R/mutate_mean_intensity.R, R/mutate_median_intensity.R, R/mutate_rsd.R, and 4 more
add_new_column-mass_dataset.RdAdd mean intensity for each feature to variable_info
Add median intensity for each feature to variable_info
Calculate RSD for features
Add NA number for each sample to sample_info.
Add NA frequency for each sample to sample_info
Add NA number for each sample to sample_info.
Add NA frequency for each sample to sample_info
Add NA number for each feature to variable_info
Add NA number for each feature to variable_info
Add NA number for each feature to variable_info
Add NA number for each feature to variable_info
mutate_mean_intensity(object, according_to_samples = "all", na.rm = TRUE)
mutate_median_intensity(object, according_to_samples = "all", na.rm = TRUE)
mutate_rsd(object, according_to_samples = "all")
mutate_sample_na_number(object, according_to_variables = "all")
mutate_sample_na_freq(object, according_to_variables = "all")
mutate_sample_zero_number(object, according_to_variables = "all")
mutate_sample_zero_freq(object, according_to_variables = "all")
mutate_variable_na_number(object, according_to_samples = "all")
mutate_variable_na_freq(object, according_to_samples = "all")
mutate_variable_zero_number(object, according_to_samples = "all")
mutate_variable_zero_freq(object, according_to_samples = "all")(required) mass_dataset class object.
(required) What samples used to calculate NA number or percentage. Default is "all". If you want to use only several samples, provide their names as a vector.
na.rm
(required) What variables used to calculate NA number or percentage. Default is "all". If you want to use only several variables, provide their names as a vector.
A mass_dataset class object.
A mass_dataset class object
A mass_data class object.
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
A mass_dataset class object
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate mean intensity according to all the samples
object2 =
mutate_mean_intensity(object = object, na.rm = TRUE)
object2
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 4 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 4 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_mean_intensity ----------
#> Package Function.used Time
#> 1 massdataset mutate_mean_intensity() 2023-10-01 14:00:40
head(extract_variable_info(object))
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
head(extract_variable_info(object2))
#> variable_id mz rt mean_intensity
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 1808605.7
#> M79T35_POS M79T35_POS 79.05394 35.36550 2671173.4
#> M307T548_POS M307T548_POS 307.14035 547.56641 276256.3
#> M183T224_POS M183T224_POS 183.06209 224.32777 5103244.9
#> M349T47_POS M349T47_POS 349.01584 47.00262 6789862.4
#> M182T828_POS M182T828_POS 181.99775 828.35712 4326865.8
##calculate mean intensity according to only QC samples
object3 =
mutate_mean_intensity(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_mean_intensity ----------
#> Package Function.used Time
#> 1 massdataset mutate_mean_intensity() 2023-10-01 14:00:40.571995
#> 2 massdataset mutate_mean_intensity() 2023-10-01 14:00:40.598109
head(extract_variable_info(object3))
#> variable_id mz rt mean_intensity mean_intensity.1
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 1808605.7 1447844.3
#> M79T35_POS M79T35_POS 79.05394 35.36550 2671173.4 2063212.8
#> M307T548_POS M307T548_POS 307.14035 547.56641 276256.3 342037.7
#> M183T224_POS M183T224_POS 183.06209 224.32777 5103244.9 NaN
#> M349T47_POS M349T47_POS 349.01584 47.00262 6789862.4 6417851.6
#> M182T828_POS M182T828_POS 181.99775 828.35712 4326865.8 3662819.1
###remain variables with mean intensity (QC) / mean intensity (Blank) > 3
qc_sample_name =
get_sample_id(object)[extract_sample_info(object)$class == "QC"]
blank_sample_name =
get_sample_id(object)[extract_sample_info(object)$class == "Blank"]
object4 =
object %>%
mutate_mean_intensity(according_to_samples = qc_sample_name,
na.rm = TRUE) %>%
