Introduction

Function optimizes Extraction windows for DIA/SWATH so we have the same number of precursor per window. This optimization is based on spectral library data or non redundant .blib files (Bibliospec).

Prerequisites

Constant with method

data("masses")
cdsw <- Cdsw(masses , nbins = 25, digits = 1)
cdsw$plot()

knitr::kable(cdsw$asTable())

from	to	mid	width	counts
349.63	384.62	367.125	34.99	6688
383.62	418.62	401.120	35.00	8357
417.62	452.61	435.115	34.99	9661
451.61	486.61	469.110	35.00	10452
485.61	520.60	503.105	34.99	10725
519.60	554.59	537.095	34.99	10837
553.59	588.59	571.090	35.00	10433
587.59	622.58	605.085	34.99	9750
621.58	656.58	639.080	35.00	9276
655.58	690.57	673.075	34.99	8406
689.57	724.56	707.065	34.99	7848
723.56	758.56	741.060	35.00	7116
757.56	792.55	775.055	34.99	6355
791.55	826.55	809.050	35.00	5666
825.55	860.54	843.045	34.99	4923
859.54	894.53	877.035	34.99	4359
893.53	928.53	911.030	35.00	3807
927.53	962.52	945.025	34.99	3344
961.52	996.52	979.020	35.00	2724
995.52	1030.51	1013.015	34.99	2357
1029.51	1064.50	1047.005	34.99	2042
1063.50	1098.50	1081.000	35.00	1807
1097.50	1132.49	1114.995	34.99	1313
1131.49	1166.49	1148.990	35.00	1088
1165.49	1200.48	1182.985	34.99	881

constError <- cdsw$error()

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	365.330	31.40	5956
4%	380.03	406.71	393.370	26.68	6131
8%	405.71	429.24	417.475	23.53	6070
12%	428.24	450.05	439.145	21.81	6086
16%	449.05	470.06	459.555	21.01	6095
20%	469.06	488.80	478.930	19.74	6107
24%	487.80	508.12	497.960	20.32	6173
28%	507.12	526.81	516.965	19.69	6150
32%	525.81	545.79	535.800	19.98	6166
36%	544.79	565.29	555.040	20.50	6123
40%	564.29	584.80	574.545	20.51	6139
44%	583.80	605.12	594.460	21.32	6121
48%	604.12	626.34	615.230	22.22	6113
52%	625.34	648.36	636.850	23.02	6108
56%	647.36	672.34	659.850	24.98	6074
60%	671.34	696.53	683.935	25.19	6082
64%	695.53	722.89	709.210	27.36	6054
68%	721.89	751.40	736.645	29.51	6053
72%	750.40	782.43	766.415	32.03	6023
76%	781.43	817.40	799.415	35.97	5982
80%	816.40	857.96	837.180	41.56	6026
84%	856.96	905.62	881.290	48.66	5971
88%	904.62	964.93	934.775	60.31	5943
92%	963.93	1049.48	1006.705	85.55	5903
96%	1048.48	1200.48	1124.480	152.00	5863

quantileError <- cdsw$error()

Adjust windows

Using this method the window start and end is shifted to a mass range with as few MS1 peaks as possible.

knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )

from	to	mid	width	counts
350.13	380.95	365.54	30.82	5952
380.45	406.35	393.40	25.90	5932
406.05	429.05	417.55	23.00	5948
428.65	449.65	439.15	21.00	5891
449.45	469.65	459.55	20.20	5872
469.45	488.35	478.90	18.90	5860
488.15	508.05	498.10	19.90	6137
507.45	526.45	516.95	19.00	5892
526.15	545.45	535.80	19.30	6022
545.15	565.05	555.10	19.90	5992
564.55	584.45	574.50	19.90	5976
584.15	605.05	594.60	20.90	6035
604.55	626.05	615.30	21.50	5893
625.55	648.15	636.85	22.60	5987
647.55	672.15	659.85	24.60	6023
671.75	696.15	683.95	24.40	5890
695.55	722.55	709.05	27.00	5981
722.25	751.15	736.70	28.90	5932
750.65	782.15	766.40	31.50	5927
781.65	817.15	799.40	35.50	5944
816.65	857.55	837.10	40.90	5901
857.35	905.25	881.30	47.90	5904
905.05	964.65	934.85	59.60	5881
964.35	1049.15	1006.75	84.80	5864
1048.95	1200.05	1124.50	151.10	5843

Dynamic Swath Windows with Constraints.

Mass range can be specified (mass_range)
Maximal window size can be specified (max_window_size). This is because windows should not be to large because of optimal collision energy (personal communication by Bernd R.).
Minimal window size can be specified (min_window_size).
target number of windows can be specified (nr_windows).
boundaries between windows are placed in regions were no precursors are observed.

