| Type: | Package |
| Title: | Identify Kinase Substrate Relationships Using Dynamic Data |
| Version: | 1.0 |
| Date: | 2015-10-13 |
| Author: | Westa Domanova |
| Maintainer: | Westa Domanova <w.domanova@gmail.com> |
| Description: | Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates. |
| License: | GPL-2 | GPL-3 |
| Depends: | R (≥ 3.0.0) |
| Imports: | tightClust, stats |
| RoxygenNote: | 4.1.1.9001 |
| NeedsCompilation: | no |
| Packaged: | 2015-10-18 23:57:01 UTC; westa |
| Repository: | CRAN |
| Date/Publication: | 2015-10-19 08:41:56 |
Identify site specific kinase substrate relationships using dynamic data.
Description
Using this package you can combine known site specific kinase substrate relationships with dynamic experimental data and determine active kinases and their substrates.
Author(s)
Westa Domanova
Create a kinase substrate relationship list from a data frame
Description
KSR.list returns a list of kinase substrate relationships
Usage
KSR.list(df, kinasefamilies = NULL, exclusive = FALSE)
Arguments
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) |
Details
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.
Value
named list of substrate identifiers, with the corresponding kinase identifiers as the list names
Examples
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)
Find clusters containing core substrates
Description
clust.expand returns a list of kinase substrate relationships
Usage
clust.expand(clust, clust_all, diff = NULL)
Arguments
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 |
Details
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.
Value
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers
Examples
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)
Return clustering assignments produced by tight.clust
Description
clustering returns vectors of clustering assignments
Usage
clustering(tightclust, data)
Arguments
tightclust |
list of objects returned by the tight.clust function |
data |
data frame of time course of substrates, each substrate is a row |
Details
The function clustering creates a named list of cluster assignments for substrates.
Value
named list containing named vectors of cluster assignments, names correspond to rownames in data and names of list are kinase identifiers
Examples
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)
Time course data for phosphorylation sites
Description
This dataset contains time course data of phosphorylation sites after insulin stimulation.
Usage
data_kin
Format
'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" ...
Source
Humphrey et al., Cell Metabolism, 2013
site specific kinase substrate relationship dataset
Description
This dataset contains all site specific kinase relationships from PhosphoSitePlus, PhosphoElm, HPRD and PhosphoPoint.
Usage
phosphonetwork_df
Format
'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" ...
Create random data
Description
random.data returns a data frame of random numeric values
Usage
random.data(data, back_data = NULL, n = 50, random.seed = NULL)
Arguments
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 |
Details
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.
Value
data frame of random numeric values with n-nrow(data) rows and same number of columns as input data
Examples
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))})