mutate_mean_intensity(according_to_samples = blank_sample_name,
na.rm = TRUE) %>%
activate_mass_dataset(what = "variable_info") %>%
mutate(mean_intensity.1 = case_when(
is.na(mean_intensity.1) ~ 0,
TRUE ~ mean_intensity.1
)) %>%
mutate(mean_intensity = case_when(
is.na(mean_intensity) ~ 0,
TRUE ~ mean_intensity
)) %>%
mutate(qc_blank_ratio = mean_intensity.1 / mean_intensity) %>%
mutate(qc_blank_ratio = case_when(
is.na(qc_blank_ratio) ~ 0,
TRUE ~ qc_blank_ratio
)) %>%
filter(qc_blank_ratio > 3)
object4
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 144 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 144 x 6 data.frame]
#> 144 variables:M317T1035_POS M355T511_POS M270T586_POS ... M159T907_POS M172T916_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 6 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 4 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_mean_intensity ----------
#> Package Function.used Time
#> 1 massdataset mutate_mean_intensity() 2023-10-01 14:00:40.621658
#> 2 massdataset mutate_mean_intensity() 2023-10-01 14:00:40.636038
#> mutate ----------
#> Package Function.used Time
#> 1 massdataset mutate() 2023-10-01 14:00:40.65044
#> 2 massdataset mutate() 2023-10-01 14:00:40.654718
#> 3 massdataset mutate() 2023-10-01 14:00:40.659465
#> 4 massdataset mutate() 2023-10-01 14:00:40.664817
#> filter ----------
#> Package Function.used Time
#> 1 massdataset filter() 2023-10-01 14:00:40
object4 %>%
extract_variable_info()
#> variable_id mz rt mean_intensity
#> M317T1035_POS M317T1035_POS 316.98431 1034.84997 0.00
#> M355T511_POS M355T511_POS 355.14057 510.77341 0.00
#> M270T586_POS M270T586_POS 270.15293 585.84412 0.00
#> M297T826_POS M297T826_POS 296.97977 826.48639 0.00
#> M102T310_POS M102T310_POS 102.09111 309.99429 0.00
#> M288T32_POS M288T32_POS 287.91101 32.38870 0.00
#> M343T415_POS M343T415_POS 343.08142 415.20071 0.00
#> M340T611_POS M340T611_POS 340.17983 610.92194 0.00
#> M224T34_POS M224T34_POS 223.94330 33.89216 0.00
#> M325T33_POS M325T33_POS 324.92931 32.65797 0.00
#> M293T559_POS M293T559_POS 293.17128 559.48651 0.00
#> M169T33_POS M169T33_POS 168.97938 33.26004 0.00
#> M233T34_1_POS M233T34_1_POS 233.00991 34.19781 0.00
#> M104T1036_POS M104T1036_POS 104.07047 1035.85997 0.00
#> M259T522_POS M259T522_POS 259.16779 521.98340 0.00
#> M205T34_2_POS M205T34_2_POS 204.99926 33.56249 0.00
#> M178T54_POS M178T54_POS 178.00991 54.49800 8123950.28
#> M193T164_POS M193T164_POS 192.97888 164.14429 0.00
#> M183T821_POS M183T821_POS 182.98231 820.53082 0.00
#> M217T829_POS M217T829_POS 217.19441 829.27887 0.00
#> M312T436_POS M312T436_POS 312.24952 436.36490 0.00
#> M201T33_POS M201T33_POS 200.99608 32.95857 0.00
#> M100T38_POS M100T38_POS 99.97840 37.87358 0.00
#> M154T60_POS M154T60_POS 153.97879 60.17500 0.00
#> M180T33_POS M180T33_POS 179.97791 32.95857 0.00
#> M231T531_POS M231T531_POS 231.17326 531.31525 0.00
#> M199T120_POS M199T120_POS 199.00070 119.91092 0.00
#> M161T1024_1_POS M161T1024_1_POS 161.00031 1023.56997 0.00
#> M265T428_POS M265T428_POS 265.18709 427.92566 97164.46
#> M248T34_POS M248T34_POS 247.96430 33.89216 0.00
#> M292T34_POS M292T34_POS 291.94954 34.16998 0.00
#> M261T829_1_POS M261T829_1_POS 261.00909 829.46042 348637.28
#> M183T34_1_POS M183T34_1_POS 182.99510 33.70049 0.00
#> M179T535_POS M179T535_POS 179.07614 535.12622 0.00
#> M164T1036_POS M164T1036_POS 163.98726 1036.18997 0.00
#> M351T467_POS M351T467_POS 351.14741 467.13400 0.00
#> M102T112_POS M102T112_POS 102.09119 111.92390 0.00
#> M178T968_2_POS M178T968_2_POS 178.00998 967.70428 0.00
#> M241T822_POS M241T822_POS 241.06738 822.17419 0.00
#> M192T122_POS M192T122_POS 191.97117 122.43882 0.00
#> M195T155_POS M195T155_POS 195.09274 155.12875 0.00
#> M136T909_POS M136T909_POS 135.99948 909.14572 0.00
#> M249T33_POS M249T33_POS 248.99771 32.95857 0.00
#> M305T225_POS M305T225_POS 305.12567 225.32370 0.00
#> M167T402_POS M167T402_POS 167.06997 401.73358 715970.92
#> M323T643_POS M323T643_POS 323.12232 643.12091 0.00
#> M317T1008_POS M317T1008_POS 316.98441 1007.75222 405656.23
