R/ProteinQuantification.R
In rdshankar/RImmPort: RImmPort: Enabling Ready-for-analysis Immunology Research Data
Defines functions
getProteinQuantification
getCountOfProteinQuantification
#' Protein Quantification Domain
#'
#' @name Protein Quantification Domain
#' @description The ImmPort study data generated from assays of types: ELISA and MBAA are grouped into the
#' Cellular Quantification Domain. The data is reformated to a custom Protein Quantification domain model in
#' CDISC SDTM standards, and is a list of 2 data frames containing 1) Protein Quantification data \code{\link{ZA}}
#' and 2) any supplemental Protein Quantification data \code{\link{SUPP}}
NULL
#> NULL
# call to globalVariables to prevent from generating NOTE: no visible binding for global variable <variable name>
# this hack is to satisfy CRAN (http://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when)
globalVariables(c("subject_id", "result_id", "experiment_title", "assay_purpose", "measurement_technique",
"analyte", "value", "unit", "specimen_type",
"specimen_subtype", "specimen_treatment",
"treatment_amount_value", "treatment_amount_unit",
"treatment_duration_value", "treatment_duration_unit",
"treatment_temperature_value", "treatment_temperature_unit",
"visit_name", "visit_min_start_day", "visit_max_start_day", "visit_order",
"elapsed_time_of_specimen_collection", "time_point_reference",
"biosample_accession", "file_name", "concentration_value", "concentration_unit",
"mfi", "mfi_coordinate", "QNAM", "QVAL", "ZAMFI", "ZAMFICRD", "ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU"))
# Get Protein Quantification data of a specific study
#
# The function \code{getProteinQuantification} queries the ImmPort database for Protein Quantification data and
# reformats it to a custom Protein Quantification domain model in CDISC SDTM standards.
#
# @param data_src A data_srcection handle to ImmPort (MySQL or SQLite) database instance or
# a directory handle to folder where study RDS files are located
# @param study_id Identifier of a specific study
# @param assay_type Assay Type **optional**
# @return a list of 2 data frames containing 1) Protein Quantification data \code{\link{ZA}} and 2) any supplemental
# Protein Quantification data \code{\link{SUPPZA}}
# @examples
# \dontrun{
# # get all of study SDY1's Protein Quantification data
# pq_l <- getProteinQuantification(data_src, "SDY1")
#
# # get study SDY1's Protein Quantification data that was generated using ELISA assay
# pq_l <- getProteinQuantification(data_src, "SDY1", "ELISA")
# }
#' @importFrom dplyr %>% select
#' @importFrom reshape2 melt
#' @importFrom plyr rename
getProteinQuantification <- function(data_src, study_id, assay_type="ALL") {
cat("loading Protein Quantification data....")
pq_cols <- c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ", "ZATEST", "ZACAT", "ZAMETHOD", "ZAANALYT", "ZAORRES",
"ZAORRESU", "ZASPEC", "VISITNUM", "VISIT", "ZAELTM", "ZATPTREF", "ZAREFID", "ZAXFN")
supppq_cols <- c("STUDYID", "RDOMAIN", "USUBJID", "IDVAR", "IDVARVAL", "QNAM", "QLABEL", "QVAL")
pq_df = data.frame()
supppq_df = data.frame()
if ((class(data_src)[1] == 'MySQLConnection') ||
(class(data_src)[1] == 'SQLiteConnection')) {
if ((assay_type == "ALL") || (assay_type =="ELISA")) {
# get ELISA results
measurement_types <- list("Protein_Quantification", "Cytokine_Quantification")
els_df <- getElisaResults(data_src, study_id, "")
if (nrow(els_df) > 0) {
els_df <- select(els_df, STUDYID = study_id, USUBJID = subject_id, ZASEQ = sequence, ZATEST = experiment_title,
ZACAT = assay_purpose, ZAMETHOD = measurement_technique, ZAANALYT = analyte, ZAORRES = value, ZAORRESU = unit,
