Filtering

Retention Time Modelling

Feature Space

Using nsd = 2 and ppm error = 10 as an example.
Optimal values are selected below.

Grid Optimisation

Using 20% of the data.

plotGrid(obj, what=“details”)

EMRT Matching

Performance

Exported Files

• Identification Peptides
• Quantitation Peptides
• Merged Peptides
• Matched Peptides
• QuantPeptides.csv
• Grid Details
• Saved R Object (binary)

Full Analysis Log

• Instance created on Wed Jun 6 23:37:36 2012
• Read identification peptide data [5915,18]
• Read quantitation peptide data [4731,17]
• Read quantitation Pep3D data [8356,10]
• Filtered identification peptide by PepFrag1|2 [5684,18]
• Filtered quantitation peptide by PepFrag1|2 [4492,17]
• Filtered quantitation Pep3D by Function [8356,10]
• Kept unique spectrum ids quantitation Pep3D [8356,10]
  
• Added identification statistics to quantitation data
• Filtered quantitation Random entries [3492,21]
• Added identification statistics to identification data
• Filtered identification Random entries [4979,22]
• Instance updated to synapter 1.99.0 on Sun Oct 16 16:47:24 2016
• Kept quantitation non duplicated peptide sequences [3327,21]
• Kept identification non duplicated peptide sequences [4852,22]
• Kept identification peptides that match unique protein (0 missed cleavages; I/L treatment: I != L) [3726,22]
• Kept quantitation peptides that match unique protein (0 missed cleavages; I/L treatment: I != L) [2279,21]
• Keeping quant peptides of length >= 7 [2105,21]
• Keeping ident peptides of length >= 7 [3545,22]
• Set peptide score fdr to 0.01
• Filtered quantitation peptide data on pep score with fdr 0.01 [1860,21]
• Filtered identification peptide data on pep score with fdr 0.01 [3097,22]
• Set quantitation ppm error to 20
• Quantitation mass error filtered at 20 ppm [1849,21]
• Set identification ppm error to 20
• Identification mass error filtered at 20 ppm [3045,22]
• Set protein fpr to 0.01
• Filtered identification peptide data using protein fpr 0.01 [3045,22]
• Set protein fpr to 0.01
• Filtered quantitation peptide data using protein fpr 0.01 [1840,21]
• Merged identification and quantitation data [1456,44]
• Set span to 0.05
• Modelled retention time using lowess and span 0.05
• Set nsd to 2
• Set ppm error to 10
• Performed grid search using 20% of identification peptides.
• Set ‘nsd’, ‘ppm error’ and ‘im diff’ to 2, 8 and Inf (best ‘auto’)
• Matched identification peptides and quantitation EMRTs [3045,37]
• Rescue EMRTs [3045,37] (rescue)

Session Info

## R version 3.5.1 Patched (2018-08-16 r75161)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.5 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.0
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] synapter_2.5.2      MSnbase_2.7.4       ProtGenerics_1.12.0
## [4] BiocParallel_1.14.2 mzR_2.15.2          Rcpp_0.12.18       
## [7] Biobase_2.40.0      BiocGenerics_0.26.0
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.3.1            vsn_3.48.1            splines_3.5.1        
##  [4] foreach_1.4.4         assertthat_0.2.0      highr_0.7            
##  [7] affy_1.58.0           stats4_3.5.1          yaml_2.2.0           
## [10] impute_1.54.0         highlight_0.4.7.2     pillar_1.3.0         
## [13] backports_1.1.2       lattice_0.20-35       glue_1.3.0           
## [16] limma_3.36.3          digest_0.6.16         RColorBrewer_1.1-2   
## [19] XVector_0.20.0        qvalue_2.12.0         colorspace_1.3-2     
## [22] Matrix_1.2-14         htmltools_0.3.6       preprocessCore_1.42.0
## [25] plyr_1.8.4            devtools_1.13.6       MALDIquant_1.18      
## [28] XML_3.98-1.16         pkgconfig_2.0.2       zlibbioc_1.26.0      
## [31] purrr_0.2.5           scales_1.0.0          whisker_0.3-2        
## [34] affyio_1.50.0         cleaver_1.18.0        tibble_1.4.2         
## [37] IRanges_2.14.11       ggplot2_3.0.0         withr_2.1.2          
## [40] lazyeval_0.2.1        cli_1.0.0             survival_2.42-6      
## [43] magrittr_1.5          crayon_1.3.4          memoise_1.1.0        
## [46] evaluate_0.11         fs_1.2.6              doParallel_1.0.11    
## [49] MASS_7.3-50           xml2_1.2.0            BiocInstaller_1.30.0 
## [52] tools_3.5.1           hms_0.4.2             stringr_1.3.1        
## [55] S4Vectors_0.18.3      munsell_0.5.0         bindrcpp_0.2.2       
## [58] Biostrings_2.48.0     pcaMethods_1.72.0     compiler_3.5.1       
## [61] pkgdown_1.1.0         mzID_1.18.0           rlang_0.2.2          
## [64] grid_3.5.1            rstudioapi_0.7        iterators_1.0.10     
## [67] rmarkdown_1.10        testthat_2.0.0        gtable_0.2.0         
## [70] codetools_0.2-15      multtest_2.36.0       rematch_1.0.1        
## [73] roxygen2_6.1.0        curl_3.2              reshape2_1.4.3       
## [76] R6_2.2.2              knitr_1.20            dplyr_0.7.6          
## [79] bindr_0.1.1           commonmark_1.5        rprojroot_1.3-2      
## [82] readr_1.1.1           desc_1.2.0            stringi_1.2.4        
## [85] tidyselect_0.2.4