#> M232T235_POS M232T235_POS 232.05674 234.67485 311509.18
#> M133T103_POS M133T103_POS 132.95810 103.12468 0.00
#> M74T146_POS M74T146_POS 74.09637 145.82797 0.00
#> M106T33_POS M106T33_POS 105.96361 32.95857 0.00
#> M313T481_POS M313T481_POS 313.13986 480.83691 0.00
#> M95T1017_POS M95T1017_POS 95.08536 1017.01251 0.00
#> M141T80_POS M141T80_POS 141.07129 79.54538 0.00
#> M153T353_POS M153T353_POS 153.00479 353.41220 0.00
#> M173T866_POS M173T866_POS 172.97485 866.12396 0.00
#> M99T89_POS M99T89_POS 99.08028 89.06227 0.00
#> M323T658_POS M323T658_POS 323.15820 657.52545 0.00
#> M216T61_POS M216T61_POS 215.99487 61.31047 0.00
#> M160T33_1_POS M160T33_1_POS 159.96965 32.65797 0.00
#> M164T426_POS M164T426_POS 164.03054 425.64816 34100870.56
#> M160T1010_POS M160T1010_POS 159.99923 1010.42261 0.00
#> M237T464_POS M237T464_POS 237.12704 463.65128 0.00
#> M314T471_POS M314T471_POS 314.16304 470.54288 0.00
#> M359T481_POS M359T481_POS 359.11526 480.64351 0.00
#> M117T1034_POS M117T1034_POS 116.98565 1033.91997 0.00
#> M193T104_POS M193T104_POS 192.97900 103.90944 0.00
#> M167T461_1_POS M167T461_1_POS 167.06719 460.94473 0.00
#> M354T637_1_POS M354T637_1_POS 354.23080 636.98621 0.00
#> M215T829_1_POS M215T829_1_POS 214.97743 828.91132 0.00
#> M211T38_POS M211T38_POS 210.96743 37.91269 0.00
#> M233T33_POS M233T33_POS 232.99380 33.42523 0.00
#> M333T36_POS M333T36_POS 332.96835 36.35993 0.00
#> M173T55_1_POS M173T55_1_POS 172.99221 54.58524 0.00
#> M287T828_POS M287T828_POS 287.01774 828.22208 4509238.10
#> M148T705_POS M148T705_POS 148.05078 705.42828 0.00
#> M173T94_POS M173T94_POS 173.01185 94.03935 0.00
#> M184T102_POS M184T102_POS 184.04512 102.23300 0.00
#> M187T91_POS M187T91_POS 187.00766 90.78848 0.00
#> M158T973_POS M158T973_POS 157.97441 972.52249 0.00
#> M213T497_POS M213T497_POS 213.00415 496.90598 0.00
#> M102T925_POS M102T925_POS 102.09109 924.68045 0.00
#> M361T510_POS M361T510_POS 361.15921 510.06927 0.00
#> M160T33_2_POS M160T33_2_POS 159.98197 32.95857 0.00
#> M145T40_1_POS M145T40_1_POS 144.99743 40.33353 0.00
#> M201T930_POS M201T930_POS 201.02267 930.13623 0.00
#> M141T271_POS M141T271_POS 141.07128 271.36676 0.00
#> M325T520_POS M325T520_POS 325.13785 520.38269 825215.83
#> M246T221_POS M246T221_POS 246.10861 221.40201 0.00
#> M187T154_POS M187T154_POS 187.00765 153.82877 0.00
#> M156T165_POS M156T165_POS 155.97397 165.08819 0.00
#> M183T49_POS M183T49_POS 183.02410 48.93789 0.00
#> M271T448_POS M271T448_POS 271.15266 447.93579 0.00
#> M250T481_POS M250T481_POS 250.17661 481.03885 17019566.43
#> M261T522_POS M261T522_POS 261.18150 521.75232 0.00
#> M360T547_POS M360T547_POS 360.22115 546.53815 0.00
#> M192T150_POS M192T150_POS 191.97627 150.17074 0.00
#> M282T481_POS M282T481_POS 282.20261 480.71379 11364551.57
#> M172T33_POS M172T33_POS 171.98932 32.95857 0.00
#> M359T33_POS M359T33_POS 358.84215 32.52293 0.00
#> M200T37_POS M200T37_POS 200.02223 37.19968 0.00
#> M252T192_POS M252T192_POS 252.04628 192.37624 0.00
#> M341T605_2_POS M341T605_2_POS 341.39605 604.66263 0.00
#> M195T75_1_POS M195T75_1_POS 194.97944 74.73264 0.00
#> M151T546_POS M151T546_POS 151.00498 546.37540 3348781.68
#> M162T33_POS M162T33_POS 161.96736 32.65797 0.00
#> M200T488_POS M200T488_POS 200.17202 488.12100 0.00
#> M166T737_POS M166T737_POS 166.09402 736.74915 0.00
#> M185T1004_POS M185T1004_POS 185.01938 1004.23590 0.00
#> M153T33_1_POS M153T33_1_POS 152.96912 33.09496 0.00
#> M175T104_POS M175T104_POS 174.97963 103.73222 0.00
#> M220T963_POS M220T963_POS 220.02006 963.31354 0.00
#> M226T536_POS M226T536_POS 226.09662 535.79327 0.00
#> M273T829_2_POS M273T829_2_POS 273.25638 829.27887 0.00
#> M126T826_POS M126T826_POS 126.12746 826.33936 0.00
#> M305T34_1_POS M305T34_1_POS 304.94916 33.71391 118631.41
#> M197T933_POS M197T933_POS 196.99989 932.58405 5881674.72
#> M95T918_POS M95T918_POS 95.08532 918.17102 0.00
#> M149T694_POS M149T694_POS 149.02295 694.02527 0.00
#> M196T179_POS M196T179_POS 196.00222 178.80635 0.00
#> M301T544_POS M301T544_POS 301.17615 544.49435 0.00