ZASPEC = specimen_type, ZASPECSB = specimen_subtype,
ZASPTRT = specimen_treatment,
ZATRTAMV = treatment_amount_value, ZATRTAMU = treatment_amount_unit,
ZATRTDUV = treatment_duration_value, ZATRTDUU = treatment_duration_unit,
ZATRTTMV = treatment_temperature_value, ZATRTTMU = treatment_temperature_unit,
VISIT = visit_name, VISITNUM = visit_order, VISITMIN = visit_min_start_day, VISITMAX = visit_max_start_day,
ZAELTM = elapsed_time_of_specimen_collection, ZATPTREF = time_point_reference,
ZAREFID = experiment_sample_accession, ZAREFIDP = biosample_accession, ZAXFN = file_name)
els_df$DOMAIN <- "ZA"
qnam_values = c("ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU")
qlabel_values= c("Specimen Subtype", "Source Specimen Identifier",
"Planned Visit Minimum Start Day", "Planned Visit Maximum Start Day",
"Specimen Treatment",
"Specimen Treatment Amount Value", "Specimen Treatment Amount Unit",
"Specimen Treatment Duration Value", "Specimen Treatment Duration Unit",
"Specimen Treatment Temperature Value", "Specimen Treatment Temperature Unit")
supp_els_df <- melt(els_df,
id = c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ"),
measure = qnam_values,
variable.name = "QNAM",
value.name = "QVAL")
supp_els_df <- transform(supp_els_df, QLABEL = unlist(qlabel_values[QNAM]))
supp_els_df <- plyr::rename(supp_els_df, c("DOMAIN" = "RDOMAIN", "ZASEQ" = "IDVARVAL"))
supp_els_df$IDVAR <- "ZASEQ"
supp_els_df <- supp_els_df[supppq_cols]
# remove rows that have empty QVAL values
supp_els_df <- subset(supp_els_df,QVAL!="")
els_df <- subset(els_df, select = -c(ZASPECSB, ZAREFIDP, ZASPTRT,
VISITMIN, VISITMAX,
ZATRTAMV, ZATRTAMU,
ZATRTDUV, ZATRTDUU,
ZATRTTMV, ZATRTTMU))
els_df <- els_df[, pq_cols]
pq_df <- rbind(pq_df, els_df)
supppq_df <- rbind(supppq_df, supp_els_df)
}
}
if ((assay_type == "ALL") || (assay_type =="MBAA")) {
# get MBAA results
# mbaa_column_names <- c('study_id', 'subject_id', 'result_id', 'analyte', 'concentration_unit',
# 'concentration_value', 'mfi', 'mfi_coordinate', 'experiment_title', 'assay_purpose', 'measurement_technique',
# 'biosample_accession', 'specimen_type', 'specimen_subtype', 'elapsed_time_of_specimen_collection',
# 'time_point_reference', 'file_name')
measurement_types <- list("Cytokine_Quantification")
mbaa_df <- getMbaaResults(data_src, study_id, "")
if (nrow(mbaa_df) > 0) {
mbaa_df <- mbaa_df %>%
select(STUDYID = study_id, USUBJID = subject_id, ZASEQ = sequence, ZATEST = experiment_title,
ZACAT = assay_purpose, ZAMETHOD = measurement_technique, ZAANALYT = analyte, ZAORRES = concentration_value,
ZAORRESU = concentration_unit, ZASPEC = specimen_type, ZASPECSB = specimen_subtype,
ZASPTRT = specimen_treatment,
ZATRTAMV = treatment_amount_value, ZATRTAMU = treatment_amount_unit,
ZATRTDUV = treatment_duration_value, ZATRTDUU = treatment_duration_unit,
ZATRTTMV = treatment_temperature_value, ZATRTTMU = treatment_temperature_unit,
VISIT = visit_name, VISITNUM = visit_order, VISITMIN = visit_min_start_day, VISITMAX = visit_max_start_day,
ZAELTM = elapsed_time_of_specimen_collection,
ZATPTREF = time_point_reference,
ZAREFID = experiment_sample_accession, ZAREFIDP = biosample_accession,
ZAXFN = file_name, ZAMFI = mfi, ZAMFICRD = mfi_coordinate)
mbaa_df$DOMAIN <- "ZA"
qnam_values = c("ZAMFI", "ZAMFICRD", "ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU")
qlabel_values= c("MFI", "MFI Coordinate", "Specimen Subtype", "Source Specimen Identifier",
"Planned Visit Minimum Start Day", "Planned Visit Maximum Start Day",
"Specimen Treatment",
"Specimen Treatment Amount Value", "Specimen Treatment Amount Unit",
