| Title: | Identify Kinase Substrate Relationships Using Dynamic Data |
|---|---|
| Description: | Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates. |
| Authors: | Westa Domanova |
| Maintainer: | Westa Domanova <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 1.0 |
| Built: | 2024-10-31 06:43:47 UTC |
| Source: | CRAN |
clust.expand returns a list of kinase substrate relationships
clust.expand(clust, clust_all, diff = NULL)clust.expand(clust, clust_all, diff = NULL)
clust |
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers (result of clustering performed using exclusive substrates) |
clust_all |
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers (result of clustering performed using all substrates) |
diff |
character vector of substrate identifiers that are differentially regulated |
The function clust.expand takes the resulting core substrates from the exclusive clustering and finds the corresponding substrate clusters in the clustering using all substrates.
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers
data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight <- lapply(substrate_profiles_random, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust<- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight, substrate_profiles, SIMPLIFY = FALSE) # do clustering using all available substrates kin_data_fam <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam) substrate_profiles_all <- lapply(kin_data_fam[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random_all <- lapply(substrate_profiles_all, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight_all <- lapply(substrate_profiles_random_all, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust_all <- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight_all, substrate_profiles_all, SIMPLIFY = FALSE) expand_all <- mapply(function(x,y){clust.expand(x, y)}, kin_clust, kin_clust_all, SIMPLIFY = FALSE)data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight <- lapply(substrate_profiles_random, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust<- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight, substrate_profiles, SIMPLIFY = FALSE) # do clustering using all available substrates kin_data_fam <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam) substrate_profiles_all <- lapply(kin_data_fam[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random_all <- lapply(substrate_profiles_all, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight_all <- lapply(substrate_profiles_random_all, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust_all <- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight_all, substrate_profiles_all, SIMPLIFY = FALSE) expand_all <- mapply(function(x,y){clust.expand(x, y)}, kin_clust, kin_clust_all, SIMPLIFY = FALSE)
clustering returns vectors of clustering assignments
clustering(tightclust, data)clustering(tightclust, data)
tightclust |
list of objects returned by the tight.clust function |
data |
data frame of time course of substrates, each substrate is a row |
The function clustering creates a named list of cluster assignments for substrates.
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers
data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight <- lapply(substrate_profiles_random, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust<- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight, substrate_profiles, SIMPLIFY = FALSE)data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))}) target <- 3 substrate_profiles_tight <- lapply(substrate_profiles_random, function(x){ tightClust::tight.clust(x, target = target, k.min = 7, resamp.num = 100, random.seed = 12345) }) kin_clust<- mapply(function(x,y){clustering(x, y)}, substrate_profiles_tight, substrate_profiles, SIMPLIFY = FALSE)
This dataset contains time course data of phosphorylation sites after insulin stimulation.
data_kindata_kin
'data.frame': 84 obs. of 10 variables: $ 0_scaled : num 0.4481 0 0 0.1618 0.0909 ... $ 15s_scaled : num 0.224 0.517 0.357 0 0 ... $ 30s_scaled : num 0.266 0.655 0.636 0.785 0.136 ... $ 1min_scaled : num 0.0332 1 0.8149 0.7188 0.0909 ... $ 2min_scaled : num 0 0.918 0.756 0.912 0 ... $ 5min_scaled : num 0.6017 0.6897 0.8571 0.9523 0.0455 ... $ 10min_scaled: num 0.759 0.74 0.964 0.79 1 ... $ 20min_scaled: num 1 0.483 0.974 1 0.5 ... $ 60min_scaled: num 0.598 0.724 1 0.78 0.545 ... $ SUB_IDENT : chr "O43521_FIFMRRSSLLSRSSS" "O60343_QFRRRAHTFSHPPSS" "O60825_IRRPRNYSVGSRPLK" "O60825_PVRMRRNSFTPLSSS" ...