#> M167T270_POS M167T270_POS 167.06854 269.85345 0.00
#> M185T961_POS M185T961_POS 185.01579 961.07852 0.00
#> M229T33_2_POS M229T33_2_POS 229.01499 33.42523 0.00
#> M103T534_POS M103T534_POS 103.00632 534.36121 13445378.54
#> M72T34_POS M72T34_POS 71.92866 33.50882 0.00
#> M210T203_POS M210T203_POS 210.01770 202.93176 0.00
#> M333T526_POS M333T526_POS 333.16565 526.45145 0.00
#> M176T32_POS M176T32_POS 175.98469 32.40417 4443308.21
#> M235T92_POS M235T92_POS 234.98957 92.36749 0.00
#> M181T204_POS M181T204_POS 181.03052 204.29840 800621.26
#> M195T1035_POS M195T1035_POS 194.97909 1035.16997 0.00
#> M362T827_POS M362T827_POS 361.93258 827.00286 114210.45
#> M333T563_POS M333T563_POS 333.17906 563.24689 0.00
#> M293T530_POS M293T530_POS 293.17292 530.19983 0.00
#> M164T210_POS M164T210_POS 164.03058 210.35624 12150849.08
#> M359T539_POS M359T539_POS 359.36947 538.65298 0.00
#> M161T1012_POS M161T1012_POS 161.00040 1011.90338 0.00
#> M326T519_POS M326T519_POS 326.37694 519.15479 0.00
#> M251T509_POS M251T509_POS 251.19720 509.40466 0.00
#> M301T34_1_POS M301T34_1_POS 300.90872 33.61617 0.00
#> M253T830_2_POS M253T830_2_POS 253.02032 829.69879 1922198.26
#> M159T907_POS M159T907_POS 159.00689 907.19733 0.00
#> M172T916_POS M172T916_POS 171.96517 916.47171 0.00
#> mean_intensity.1 qc_blank_ratio
#> M317T1035_POS 1589593.87 Inf
#> M355T511_POS 125532.88 Inf
#> M270T586_POS 175169.64 Inf
#> M297T826_POS 421410.38 Inf
#> M102T310_POS 3706367.91 Inf
#> M288T32_POS 7041231.86 Inf
#> M343T415_POS 104459.94 Inf
#> M340T611_POS 692102.74 Inf
#> M224T34_POS 1249987.39 Inf
#> M325T33_POS 8245924.53 Inf
#> M293T559_POS 1931033.26 Inf
#> M169T33_POS 13239755.94 Inf
#> M233T34_1_POS 2202366.37 Inf
#> M104T1036_POS 682326.04 Inf
#> M259T522_POS 1778856.90 Inf
#> M205T34_2_POS 28369003.40 Inf
#> M178T54_POS 48761266.62 6.002162
#> M193T164_POS 12657903.77 Inf
#> M183T821_POS 258769.55 Inf
#> M217T829_POS 483984.23 Inf
#> M312T436_POS 252062.89 Inf
#> M201T33_POS 14437236.68 Inf
#> M100T38_POS 7100827.97 Inf
#> M154T60_POS 12637719.38 Inf
#> M180T33_POS 15610247.24 Inf
#> M231T531_POS 689831.72 Inf
#> M199T120_POS 4562978.53 Inf
#> M161T1024_1_POS 1715204.92 Inf
#> M265T428_POS 1614838.48 16.619642
#> M248T34_POS 979718.49 Inf
#> M292T34_POS 909575.35 Inf
#> M261T829_1_POS 2055234.90 5.895052
#> M183T34_1_POS 1847383.90 Inf
#> M179T535_POS 13635502.84 Inf
#> M164T1036_POS 424728.36 Inf
#> M351T467_POS 81056.64 Inf
#> M102T112_POS 22054068.30 Inf
#> M178T968_2_POS 21389765.66 Inf
#> M241T822_POS 1724941.73 Inf
#> M192T122_POS 495938567.21 Inf
#> M195T155_POS 4683161.49 Inf
#> M136T909_POS 13447802.47 Inf
#> M249T33_POS 3305877.21 Inf
#> M305T225_POS 60378.24 Inf
#> M167T402_POS 3253105.52 4.543628
#> M323T643_POS 311837.38 Inf
#> M317T1008_POS 3059302.39 7.541613
#> M232T235_POS 3898892.53 12.516140
#> M133T103_POS 62498508.02 Inf
#> M74T146_POS 7315628.52 Inf
#> M106T33_POS 5152620.80 Inf
#> M313T481_POS 1730513.49 Inf
#> M95T1017_POS 2100708.97 Inf
#> M141T80_POS 2227386.88 Inf
#> M153T353_POS 13233809.33 Inf
#> M173T866_POS 7286010.44 Inf
#> M99T89_POS 1981797.35 Inf
#> M323T658_POS 2281229.49 Inf
#> M216T61_POS 29824980.50 Inf
#> M160T33_1_POS 82861899.00 Inf
#> M164T426_POS 118641296.15 3.479128
#> M160T1010_POS 9290642.32 Inf
#> M237T464_POS 92886.66 Inf
#> M314T471_POS 309761.57 Inf
#> M359T481_POS 84515.31 Inf
#> M117T1034_POS 397365097.11 Inf
#> M193T104_POS 125445310.20 Inf
#> M167T461_1_POS 1367153.98 Inf
#> M354T637_1_POS 326502.92 Inf
#> M215T829_1_POS 2204549.53 Inf
#> M211T38_POS 2266744.70 Inf
#> M233T33_POS 8647145.41 Inf
#> M333T36_POS 1216438.57 Inf
#> M173T55_1_POS 17846337.80 Inf
#> M287T828_POS 14286940.76 3.168371
#> M148T705_POS 5255395.10 Inf
#> M173T94_POS 1100997.79 Inf
#> M184T102_POS 1684119.12 Inf
#> M187T91_POS 14633154.61 Inf
#> M158T973_POS 17187569.47 Inf
#> M213T497_POS 24266118.74 Inf
#> M102T925_POS 12252054.39 Inf
#> M361T510_POS 210483.79 Inf
#> M160T33_2_POS 4041697.03 Inf
#> M145T40_1_POS 4448727.42 Inf
#> M201T930_POS 9330131.57 Inf