"Specimen Treatment Duration Value", "Specimen Treatment Duration Unit",
"Specimen Treatment Temperature Value", "Specimen Treatment Temperature Unit")
supp_mbaa_df <- melt(mbaa_df,
id = c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ"),
measure = qnam_values,
variable.name = "QNAM",
value.name = "QVAL")
supp_mbaa_df <- transform(supp_mbaa_df, QLABEL = unlist(qlabel_values[QNAM]))
supp_mbaa_df <- plyr::rename(supp_mbaa_df, c("DOMAIN" = "RDOMAIN", "ZASEQ" = "IDVARVAL"))
supp_mbaa_df$IDVAR <- "ZASEQ"
supp_mbaa_df <- supp_mbaa_df[supppq_cols]
# remove rows that have empty QVAL values
supp_mbaa_df <- subset(supp_mbaa_df,QVAL!="")
mbaa_df <- subset(mbaa_df, select = -c(ZAMFI, ZAMFICRD, ZASPECSB, ZAREFIDP,
VISITMIN, VISITMAX,
ZASPTRT,
ZATRTAMV, ZATRTAMU,
ZATRTDUV, ZATRTDUU,
ZATRTTMV, ZATRTTMU))
mbaa_df <- mbaa_df[, pq_cols]
pq_df <- rbind(pq_df, mbaa_df)
supppq_df <- rbind(supppq_df, supp_mbaa_df)
}
}
} else {
l <- loadSerializedStudyData(data_src, study_id, "Protein Quantification")
if (assay_type == "ALL") {
pq_df <- l[[1]]
supppq_df <- l[[2]]
} else {
}
}
cat("done", "\n")
pq_l <- list()
if (nrow(pq_df) > 0)
pq_l <- list(za_df=pq_df, suppza_df=supppq_df)
pq_l
}
# Get count of Protein Quantification data of a specific study
#
# The function \code{getCountOfProteinQuantification} queries the ImmPort database for count
# of Protein Quantification data
#
# @param data_src A connection handle to ImmPort database instance
# @param assay_type Assay Type **optional**
# @param study_id Identifier of a specific study
# @return a count of Protein Quantification data
# @examples
# \dontrun{
# # get count of all of study SDY1's Protein Quantification data
# count <- getCountOfProteinQuantification(data_src, "SDY1")
#
# # get count of study SDY1's Protein Quantification data that was generated using ELISA assay
# count <- getCountOfProteinQuantification(data_src, "SDY1", "ELISA")
# }
getCountOfProteinQuantification <- function(data_src, study_id, assay_type="ALL") {
count <- 0
if ((assay_type == "ALL") || (assay_type =="ELISA"))
count <- count + getCountOfElisaResults(data_src, study_id)
if ((assay_type == "ALL") || (assay_type =="MBAA"))
count <- count + getCountOfMbaaResults(data_src, study_id)
count
}
##' Protein Quantification Domain Variables
##' @name ZA
##' @description {
##' \tabular{ll}{
##' \strong{Variable Name } \tab \strong{Variable Label} \cr
##' STUDYID \tab Study Identifier \cr
##' DOMAIN \tab Domain Abbreviation \cr
##' USUBJID \tab Unique Subject Identifier \cr
##' ZASEQ \tab Sequence Number \cr
##' ZATEST \tab Protein Quantification Test Name \cr
##' ZACAT \tab Category for Protein Quantification \cr
##' ZAMETHOD \tab Measurement Technique \cr
##' ZAANALYT \tab Analyte \cr
##' ZAORRES \tab Result or Finding in Original Units \cr
##' ZAORRESU \tab Original Units \cr
##' ZASPEC \tab Specimen Type \cr
##' VISITNUM \tab Visit Number \cr
##' VISIT \tab Visit Name \cr
##' ZAELTM \tab Planned Elapsed Time from Time Point Ref \cr
##' ZATPTREF \tab Time Point Reference \cr
##' ZAREFID \tab Specimen Identifier \cr
##' ZAXFN \tab Raw Data File or Life Science Identifier
##' }
##' }
NULL
#> NULL
##' Protein Quantification Domain Supplemental Variables
##' @name SUPPZA
##' @description {
##' \tabular{ll}{
##' \strong{Variable Name} \tab \strong{Variable Label} \cr
##' STUDYID \tab Study Identifier \cr
##' RDOMAIN \tab Related Domain Abbreviation \cr
##' USUBJID \tab Unique Subject Identifier \cr
##' IDVAR \tab Identifying Variable \cr
##' IDVARVAL \tab Identifying Variable Value \cr
##' QNAM \tab Qualifier Variable Name \cr
##' QLABEL \tab Qualifier Variable Label \cr
##' QVAL \tab Data Value