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

cdsw$plot()

knitr::kable(cdsw$asTable())

	from	to	mid	width	counts
0%	349.63	381.72	365.675	32.09	6123
4%	380.72	408.69	394.705	27.97	6392
8%	407.69	432.39	420.040	24.70	6553
12%	431.39	455.08	443.235	23.69	6612
16%	454.08	476.75	465.415	22.67	6698
20%	475.75	497.28	486.515	21.53	6692
24%	496.28	518.27	507.275	21.99	6720
28%	517.27	538.86	528.065	21.59	6726
32%	537.86	560.14	549.000	22.28	6732
36%	559.14	581.33	570.235	22.19	6741
40%	580.33	603.32	591.825	22.99	6625
44%	602.32	626.32	614.320	24.00	6566
48%	625.32	650.13	637.725	24.81	6563
52%	649.13	675.35	662.240	26.22	6453
56%	674.35	701.39	687.870	27.04	6428
60%	700.39	728.90	714.645	28.51	6246
64%	727.90	758.86	743.380	30.96	6202
68%	757.86	790.62	774.240	32.76	6023
72%	789.62	826.39	808.005	36.77	5948
76%	825.39	866.58	845.985	41.19	5783
80%	865.58	911.76	888.670	46.18	5452
84%	910.76	963.46	937.110	52.70	5175
88%	962.46	1026.54	994.500	64.08	4698
92%	1025.54	1101.10	1063.320	75.56	4154
96%	1100.10	1200.48	1150.290	100.38	3103

knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )

from	to	mid	width	counts
350.13	381.35	365.74	31.22	6053
381.05	408.35	394.70	27.30	6304
408.05	432.05	420.05	24.00	6323
431.65	454.65	443.15	23.00	6474
454.45	476.35	465.40	21.90	6508
476.15	496.85	486.50	20.70	6498
496.45	517.85	507.15	21.40	6587
517.45	538.45	527.95	21.00	6492
538.15	560.05	549.10	21.90	6688
559.55	581.05	570.30	21.50	6482
580.55	603.05	591.80	22.50	6526
602.55	626.05	614.30	23.50	6452
625.55	650.15	637.85	24.60	6495
649.55	675.15	662.35	25.60	6286
674.55	701.15	687.85	26.60	6293
700.55	728.55	714.55	28.00	6160
728.25	758.55	743.40	30.30	6119
758.25	790.15	774.20	31.90	5881
789.65	826.25	807.95	36.60	5932
825.65	866.25	845.95	40.60	5666
866.05	911.35	888.70	45.30	5342
911.25	963.15	937.20	51.90	5108
962.75	1026.05	994.40	63.30	4623
1025.95	1100.65	1063.30	74.70	4126
1100.45	1200.05	1150.25	99.60	3091

mixedError <- cdsw$error()

Benchmarking of the methods.

We compare the optimal number of MS1 peaks per SWATH window (same in each window) with the numbers obtained by using all of the 3 methods implemented.

barplot(c(const = constError$score1, quantile = quantileError$score1, mixed = mixedError$score1),ylab = "Manhattan distance")

barplot(c(const = constError$score2, quantile = quantileError$score2, mixed = mixedError$score2),ylab = "Euclidean distance")

We can see that Method 3 has a relatively small error although it is able to fulfill constraints such as maximum window size.

Session info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] prozor_0.3.1
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7            highr_0.9             pillar_1.6.4         
##  [4] bslib_0.3.1           compiler_4.1.1        jquerylib_0.1.4      
##  [7] tools_4.1.1           digest_0.6.28         docopt_0.7.1         
## [10] tibble_3.1.4          lifecycle_1.0.1       jsonlite_1.7.2       
## [13] evaluate_0.14         memoise_2.0.0         AhoCorasickTrie_0.1.2
## [16] lattice_0.20-44       pkgconfig_2.0.3       rlang_0.4.11         
## [19] Matrix_1.3-4          yaml_2.2.1            pkgdown_1.6.1        
## [22] xfun_0.26             fastmap_1.1.0         stringr_1.4.0        
## [25] knitr_1.36            desc_1.4.0            fs_1.5.0             
## [28] sass_0.4.0            vctrs_0.3.8           systemfonts_1.0.3    
## [31] hms_1.1.1             rprojroot_2.0.2       ade4_1.7-18          
## [34] grid_4.1.1            R6_2.5.1              textshaping_0.3.6    
## [37] fansi_0.5.0           rmarkdown_2.11        tzdb_0.1.2           
## [40] purrr_0.3.4           readr_2.0.1           seqinr_4.2-8         
## [43] magrittr_2.0.1        htmltools_0.5.2       ellipsis_0.3.2       
## [46] MASS_7.3-54           ragg_1.2.0            utf8_1.2.2           
## [49] stringi_1.7.4         cachem_1.0.6          crayon_1.4.2

Computing Dynamic SWATH Windows

Witold E. Wolski, Bernd Roschitzki

2021-12-07

Introduction

Prerequisites

Constant with method

Classical Method based on `quantile`

Adjust windows

Dynamic Swath Windows with Constraints.

Benchmarking of the methods.

Session info

Computing Dynamic SWATH Windows

Witold E. Wolski, Bernd Roschitzki

2021-12-07

Introduction

Prerequisites

Constant with method

Classical Method based on quantile

Adjust windows

Dynamic Swath Windows with Constraints.

Benchmarking of the methods.

Session info

Classical Method based on `quantile`