#> M141T271_POS 3268042.17 Inf
#> M325T520_POS 2754628.69 3.338071
#> M246T221_POS 127934.38 Inf
#> M187T154_POS 9912862.44 Inf
#> M156T165_POS 24097429.22 Inf
#> M183T49_POS 17916278.77 Inf
#> M271T448_POS 1629170.56 Inf
#> M250T481_POS 58187874.06 3.418881
#> M261T522_POS 988088.70 Inf
#> M360T547_POS 440523.13 Inf
#> M192T150_POS 57361084.45 Inf
#> M282T481_POS 39022312.14 3.433687
#> M172T33_POS 75527416.31 Inf
#> M359T33_POS 8317512.63 Inf
#> M200T37_POS 3104648.18 Inf
#> M252T192_POS 1760546.26 Inf
#> M341T605_2_POS 1376759.16 Inf
#> M195T75_1_POS 8664373.57 Inf
#> M151T546_POS 16226658.36 4.845541
#> M162T33_POS 10428428.93 Inf
#> M200T488_POS 273025.26 Inf
#> M166T737_POS 3890542.47 Inf
#> M185T1004_POS 3630130.16 Inf
#> M153T33_1_POS 35097582.78 Inf
#> M175T104_POS 9755350.21 Inf
#> M220T963_POS 7401092.99 Inf
#> M226T536_POS 372160.83 Inf
#> M273T829_2_POS 404811.79 Inf
#> M126T826_POS 164848.88 Inf
#> M305T34_1_POS 1600901.28 13.494751
#> M197T933_POS 22114522.68 3.759902
#> M95T918_POS 1585287.50 Inf
#> M149T694_POS 5423565.01 Inf
#> M196T179_POS 6956064.34 Inf
#> M301T544_POS 26094367.81 Inf
#> M167T270_POS 2056951.33 Inf
#> M185T961_POS 2101928.86 Inf
#> M229T33_2_POS 2677465.77 Inf
#> M103T534_POS 43250685.25 3.216770
#> M72T34_POS 468329.27 Inf
#> M210T203_POS 29742563.05 Inf
#> M333T526_POS 2922955.53 Inf
#> M176T32_POS 321650414.47 72.389850
#> M235T92_POS 11471894.29 Inf
#> M181T204_POS 58478294.50 73.041146
#> M195T1035_POS 2482562.36 Inf
#> M362T827_POS 448563.95 3.927521
#> M333T563_POS 37968.16 Inf
#> M293T530_POS 2111122.64 Inf
#> M164T210_POS 48792496.72 4.015563
#> M359T539_POS 253846.58 Inf
#> M161T1012_POS 776518.02 Inf
#> M326T519_POS 490420.09 Inf
#> M251T509_POS 1408886.26 Inf
#> M301T34_1_POS 603205.06 Inf
#> M253T830_2_POS 8362385.71 4.350428
#> M159T907_POS 8756452.51 Inf
#> M172T916_POS 2360169.30 Inf
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate median intensity according to all the samples
object2 =
mutate_median_intensity(object = object, na.rm = TRUE)
object2
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 4 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 4 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_median_intensity ----------
#> Package Function.used Time
#> 1 massdataset mutate_median_intensity() 2023-10-01 14:00:40
head(extract_variable_info(object))
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
head(extract_variable_info(object2))
#> variable_id mz rt median_intensity
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 1676180.4
#> M79T35_POS M79T35_POS 79.05394 35.36550 2777897.0
#> M307T548_POS M307T548_POS 307.14035 547.56641 273687.8
#> M183T224_POS M183T224_POS 183.06209 224.32777 5103244.9
#> M349T47_POS M349T47_POS 349.01584 47.00262 7169041.1
#> M182T828_POS M182T828_POS 181.99775 828.35712 4433034.2
##calculate median intensity according to only QC samples
object3 =
mutate_median_intensity(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_median_intensity ----------
#> Package Function.used Time
#> 1 massdataset mutate_median_intensity() 2023-10-01 14:00:40.764494
#> 2 massdataset mutate_median_intensity() 2023-10-01 14:00:40.80526
head(extract_variable_info(object3))
#> variable_id mz rt median_intensity
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 1676180.4
#> M79T35_POS M79T35_POS 79.05394 35.36550 2777897.0
#> M307T548_POS M307T548_POS 307.14035 547.56641 273687.8
#> M183T224_POS M183T224_POS 183.06209 224.32777 5103244.9
#> M349T47_POS M349T47_POS 349.01584 47.00262 7169041.1
#> M182T828_POS M182T828_POS 181.99775 828.35712 4433034.2
#> median_intensity.1
#> M136T55_2_POS 1447844.3
#> M79T35_POS 2063212.8
#> M307T548_POS 342037.7
#> M183T224_POS NA
#> M349T47_POS 6417851.6
#> M182T828_POS 3662819.1
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate RSDs according to all the samples
object =
mutate_rsd(object = object)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 4 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 4 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_rsd ----------
