Entering edit mode
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
