examples/get_data.R
In dschief001/timsr: Easily Access timsTOF Data
library(timsr)
path = 'path/to/your/data.d'
# Do you want to have access only to 'frame', 'scan', 'time of flight', and 'intensity'?
accept_Bruker_EULA_and_on_Windows_or_Linux = TRUE
if(accept_Bruker_EULA_and_on_Windows_or_Linux){
folder_to_stode_priopriatary_code = "/home/matteo"
path_to_bruker_dll = download_bruker_proprietary_code(folder_to_stode_priopriatary_code)
setup_bruker_so(path_to_bruker_dll)
all_columns = c('frame','scan','tof','intensity','mz','inv_ion_mobility','retention_time')
} else {
all_columns = c('frame','scan','tof','intensity','retention_time')
}
D = TimsR(path) # get data handle
print(D)
print(length(D)) # The number of peaks.
# 404183877
# Get a data,frame with data from frames 1, 5, and 67.
X = D[c(1,5,67), all_columns]
print(X)
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 1 33 312260 9 1174.6558 1.6011418 0.3264921
# 2: 1 34 220720 9 733.4809 1.6000000 0.3264921
# 3: 1 34 261438 9 916.9524 1.6000000 0.3264921
# 4: 1 36 33072 9 152.3557 1.5977164 0.3264921
# 5: 1 36 242110 9 827.3114 1.5977164 0.3264921
# ---
# 224733: 67 917 192745 51 619.2850 0.6007742 7.4056544
# 224734: 67 917 201838 54 655.3439 0.6007742 7.4056544
# 224735: 67 917 205954 19 672.0017 0.6007742 7.4056544
# 224736: 67 917 236501 57 802.1606 0.6007742 7.4056544
# 224737: 67 917 289480 95 1055.2037 0.6007742 7.4056544
# Get a dict with each 10th frame, starting from frame 2, finishing on frame 1000.
X = D[seq(2,1000,10), all_columns]
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 2 33 97298 9 302.3477 1.6011418 0.4347063
# 2: 2 33 310524 9 1165.3273 1.6011418 0.4347063
# 3: 2 34 127985 9 391.9841 1.6000000 0.4347063
# 4: 2 35 280460 9 1009.6751 1.5988582 0.4347063
# 5: 2 37 329377 72 1268.6262 1.5965746 0.4347063
# ---
# 669553: 992 909 198994 9 643.9562 0.6097471 106.7102786
# 669554: 992 909 282616 9 1020.4663 0.6097471 106.7102786
# 669555: 992 912 143270 9 440.9670 0.6063821 106.7102786
# 669556: 992 915 309328 9 1158.9221 0.6030173 106.7102786
# 669557: 992 916 224410 9 749.2647 0.6018958 106.7102786
# Get all MS1 frames
# print(query(D, frames=MS1(D)))
# ATTENTION: that's quite a lot of data!!! And R will first make a stupid copy, because it's bad. You might exceed your RAM.
# Getting subset of columns: simply specify 'columns':
D[c(1,5,67), c('scan','intensity')]
# scan intensity
# 1: 33 9
# 2: 34 9
# 3: 34 9
# 4: 36 9
# 5: 36 9
# ---
# 224733: 917 51
# 224734: 917 54
# 224735: 917 19
# 224736: 917 57
# 224737: 917 95
#
# this is also the only way to get data without accepting Bruker terms of service and on MacOS (for time being).
# In that case, you can choose among columns:
# c('frame','scan','tof','intensity','rt')
# Not specifying columns defaults to extracting only the actaully stored raw data in t'analysis.tdf_raw' without their transformations:
D[100]
# frame scan tof intensity
# 1: 100 35 389679 9
# 2: 100 35 394578 9
# 3: 100 37 78036 9
# 4: 100 37 210934 9
# 5: 100 37 211498 9
# ---
# 7281: 100 910 156846 9
# 7282: 100 910 355234 9
# 7283: 100 911 294204 9
# 7284: 100 913 248788 9
# 7285: 100 915 100120 120
D[1:100]