#> Package Function.used Time
#> 1 massdataset mutate_rsd() 2023-10-01 14:00:40
head(extract_variable_info(object))
#> variable_id mz rt rsd
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 50.756560
#> M79T35_POS M79T35_POS 79.05394 35.36550 28.257007
#> M307T548_POS M307T548_POS 307.14035 547.56641 35.041286
#> M183T224_POS M183T224_POS 183.06209 224.32777 1.224228
#> M349T47_POS M349T47_POS 349.01584 47.00262 27.715030
#> M182T828_POS M182T828_POS 181.99775 828.35712 25.534063
##calculate RSDs according to only QC samples
object =
mutate_rsd(object = object,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_rsd ----------
#> Package Function.used Time
#> 1 massdataset mutate_rsd() 2023-10-01 14:00:40.846596
#> 2 massdataset mutate_rsd() 2023-10-01 14:00:40.872842
head(extract_variable_info(object))
#> variable_id mz rt rsd rsd.1
#> M136T55_2_POS M136T55_2_POS 136.06140 54.97902 50.756560 40.05551
#> M79T35_POS M79T35_POS 79.05394 35.36550 28.257007 51.97966
#> M307T548_POS M307T548_POS 307.14035 547.56641 35.041286 28.26044
#> M183T224_POS M183T224_POS 183.06209 224.32777 1.224228 NA
#> M349T47_POS M349T47_POS 349.01584 47.00262 27.715030 50.95194
#> M182T828_POS M182T828_POS 181.99775 828.35712 25.534063 NA
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate NA number according to all the variables
object2 =
mutate_sample_na_number(object = object)
colnames(extract_sample_info(object))
#> [1] "sample_id" "injection.order" "class" "group"
colnames(extract_sample_info(object2))
#> [1] "sample_id" "injection.order" "class" "group"
#> [5] "na_number"
object2@sample_info_note
#> name meaning
#> 1 sample_id sample_id
#> 2 injection.order injection.order
#> 3 class class
#> 4 group group
#> 5 na_number na_number
##calculate NA number according to only variables with mz > 100
variable_id =
object2 %>%
activate_mass_dataset(what = "variable_info") %>%
filter(mz > 100) %>%
pull(variable_id)
object3 =
mutate_sample_na_number(object = object2,
according_to_variables = variable_id)
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 6 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_sample_na_number ----------
#> Package Function.used Time
#> 1 massdataset mutate_sample_na_number() 2023-10-01 14:00:40.901413
#> 2 massdataset mutate_sample_na_number() 2023-10-01 14:00:40.914953
head(extract_sample_info(object3))
#> sample_id injection.order class group na_number na_number.1
#> 1 Blank_3 1 Blank Blank 682 667
#> 2 Blank_4 2 Blank Blank 702 687
#> 3 QC_1 3 QC QC 397 385
#> 4 QC_2 4 QC QC 381 372
#> 5 PS4P1 5 Subject Subject 424 411
#> 6 PS4P2 6 Subject Subject 427 414
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate NA frequency according to all the variables
object2 =
mutate_sample_na_freq(object = object)
head(extract_sample_info(object))
#> sample_id injection.order class group
#> 1 Blank_3 1 Blank Blank
#> 2 Blank_4 2 Blank Blank
#> 3 QC_1 3 QC QC
#> 4 QC_2 4 QC QC
#> 5 PS4P1 5 Subject Subject
#> 6 PS4P2 6 Subject Subject
head(extract_sample_info(object2))
#> sample_id injection.order class group na_freq
#> 1 Blank_3 1 Blank Blank 0.682
#> 2 Blank_4 2 Blank Blank 0.702
#> 3 QC_1 3 QC QC 0.397
#> 4 QC_2 4 QC QC 0.381
#> 5 PS4P1 5 Subject Subject 0.424
#> 6 PS4P2 6 Subject Subject 0.427
##calculate NA frequency according to only variables with mz > 100
variable_id =
object2 %>%
activate_mass_dataset(what = "variable_info") %>%
filter(mz > 100) %>%
pull(variable_id)
object3 =
mutate_sample_na_freq(object = object2,
according_to_variables = variable_id)
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 6 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_sample_na_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_sample_na_freq() 2023-10-01 14:00:40.942775
#> 2 massdataset mutate_sample_na_freq() 2023-10-01 14:00:40.954041
head(extract_sample_info(object3))
#> sample_id injection.order class group na_freq na_freq.1
#> 1 Blank_3 1 Blank Blank 0.682 0.6834016