##' }
##' }
##' @note The following table enumerates the values in QNAM and QLABEL variables {
##' \tabular{ll}{
##' \strong{QNAM} \tab \strong{QLABEL} \cr
##' ZAMFI \tab MFI \cr
##' ZAMFICRD \tab MFI Coordinate \cr
##' ZASPECSB \tab Specimen Subtype \cr
##' ZAREFIDP \tab Source Specimen Identifier \cr
##' VISITMIN \tab Planned Visit Minimum Start Day \cr
##' VISITMAX \tab Planned Visit Maximum Start Day \cr
##' ZASPTRT \tab Specimen Treatment \cr
##' ZATRTAMV \tab Specimen Treatment Amount Value \cr
##' ZATRTAMU \tab Specimen Treatment Amount Unit \cr
##' ZATRTDUV \tab Specimen Treatment Duration Value \cr
##' ZATRTDUU \tab Specimen Treatment Duration Unit \cr
##' ZATRTTMV \tab Specimen Treatment Temperature Value \cr
##' ZATRTTMU \tab Specimen Treatment Temperature Unit
##' }
##' }
NULL
#> NULL
rdshankar/RImmPort documentation built on May 27, 2019, 3:07 a.m.
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Defines functions getProteinQuantification getCountOfProteinQuantification
#' Protein Quantification Domain
#'
#' @name Protein Quantification Domain
#' @description The ImmPort study data generated from assays of types: ELISA and MBAA are grouped into the
#' Cellular Quantification Domain. The data is reformated to a custom Protein Quantification domain model in
#' CDISC SDTM standards, and is a list of 2 data frames containing 1) Protein Quantification data \code{\link{ZA}}
#' and 2) any supplemental Protein Quantification data \code{\link{SUPP}}
NULL
#> NULL
# call to globalVariables to prevent from generating NOTE: no visible binding for global variable <variable name>
# this hack is to satisfy CRAN (http://stackoverflow.com/questions/9439256/how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when)
globalVariables(c("subject_id", "result_id", "experiment_title", "assay_purpose", "measurement_technique",
"analyte", "value", "unit", "specimen_type",
"specimen_subtype", "specimen_treatment",
"treatment_amount_value", "treatment_amount_unit",
"treatment_duration_value", "treatment_duration_unit",
"treatment_temperature_value", "treatment_temperature_unit",
"visit_name", "visit_min_start_day", "visit_max_start_day", "visit_order",
"elapsed_time_of_specimen_collection", "time_point_reference",
"biosample_accession", "file_name", "concentration_value", "concentration_unit",
"mfi", "mfi_coordinate", "QNAM", "QVAL", "ZAMFI", "ZAMFICRD", "ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU"))
# Get Protein Quantification data of a specific study
#
# The function \code{getProteinQuantification} queries the ImmPort database for Protein Quantification data and
# reformats it to a custom Protein Quantification domain model in CDISC SDTM standards.
#
# @param data_src A data_srcection handle to ImmPort (MySQL or SQLite) database instance or
# a directory handle to folder where study RDS files are located
# @param study_id Identifier of a specific study
# @param assay_type Assay Type **optional**
# @return a list of 2 data frames containing 1) Protein Quantification data \code{\link{ZA}} and 2) any supplemental
# Protein Quantification data \code{\link{SUPPZA}}
# @examples
# \dontrun{
# # get all of study SDY1's Protein Quantification data
# pq_l <- getProteinQuantification(data_src, "SDY1")
#
# # get study SDY1's Protein Quantification data that was generated using ELISA assay
# pq_l <- getProteinQuantification(data_src, "SDY1", "ELISA")
# }
#' @importFrom dplyr %>% select
#' @importFrom reshape2 melt
#' @importFrom plyr rename
getProteinQuantification <- function(data_src, study_id, assay_type="ALL") {
cat("loading Protein Quantification data....")
