Package version: specL 1.7.6

Contents

!!!Caution work in progress!!!

1 Introduction

Function optimizes Extraction windows so we have the same number of precursor per window. To do it uses spectral library or nonredundant blib.

2 Prerequisites

3 Constant with method

cdsw <- Cdsw(masses)
cdsw$plot()

knitr::kable(cdsw$asTable())
from to mid width counts
349.63 384.62 192.310 34.99 6688
383.62 418.62 209.310 35.00 8357
417.62 452.61 226.305 34.99 9661
451.61 486.61 243.305 35.00 10452
485.61 520.60 260.300 34.99 10725
519.60 554.59 277.295 34.99 10837
553.59 588.59 294.295 35.00 10433
587.59 622.58 311.290 34.99 9750
621.58 656.58 328.290 35.00 9276
655.58 690.57 345.285 34.99 8406
689.57 724.56 362.280 34.99 7848
723.56 758.56 379.280 35.00 7116
757.56 792.55 396.275 34.99 6355
791.55 826.55 413.275 35.00 5666
825.55 860.54 430.270 34.99 4923
859.54 894.53 447.265 34.99 4359
893.53 928.53 464.265 35.00 3807
927.53 962.52 481.260 34.99 3344
961.52 996.52 498.260 35.00 2724
995.52 1030.51 515.255 34.99 2357
1029.51 1064.50 532.250 34.99 2042
1063.50 1098.50 549.250 35.00 1807
1097.50 1132.49 566.245 34.99 1313
1131.49 1166.49 583.245 35.00 1088
1165.49 1200.48 600.240 34.99 881

3.1 Error

tmp <-cdsw$error()
tmp$score1
## [1] 0.4466212
tmp$score2
## [1] 0.02027801

4 Classical Method based on quantile

Same number of MS1 precursors in each window

cdsw$quantile_breaks()
cdsw$plot()

knitr::kable(cdsw$asTable())
from to mid width counts
0% 349.63 381.03 190.515 31.40 5956
4% 380.03 406.71 203.355 26.68 6131
8% 405.71 429.24 214.620 23.53 6070
12% 428.24 450.05 225.025 21.81 6086
16% 449.05 470.06 235.030 21.01 6095
20% 469.06 488.80 244.400 19.74 6107
24% 487.80 508.12 254.060 20.32 6173
28% 507.12 526.81 263.405 19.69 6150
32% 525.81 545.79 272.895 19.98 6166
36% 544.79 565.29 282.645 20.50 6123
40% 564.29 584.80 292.400 20.51 6139
44% 583.80 605.12 302.560 21.32 6121
48% 604.12 626.34 313.170 22.22 6113
52% 625.34 648.36 324.180 23.02 6108
56% 647.36 672.34 336.170 24.98 6074
60% 671.34 696.53 348.265 25.19 6082
64% 695.53 722.89 361.445 27.36 6054
68% 721.89 751.40 375.700 29.51 6053
72% 750.40 782.43 391.215 32.03 6023
76% 781.43 817.40 408.700 35.97 5982
80% 816.40 857.96 428.980 41.56 6026
84% 856.96 905.62 452.810 48.66 5971
88% 904.62 964.93 482.465 60.31 5943
92% 963.93 1049.48 524.740 85.55 5903
96% 1048.48 1200.48 600.240 152.00 5863

4.1 Error

tmp <-cdsw$error()
tmp$score1
## [1] 0.002159576
tmp$score2
## [1] 4.484344e-05

4.2 Adjust windows

Shifts window start and an to a mass range with few MS1 peaks.

knitr::kable(cdsw$optimizeWindows())

from to mid width counts
349.63 381.05 365.340 31.42 5956
379.95 406.95 393.450 27.00 6208
405.65 429.35 417.500 23.70 6153
428.15 450.15 439.150 22.00 6143
448.85 470.15 459.500 21.30 6137
469.05 488.85 478.950 19.80 6110
487.65 508.15 497.900 20.50 6284
507.05 526.85 516.950 19.80 6154
525.45 546.05 535.750 20.60 6391
544.45 565.45 554.950 21.00 6322
564.15 585.05 574.600 20.90 6267
583.55 605.15 594.350 21.60 6199
604.05 626.45 615.250 22.40 6144
625.15 648.45 636.800 23.30 6217
647.25 672.45 659.850 25.20 6215
671.25 696.55 683.900 25.30 6121
695.45 723.15 709.300 27.70 6105
721.75 751.55 736.650 29.80 6134
750.25 782.55 766.400 32.30 6076
781.25 817.55 799.400 36.30 6086
816.25 858.05 837.150 41.80 6062
856.85 905.65 881.250 48.80 6007
904.55 965.05 934.800 60.50 5967
963.85 1049.55 1006.700 85.70 5927
1048.45 1200.48 1124.465 152.03 5867

5 Iterative Distribution Mixing based cdsw

5.1 Requirements

cdsw$sampling_breaks(maxwindow = 100,plot = TRUE)

cdsw$plot()

knitr::kable(cdsw$asTable())
from to mid width counts
0% 349.63 381.69 190.845 32.06 6098
4% 380.69 408.26 204.130 27.57 6377
8% 407.26 432.42 216.210 25.16 6603
12% 431.42 455.10 227.550 23.68 6612
16% 454.10 476.73 238.365 22.63 6677
20% 475.73 497.28 248.640 21.55 6721
24% 496.28 518.14 259.070 21.86 6659
28% 517.14 538.81 269.405 21.67 6773
32% 537.81 559.81 279.905 22.00 6677
36% 558.81 581.30 290.650 22.49 6745
40% 580.30 603.30 301.650 23.00 6655
44% 602.30 626.31 313.155 24.01 6590
48% 625.31 650.11 325.055 24.80 6582
52% 649.11 675.28 337.640 26.17 6394
56% 674.28 701.40 350.700 27.12 6489
60% 700.40 729.19 364.595 28.79 6292
64% 728.19 758.93 379.465 30.74 6203
68% 757.93 791.43 395.715 33.50 6121
72% 790.43 826.93 413.465 36.50 5870
76% 825.93 866.84 433.420 40.91 5689
80% 865.84 911.96 455.980 46.12 5455
84% 910.96 963.44 481.720 52.48 5142
88% 962.44 1026.51 513.255 64.07 4700
92% 1025.51 1100.57 550.285 75.06 4140
96% 1099.57 1200.48 600.240 100.91 3126

5.2 Error

tmp <-cdsw$error()
tmp$score1
## [1] 0.09535936
tmp$score2
## [1] 0.005544713

6 Session info

Here is the output of sessionInfo() on the system on which this document was compiled:

## R version 3.3.0 (2016-05-03)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252 
## [2] LC_CTYPE=English_United Kingdom.1252   
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] cdsw_0.1.0      BiocStyle_2.0.2
## 
## loaded via a namespace (and not attached):
##  [1] magrittr_1.5    formatR_1.4     tools_3.3.0     htmltools_0.3.5
##  [5] yaml_2.1.13     Rcpp_0.12.7     stringi_1.1.1   rmarkdown_0.9.6
##  [9] highr_0.6       knitr_1.13      stringr_1.0.0   digest_0.6.10  
## [13] evaluate_0.9