#> 2 Blank_4 2 Blank Blank 0.702 0.7038934
#> 3 QC_1 3 QC QC 0.397 0.3944672
#> 4 QC_2 4 QC QC 0.381 0.3811475
#> 5 PS4P1 5 Subject Subject 0.424 0.4211066
#> 6 PS4P2 6 Subject Subject 0.427 0.4241803
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
##calculate NA number according to all the variables
object2 =
mutate_sample_zero_number(object = object)
colnames(extract_sample_info(object))
#> [1] "sample_id" "injection.order" "class" "group"
colnames(extract_sample_info(object2))
#> [1] "sample_id" "injection.order" "class" "group"
#> [5] "zero_number"
object2@sample_info_note
#> name meaning
#> 1 sample_id sample_id
#> 2 injection.order injection.order
#> 3 class class
#> 4 group group
#> 5 zero_number zero_number
##calculate NA number according to only variables with mz > 100
variable_id =
object2 %>%
activate_mass_dataset(what = "variable_info") %>%
filter(mz > 100) %>%
pull(variable_id)
object3 =
mutate_sample_zero_number(object = object2,
according_to_variables = variable_id)
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 6 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:40
#> mutate_sample_zero_number ----------
#> Package Function.used Time
#> 1 massdataset mutate_sample_zero_number() 2023-10-01 14:00:40.983062
#> 2 massdataset mutate_sample_zero_number() 2023-10-01 14:00:40.994261
head(extract_sample_info(object3))
#> sample_id injection.order class group zero_number zero_number.1
#> 1 Blank_3 1 Blank Blank NA NA
#> 2 Blank_4 2 Blank Blank NA NA
#> 3 QC_1 3 QC QC NA NA
#> 4 QC_2 4 QC QC NA NA
#> 5 PS4P1 5 Subject Subject NA NA
#> 6 PS4P2 6 Subject Subject NA NA
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
##calculate NA frequency according to all the variables
object2 =
mutate_sample_zero_freq(object = object)
head(extract_sample_info(object))
#> sample_id injection.order class group
#> 1 Blank_3 1 Blank Blank
#> 2 Blank_4 2 Blank Blank
#> 3 QC_1 3 QC QC
#> 4 QC_2 4 QC QC
#> 5 PS4P1 5 Subject Subject
#> 6 PS4P2 6 Subject Subject
head(extract_sample_info(object2))
#> sample_id injection.order class group zero_freq
#> 1 Blank_3 1 Blank Blank NA
#> 2 Blank_4 2 Blank Blank NA
#> 3 QC_1 3 QC QC NA
#> 4 QC_2 4 QC QC NA
#> 5 PS4P1 5 Subject Subject NA
#> 6 PS4P2 6 Subject Subject NA
##calculate NA frequency according to only variables with mz > 100
variable_id =
object2 %>%
activate_mass_dataset(what = "variable_info") %>%
filter(mz > 100) %>%
pull(variable_id)
object3 =
mutate_sample_zero_freq(object = object2,
according_to_variables = variable_id)
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 6 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
#> mutate_sample_zero_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_sample_zero_freq() 2023-10-01 14:00:41.021459
#> 2 massdataset mutate_sample_zero_freq() 2023-10-01 14:00:41.031262
head(extract_sample_info(object3))
#> sample_id injection.order class group zero_freq zero_freq.1
#> 1 Blank_3 1 Blank Blank NA NA
#> 2 Blank_4 2 Blank Blank NA NA
#> 3 QC_1 3 QC QC NA NA
#> 4 QC_2 4 QC QC NA NA
#> 5 PS4P1 5 Subject Subject NA NA
#> 6 PS4P2 6 Subject Subject NA NA
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
##calculate NA number according to all the samples
object2 =
mutate_variable_na_number(object = object)
colnames(extract_variable_info(object))
#> [1] "variable_id" "mz" "rt"
colnames(extract_variable_info(object2))
#> [1] "variable_id" "mz" "rt" "na_number"
object2@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 na_number na_number
##calculate NA number according to only QC samples
object3 <-
mutate_variable_na_number(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
#> mutate_variable_na_number ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_number() 2023-10-01 14:00:41.056656
#> 2 massdataset mutate_variable_na_number() 2023-10-01 14:00:41.064771
colnames(extract_variable_info(object3))
#> [1] "variable_id" "mz" "rt" "na_number" "na_number.1"
object3@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 na_number na_number
#> 5 na_number.1 na_number.1