Humphrey et al., Cell Metabolism, 2013
KSR.list returns a list of kinase substrate relationships
KSR.list(df, kinasefamilies = NULL, exclusive = FALSE)KSR.list(df, kinasefamilies = NULL, exclusive = FALSE)
df |
data frame of kinase substrate relationships with substrate identifier in the first column and kinase identifier in the second column. |
kinasefamilies |
named list of kinase identifiers that have to be combined, one list per kinase family, list will be named after first family member |
exclusive |
logical, if TRUE only substrates exclusive to the kinase will be included in the list (substrates with multiple kinases will be excluded) |
The function KSR.list creates a list of kinase substrate relationships from a data frame and can combine kinase families into one list. Substrates occuring in multiple lists can be excluded.
named list of substrate identifiers, with the corresponding kinase identifiers as the list names
data(phosphonetworkdf) data(datakin) # first column has to be substrate id, second kinase id kin_data <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")]) # Akt1 and Akt2 belong to the same kinase family, combine their substrates # into one list and name the list after the first family member fam <- list(akt = c("P31749", "P31751")) kin_data_fam <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam) # only include phosphosites appearing once kin_data_fam_exc <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE)data(phosphonetworkdf) data(datakin) # first column has to be substrate id, second kinase id kin_data <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")]) # Akt1 and Akt2 belong to the same kinase family, combine their substrates # into one list and name the list after the first family member fam <- list(akt = c("P31749", "P31751")) kin_data_fam <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam) # only include phosphosites appearing once kin_data_fam_exc <- KSR.list(phosphonetwork_df[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE)
Using this package you can combine known site specific kinase substrate relationships with dynamic experimental data and determine active kinases and their substrates.
Westa Domanova
This dataset contains all site specific kinase relationships from PhosphoSitePlus, PhosphoElm, HPRD and PhosphoPoint.
phosphonetwork_dfphosphonetwork_df
'data.frame': 13505 obs. of 34 variables: $ SUB_ACC_ID : chr "A1KXE4" "A1X283" "A2A9C3" "A2APB8" ... $ MODSITE_SEQ : chr "QTGYTPGTPYKVSCS" "DMSASAGYEEISDPD" "TPGSLVGSPREASGM" "KIARDPQTPILQTKY" ... $ KIN_ACC_ID : chr "P24941" "P12931" "Q9JLN9" "P63085" ... $ ORG : Factor w/ 17 levels "chicken","cow",..: 8 8 10 10 10 8 8 10 8 8 ... $ KINASE : chr "CDK2" "SRC" "MTOR" "ERK2" ... $ KIN_GENE_SYMB : chr "CDK2" "SRC" "MTOR" "MAPK1" ... $ HU_CHR_LOC : Factor w/ 274 levels "","10p11.1","10p11.23",..: 27 132 1 1 1 7 215 1 173 13 ... $ SUBSTRATE : chr "FAM168B" "SH3PXD2B" "SZT2" "TPX2" ... $ SUB_GENE_ID : chr "130074" "285590" "230676" "72119" ... $ SUB_GENE_SYMB : chr "FAM168B" "SH3PXD2B" "Szt2" "Tpx2" ... $ SUB_MOD_RSD : chr "T57" "Y508" "S3230" "T369" ... $ SITE_GRP_ID : int 9831677 17303901 14575118 455432 3202029 3963101 975498 468668 451197 454238 ... $ IN_VIVO_RXN : Factor w/ 2 levels " ","X": 1 2 2 1 1 1 2 1 2 2 ... $ IN_VITRO_RXN : Factor w/ 2 levels " ","X": 2 1 1 2 2 2 1 2 2 2 ... $ CST_CAT. : Factor w/ 563 levels "","11817","11834",..: 1 1 1 1 1 1 1 1 1 1 ... $ PhosphositePLUS : num 1 1 1 1 1 1 1 1 1 1 ... $ SEQ : chr "MNPVYSPGSSGVPYANAKGIGYPAGFPMGYAAAAPAYSPNMYPGANPTFQTGYTPGTPYKVSCSPTSGAVPPYSSSPNPYQTAVYPVRSAYPQQSPYAQQGTYYTQPLYAAPPHVIHHTTVVQPNGM"| __truncated__ "MPPRRSIVEVKVLDVQKRRVPNKHYVYIIRVTWSSGSTEAIYRRYSKFFDLQMQMLDKFPMEGGQKDPKQRIIPFLPGKILFRRSHIRDVAVKRLIPIDEYCKALIQLPPYISQCDEVLQFFETRPE"| __truncated__ "MASERPEPEVEEAGQVFLLMKKDYRISRNVRLAWFLNHLHQTVQATPQELLLQSEQELEVLSVLPPGWQPDEPVVPRPFLLVPSTRVTFLAWQYRFVIELDLSPSTGIVDDSTGEILFDEVFHALSR"| __truncated__ "MSQVPTTYSFDAPTDFINFSSLDAEEDTENIDSWFDEKANLENKFLRQRGIGEPFQGKNSLRKAKLQQGFVTPLKAVDNTYHKETEKENLQKQSIPSNDCSSLDAKRAVSGNTPVQPQRRSIRLSAQ"| __truncated__ ... $ PhosphoELM : num NA NA NA NA NA NA NA NA NA NA ... $ SwissProt : chr NA NA NA NA ... $ PubMed : Factor w/ 2842 levels "",";","10023679;",..: NA NA NA NA NA NA NA NA NA NA ... $ KIN_GENE_ID : chr NA NA NA NA ... $ HPRD : num NA NA NA NA NA NA NA NA NA NA ... $ PhosphoPoint : num NA NA NA NA NA NA NA NA NA NA ... $ SUB_HPRD_ID : int NA NA NA NA NA NA NA NA NA NA ... $ SUB_HPRDISO_ID : Factor w/ 13183 levels "00001_1","00002_1",..: NA NA NA NA NA NA NA NA NA NA ... $ KIN_HPRD_ID : Factor w/ 517 levels "-","00021","00084",..: NA NA NA NA NA NA NA NA NA NA ... $ SUB_ACC_ID.human : chr "A1KXE4" "A1X283" "Q5T011" "Q9ULW0" ... $ Position : chr "57" "508" "3230" "369" ... $ MODSITE_SEQ.human : chr "QTGYTPGTPYKVSCS" "DMSASAGYEEISDPD" "APGSSAGSPGEASGL" "KICRDPQTPVLQTKH" ... $ MODSITE_SEQ.mouse : chr "QTGYTPGTPYKVSCS" "DLSASTGYEEISDPT" "TPGSLVGSPREASGM" "KIARDPQTPILQTKY" ... $ SUB_ACC_ID.mouse : chr "Q80XQ8" "A2AAY5" "A2A9C3" "A2APB8" ... $ KIN_ACC_ID.human : chr "P24941" "P12931" "P42345" "P28482" ... $ KIN_GENE_SYMB.human: chr "CDK2" "SRC" "MTOR" "MAPK1" ... $ SUB_IDENT : chr "A1KXE4_QTGYTPGTPYKVSCS" "A1X283_DMSASAGYEEISDPD" "Q5T011_APGSSAGSPGEASGL" "Q9ULW0_KICRDPQTPVLQTKH" ...
random.data returns a data frame of random numeric values
random.data(data, back_data = NULL, n = 50, random.seed = NULL)random.data(data, back_data = NULL, n = 50, random.seed = NULL)
data |
data frame of time course of substrates, each substrate is a row |
back_data |
data frame of numeric values that can to be used as background data, if not provided a values are drawn from a uniform distribution between minimum and maximum of input data |
n |
numeric specifying how many rows should be contained in the resulting data frame |
random.seed |
numeric used as seed |
The function random.data returns a data frame of random numeric values with the same number of columns as the input data and with n-nrow(data) rows. By default the values are drawn from a uniform distribution of values between the minimum and the maximum of the input data. Values can be drawn from background data instead if included.
data frame of random numeric values with n-nrow(data) rows and same number of columns as input data
data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))})data(phosphonetworkdf) data(datakin) # only need what is present in data phosphonetwork_data <- phosphonetwork_df[ phosphonetwork_df[,"SUB_IDENT"] %in% data_kin[,"SUB_IDENT"] ,] fam <- list(akt = c("P31749", "P31751")) kin_data_fam_exc <- KSR.list(phosphonetwork_data[, c("SUB_IDENT", "KIN_ACC_ID")], kinasefamilies = fam, exclusive = TRUE) # only do for Akt and Mtor (P31749, P42345) substrate_profiles <- lapply(kin_data_fam_exc[c("P31749", "P42345")], function(x){data_kin[match(x, data_kin[,"SUB_IDENT"]),1:9]}) substrate_profiles_random <- lapply(substrate_profiles, function(x){rbind(x, random.data(x, random.seed = 123))})