# frame scan tof intensity
# 1: 1 33 312260 9
# 2: 1 34 220720 9
# 3: 1 34 261438 9
# 4: 1 36 33072 9
# 5: 1 36 242110 9
# ---
# 1633354: 100 910 156846 9
# 1633355: 100 910 355234 9
# 1633356: 100 911 294204 9
# 1633357: 100 913 248788 9
# 1633358: 100 915 100120 120
# The frame lasts a convenient time unit that well suits chromatography peak elution.
# What if you were interested instead in finding out which frames eluted in a given time
# time of the experiment?
# For this reasone, we have prepared a retention time based query:
# suppose you are interested in all frames corresponding to all that eluted between 10 and 12
# second of the experiment.
pprint(rt_query(opentims, 10, 12))
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 92 33 361758 9 1456.2835 1.6011418 10.08690
# 2: 92 36 65738 9 222.2822 1.5977164 10.08690
# 3: 92 41 308330 9 1153.5909 1.5920078 10.08690
# 4: 92 43 123618 9 378.5190 1.5897245 10.08690
# 5: 92 48 65346 9 221.3651 1.5840168 10.08690
# ---
# 128130: 109 914 138760 65 426.2142 0.6041389 11.91001
# 128131: 109 914 142129 69 437.2109 0.6041389 11.91001
# 128132: 109 914 144566 58 445.2528 0.6041389 11.91001
# 128133: 109 916 138933 91 426.7755 0.6018958 11.91001
# 128134: 109 917 373182 9 1525.5765 0.6007742 11.91001
# All MS1 frames, but one at a time:
for(fr in MS1(D)) print(D[fr, all_columns])
# Of course, it's better to use lapply or mclapply to speed up things.
# Extracting information from 'analysis.tdf':
# here are table names
tables_names(D)
# [1] "CalibrationInfo" "DiaFrameMsMsInfo"
# [3] "DiaFrameMsMsWindowGroups" "DiaFrameMsMsWindows"
# [5] "ErrorLog" "FrameMsMsInfo"
# [7] "FrameProperties" "Frames"
# [9] "GlobalMetadata" "GroupProperties"
# [11] "MzCalibration" "Properties"
# [13] "PropertyDefinitions" "PropertyGroups"
# [15] "Segments" "TimsCalibration"
# To get only one, use:
table2dt(D, 'Frames')
# Id Time Polarity ScanMode MsMsType TimsId MaxIntensity
# 1: 1 0.3264921 + 9 0 0 192
# 2: 2 0.4347063 + 9 9 8192 2384
# 3: 3 0.5409869 + 9 9 32768 4905
# 4: 4 0.6488865 + 9 9 61440 6206
# 5: 5 0.7566600 + 9 9 94208 6206
# ---
# 11549: 11549 1243.4941418 + 9 9 995598336 129
# 11550: 11550 1243.5991705 + 9 9 995602432 137
# 11551: 11551 1243.7072909 + 9 0 995606528 253
# 11552: 11552 1243.8112220 + 9 9 995631104 167
# 11553: 11553 1243.9193366 + 9 9 995635200 155
# SummedIntensities NumScans NumPeaks MzCalibration T1 T2
# 1: 95955 918 1601 1 25.57090 26.67739
# 2: 579402 918 6598 1 25.57090 26.67739
# 3: 907089 918 8706 1 25.57090 26.67739
# 4: 1192350 918 10820 1 25.57090 26.67739
# 5: 1408123 918 12597 1 25.57090 26.67739
# ---
# 11549: 5065 918 319 1 25.57136 26.63330
# 11550: 5414 918 345 1 25.57128 26.63330
# 11551: 406508 918 6743 1 25.57128 26.63330
# 11552: 8097 918 360 1 25.57128 26.63330
# 11553: 8633 918 397 1 25.57128 26.63330
# TimsCalibration PropertyGroup AccumulationTime RampTime
# 1: 1 1 99.953 99.953
# 2: 1 1 99.953 99.953
# 3: 1 1 99.953 99.953
# 4: 1 1 99.953 99.953
# 5: 1 1 99.953 99.953
# ---
# 11549: 1 1 99.953 99.953
# 11550: 1 1 99.953 99.953
# 11551: 1 1 99.953 99.953
# 11552: 1 1 99.953 99.953
# 11553: 1 1 99.953 99.953
# To get all of them as a named list of data.tables:
tdf.tables = tdf.tables(D)
tdf.tables[['TimsCalibration']]
# Id ModelType C0 C1 C2 C3 C4 C5 C6 C7 C8
# 1: 1 2 1 917 213.5998 75.81729 33 1 -0.009065829 135.4364 13.32608
# C9
# 1: 1663.341
# Remember, R is ultimately a BAD BAD computer language.
# If you have problems with memory getting freed immediately,
# you can call the garbage collector using:
cleanMem()
dschief001/timsr documentation built on Dec. 20, 2021, 1:21 a.m.