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
##calculate NA frequency according to all the samples
object2 =
mutate_variable_na_freq(object = object)
head(extract_variable_info(object))
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
head(extract_variable_info(object2))
#> variable_id mz rt na_freq
#> 1 M136T55_2_POS 136.06140 54.97902 0.250
#> 2 M79T35_POS 79.05394 35.36550 0.250
#> 3 M307T548_POS 307.14035 547.56641 0.375
#> 4 M183T224_POS 183.06209 224.32777 0.750
#> 5 M349T47_POS 349.01584 47.00262 0.250
#> 6 M182T828_POS 181.99775 828.35712 0.125
##calculate NA number according to only QC samples
object3 =
mutate_variable_na_freq(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
#> mutate_variable_na_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_freq() 2023-10-01 14:00:41.090454
#> 2 massdataset mutate_variable_na_freq() 2023-10-01 14:00:41.098247
head(extract_variable_info(object3))
#> variable_id mz rt na_freq na_freq.1
#> 1 M136T55_2_POS 136.06140 54.97902 0.250 0.0
#> 2 M79T35_POS 79.05394 35.36550 0.250 0.0
#> 3 M307T548_POS 307.14035 547.56641 0.375 0.0
#> 4 M183T224_POS 183.06209 224.32777 0.750 1.0
#> 5 M349T47_POS 349.01584 47.00262 0.250 0.0
#> 6 M182T828_POS 181.99775 828.35712 0.125 0.5
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
##calculate NA number according to all the samples
object2 =
mutate_variable_zero_number(object = object)
colnames(extract_variable_info(object))
#> [1] "variable_id" "mz" "rt"
colnames(extract_variable_info(object2))
#> [1] "variable_id" "mz" "rt" "zero_number"
object2@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 zero_number zero_number
##calculate NA number according to only QC samples
object3 <-
mutate_variable_zero_number(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
#> mutate_variable_zero_number ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_zero_number() 2023-10-01 14:00:41.123918
#> 2 massdataset mutate_variable_zero_number() 2023-10-01 14:00:41.133321
colnames(extract_variable_info(object3))
#> [1] "variable_id" "mz" "rt" "zero_number"
#> [5] "zero_number.1"
object3@variable_info_note
#> name meaning
#> 1 variable_id variable_id
#> 2 mz mz
#> 3 rt rt
#> 4 zero_number zero_number
#> 5 zero_number.1 zero_number.1
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
)
object
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
##calculate NA frequency according to all the samples
object2 =
mutate_variable_zero_freq(object = object)
head(extract_variable_info(object))
#> variable_id mz rt
#> 1 M136T55_2_POS 136.06140 54.97902
#> 2 M79T35_POS 79.05394 35.36550
#> 3 M307T548_POS 307.14035 547.56641
#> 4 M183T224_POS 183.06209 224.32777
#> 5 M349T47_POS 349.01584 47.00262
#> 6 M182T828_POS 181.99775 828.35712
head(extract_variable_info(object2))
#> variable_id mz rt zero_freq
#> 1 M136T55_2_POS 136.06140 54.97902 NA
#> 2 M79T35_POS 79.05394 35.36550 NA
#> 3 M307T548_POS 307.14035 547.56641 NA
#> 4 M183T224_POS 183.06209 224.32777 NA
#> 5 M349T47_POS 349.01584 47.00262 NA
#> 6 M182T828_POS 181.99775 828.35712 NA
##calculate NA number according to only QC samples
object3 =
mutate_variable_zero_freq(object = object2,
according_to_samples =
get_sample_id(object)[extract_sample_info(object)$class == "QC"])
object3
#> --------------------
#> massdataset version: 1.0.27
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 1000 x 5 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 5 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2023-10-01 14:00:41
#> mutate_variable_zero_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_zero_freq() 2023-10-01 14:00:41.169417
#> 2 massdataset mutate_variable_zero_freq() 2023-10-01 14:00:41.180255
head(extract_variable_info(object3))
#> variable_id mz rt zero_freq zero_freq.1
#> 1 M136T55_2_POS 136.06140 54.97902 NA 0
#> 2 M79T35_POS 79.05394 35.36550 NA 0
#> 3 M307T548_POS 307.14035 547.56641 NA 0
#> 4 M183T224_POS 183.06209 224.32777 NA NA
#> 5 M349T47_POS 349.01584 47.00262 NA 0
#> 6 M182T828_POS 181.99775 828.35712 NA NA