

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

other attached packages:
[1] TPP_3.30.0          tidyr_1.3.1         magrittr_2.0.3      dplyr_1.1.4         Biobase_2.62.0      BiocGenerics_0.48.1

loaded via a namespace (and not attached):
 [1] utf8_1.2.4           generics_0.1.3       bitops_1.0-8         futile.options_1.0.1 stringi_1.8.4       
 [6] digest_0.6.37        RColorBrewer_1.1-3   evaluate_0.24.0      grid_4.3.2           iterators_1.0.14    
[11] fastmap_1.2.0        foreach_1.5.2        doParallel_1.0.17    plyr_1.8.9           zip_2.3.1           
[16] limma_3.58.1         formatR_1.14         gridExtra_2.3        BiocManager_1.30.25  purrr_1.0.2         
[21] fansi_1.0.6          scales_1.3.0         codetools_0.2-20     cli_3.6.3            rlang_1.1.4         
[26] futile.logger_1.4.3  munsell_0.5.1        splines_4.3.2        proto_1.0.0          tools_4.3.2         
[31] parallel_4.3.2       reshape2_1.4.4       colorspace_2.1-1     ggplot2_3.5.1        nls2_0.3-4          
[36] VGAM_1.1-11          lambda.r_1.2.4       vctrs_0.6.5          R6_2.5.1             stats4_4.3.2        
[41] lifecycle_1.0.4      stringr_1.5.1        MASS_7.3-60.0.1      pkgconfig_2.0.3      pillar_1.9.0        
[46] openxlsx_4.2.7       gtable_0.3.5         data.table_1.16.0    glue_1.7.0           Rcpp_1.0.13         
[51] statmod_1.5.0        xfun_0.47            tibble_3.2.1         tidyselect_1.2.1     rstudioapi_0.16.0   
[56] knitr_1.48           htmltools_0.5.8.1    rmarkdown_2.28       VennDiagram_1.7.3    compiler_4.3.2      
[61] RCurl_1.98-1.16
TPP • 270 views
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2
Entering edit mode
@james-w-macdonald-5106
Last seen 1 day ago
United States

I believe you missed some steps:

## ----trTargets-------------------------------------------------------------
tr_targets <- subset(TRresults, fulfills_all_4_requirements)$Protein_ID
print(tr_targets)

## ----trHDACTargets---------------------------------------------------------
hdac_targets <- grep("HDAC", tr_targets, value=TRUE)
print(hdac_targets)

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