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library(timsr)
path = 'path/to/your/data.d'
# Do you want to have access only to 'frame', 'scan', 'time of flight', and 'intensity'?
accept_Bruker_EULA_and_on_Windows_or_Linux = TRUE
if(accept_Bruker_EULA_and_on_Windows_or_Linux){
folder_to_stode_priopriatary_code = "/home/matteo"
path_to_bruker_dll = download_bruker_proprietary_code(folder_to_stode_priopriatary_code)
setup_bruker_so(path_to_bruker_dll)
all_columns = c('frame','scan','tof','intensity','mz','inv_ion_mobility','retention_time')
} else {
all_columns = c('frame','scan','tof','intensity','retention_time')
}
D = TimsR(path) # get data handle
print(D)
print(length(D)) # The number of peaks.
# 404183877
# Get a data,frame with data from frames 1, 5, and 67.
X = D[c(1,5,67), all_columns]
print(X)
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 1 33 312260 9 1174.6558 1.6011418 0.3264921
# 2: 1 34 220720 9 733.4809 1.6000000 0.3264921
# 3: 1 34 261438 9 916.9524 1.6000000 0.3264921
# 4: 1 36 33072 9 152.3557 1.5977164 0.3264921
# 5: 1 36 242110 9 827.3114 1.5977164 0.3264921
# ---
# 224733: 67 917 192745 51 619.2850 0.6007742 7.4056544
# 224734: 67 917 201838 54 655.3439 0.6007742 7.4056544
# 224735: 67 917 205954 19 672.0017 0.6007742 7.4056544
# 224736: 67 917 236501 57 802.1606 0.6007742 7.4056544
# 224737: 67 917 289480 95 1055.2037 0.6007742 7.4056544
# Get a dict with each 10th frame, starting from frame 2, finishing on frame 1000.
X = D[seq(2,1000,10), all_columns]
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 2 33 97298 9 302.3477 1.6011418 0.4347063
# 2: 2 33 310524 9 1165.3273 1.6011418 0.4347063
# 3: 2 34 127985 9 391.9841 1.6000000 0.4347063
# 4: 2 35 280460 9 1009.6751 1.5988582 0.4347063
# 5: 2 37 329377 72 1268.6262 1.5965746 0.4347063
# ---
# 669553: 992 909 198994 9 643.9562 0.6097471 106.7102786
# 669554: 992 909 282616 9 1020.4663 0.6097471 106.7102786
# 669555: 992 912 143270 9 440.9670 0.6063821 106.7102786
# 669556: 992 915 309328 9 1158.9221 0.6030173 106.7102786
# 669557: 992 916 224410 9 749.2647 0.6018958 106.7102786
# Get all MS1 frames
# print(query(D, frames=MS1(D)))
# ATTENTION: that's quite a lot of data!!! And R will first make a stupid copy, because it's bad. You might exceed your RAM.
# Getting subset of columns: simply specify 'columns':
D[c(1,5,67), c('scan','intensity')]
# scan intensity
# 1: 33 9
# 2: 34 9
# 3: 34 9
# 4: 36 9
# 5: 36 9
# ---
# 224733: 917 51
# 224734: 917 54
# 224735: 917 19
# 224736: 917 57
# 224737: 917 95
#
# this is also the only way to get data without accepting Bruker terms of service and on MacOS (for time being).
# In that case, you can choose among columns:
# c('frame','scan','tof','intensity','rt')
# Not specifying columns defaults to extracting only the actaully stored raw data in t'analysis.tdf_raw' without their transformations:
D[100]
# frame scan tof intensity
# 1: 100 35 389679 9
# 2: 100 35 394578 9
# 3: 100 37 78036 9
# 4: 100 37 210934 9
# 5: 100 37 211498 9
# ---
# 7281: 100 910 156846 9
# 7282: 100 910 355234 9
# 7283: 100 911 294204 9
# 7284: 100 913 248788 9
# 7285: 100 915 100120 120
D[1:100]
# frame scan tof intensity
# 1: 1 33 312260 9
# 2: 1 34 220720 9
# 3: 1 34 261438 9
# 4: 1 36 33072 9
# 5: 1 36 242110 9
# ---
# 1633354: 100 910 156846 9
# 1633355: 100 910 355234 9
# 1633356: 100 911 294204 9
# 1633357: 100 913 248788 9
# 1633358: 100 915 100120 120
# The frame lasts a convenient time unit that well suits chromatography peak elution.
# What if you were interested instead in finding out which frames eluted in a given time
# time of the experiment?
# For this reasone, we have prepared a retention time based query:
# suppose you are interested in all frames corresponding to all that eluted between 10 and 12
# second of the experiment.
pprint(rt_query(opentims, 10, 12))
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1: 92 33 361758 9 1456.2835 1.6011418 10.08690
# 2: 92 36 65738 9 222.2822 1.5977164 10.08690
# 3: 92 41 308330 9 1153.5909 1.5920078 10.08690
# 4: 92 43 123618 9 378.5190 1.5897245 10.08690
# 5: 92 48 65346 9 221.3651 1.5840168 10.08690
# ---
# 128130: 109 914 138760 65 426.2142 0.6041389 11.91001
# 128131: 109 914 142129 69 437.2109 0.6041389 11.91001
# 128132: 109 914 144566 58 445.2528 0.6041389 11.91001
# 128133: 109 916 138933 91 426.7755 0.6018958 11.91001
# 128134: 109 917 373182 9 1525.5765 0.6007742 11.91001
# All MS1 frames, but one at a time:
for(fr in MS1(D)) print(D[fr, all_columns])
# Of course, it's better to use lapply or mclapply to speed up things.
# Extracting information from 'analysis.tdf':
# here are table names
tables_names(D)
# [1] "CalibrationInfo" "DiaFrameMsMsInfo"
# [3] "DiaFrameMsMsWindowGroups" "DiaFrameMsMsWindows"
# [5] "ErrorLog" "FrameMsMsInfo"
# [7] "FrameProperties" "Frames"
# [9] "GlobalMetadata" "GroupProperties"
# [11] "MzCalibration" "Properties"
# [13] "PropertyDefinitions" "PropertyGroups"
# [15] "Segments" "TimsCalibration"
# To get only one, use:
table2dt(D, 'Frames')
# Id Time Polarity ScanMode MsMsType TimsId MaxIntensity
# 1: 1 0.3264921 + 9 0 0 192
# 2: 2 0.4347063 + 9 9 8192 2384
# 3: 3 0.5409869 + 9 9 32768 4905
# 4: 4 0.6488865 + 9 9 61440 6206
# 5: 5 0.7566600 + 9 9 94208 6206
# ---
# 11549: 11549 1243.4941418 + 9 9 995598336 129
# 11550: 11550 1243.5991705 + 9 9 995602432 137
# 11551: 11551 1243.7072909 + 9 0 995606528 253
# 11552: 11552 1243.8112220 + 9 9 995631104 167
# 11553: 11553 1243.9193366 + 9 9 995635200 155
# SummedIntensities NumScans NumPeaks MzCalibration T1 T2
# 1: 95955 918 1601 1 25.57090 26.67739
# 2: 579402 918 6598 1 25.57090 26.67739
# 3: 907089 918 8706 1 25.57090 26.67739
# 4: 1192350 918 10820 1 25.57090 26.67739
# 5: 1408123 918 12597 1 25.57090 26.67739
# ---
# 11549: 5065 918 319 1 25.57136 26.63330
# 11550: 5414 918 345 1 25.57128 26.63330
# 11551: 406508 918 6743 1 25.57128 26.63330
# 11552: 8097 918 360 1 25.57128 26.63330
# 11553: 8633 918 397 1 25.57128 26.63330
# TimsCalibration PropertyGroup AccumulationTime RampTime
# 1: 1 1 99.953 99.953
# 2: 1 1 99.953 99.953
# 3: 1 1 99.953 99.953
# 4: 1 1 99.953 99.953
# 5: 1 1 99.953 99.953
# ---
# 11549: 1 1 99.953 99.953
# 11550: 1 1 99.953 99.953
# 11551: 1 1 99.953 99.953
# 11552: 1 1 99.953 99.953
# 11553: 1 1 99.953 99.953
# To get all of them as a named list of data.tables:
tdf.tables = tdf.tables(D)
tdf.tables[['TimsCalibration']]
# Id ModelType C0 C1 C2 C3 C4 C5 C6 C7 C8
# 1: 1 2 1 917 213.5998 75.81729 33 1 -0.009065829 135.4364 13.32608
# C9
# 1: 1663.341
# Remember, R is ultimately a BAD BAD computer language.
# If you have problems with memory getting freed immediately,
# you can call the garbage collector using:
cleanMem()
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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