pq_cols <- c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ", "ZATEST", "ZACAT", "ZAMETHOD", "ZAANALYT", "ZAORRES",
"ZAORRESU", "ZASPEC", "VISITNUM", "VISIT", "ZAELTM", "ZATPTREF", "ZAREFID", "ZAXFN")
supppq_cols <- c("STUDYID", "RDOMAIN", "USUBJID", "IDVAR", "IDVARVAL", "QNAM", "QLABEL", "QVAL")
pq_df = data.frame()
supppq_df = data.frame()
if ((class(data_src)[1] == 'MySQLConnection') ||
(class(data_src)[1] == 'SQLiteConnection')) {
if ((assay_type == "ALL") || (assay_type =="ELISA")) {
# get ELISA results
measurement_types <- list("Protein_Quantification", "Cytokine_Quantification")
els_df <- getElisaResults(data_src, study_id, "")
if (nrow(els_df) > 0) {
els_df <- select(els_df, STUDYID = study_id, USUBJID = subject_id, ZASEQ = sequence, ZATEST = experiment_title,
ZACAT = assay_purpose, ZAMETHOD = measurement_technique, ZAANALYT = analyte, ZAORRES = value, ZAORRESU = unit,
ZASPEC = specimen_type, ZASPECSB = specimen_subtype,
ZASPTRT = specimen_treatment,
ZATRTAMV = treatment_amount_value, ZATRTAMU = treatment_amount_unit,
ZATRTDUV = treatment_duration_value, ZATRTDUU = treatment_duration_unit,
ZATRTTMV = treatment_temperature_value, ZATRTTMU = treatment_temperature_unit,
VISIT = visit_name, VISITNUM = visit_order, VISITMIN = visit_min_start_day, VISITMAX = visit_max_start_day,
ZAELTM = elapsed_time_of_specimen_collection, ZATPTREF = time_point_reference,
ZAREFID = experiment_sample_accession, ZAREFIDP = biosample_accession, ZAXFN = file_name)
els_df$DOMAIN <- "ZA"
qnam_values = c("ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU")
qlabel_values= c("Specimen Subtype", "Source Specimen Identifier",
"Planned Visit Minimum Start Day", "Planned Visit Maximum Start Day",
"Specimen Treatment",
"Specimen Treatment Amount Value", "Specimen Treatment Amount Unit",
"Specimen Treatment Duration Value", "Specimen Treatment Duration Unit",
"Specimen Treatment Temperature Value", "Specimen Treatment Temperature Unit")
supp_els_df <- melt(els_df,
id = c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ"),
measure = qnam_values,
variable.name = "QNAM",
value.name = "QVAL")
supp_els_df <- transform(supp_els_df, QLABEL = unlist(qlabel_values[QNAM]))
supp_els_df <- plyr::rename(supp_els_df, c("DOMAIN" = "RDOMAIN", "ZASEQ" = "IDVARVAL"))
supp_els_df$IDVAR <- "ZASEQ"
supp_els_df <- supp_els_df[supppq_cols]
# remove rows that have empty QVAL values
supp_els_df <- subset(supp_els_df,QVAL!="")
els_df <- subset(els_df, select = -c(ZASPECSB, ZAREFIDP, ZASPTRT,
VISITMIN, VISITMAX,
ZATRTAMV, ZATRTAMU,
ZATRTDUV, ZATRTDUU,
ZATRTTMV, ZATRTTMU))
els_df <- els_df[, pq_cols]
pq_df <- rbind(pq_df, els_df)
supppq_df <- rbind(supppq_df, supp_els_df)
}
}
if ((assay_type == "ALL") || (assay_type =="MBAA")) {
# get MBAA results
# mbaa_column_names <- c('study_id', 'subject_id', 'result_id', 'analyte', 'concentration_unit',
# 'concentration_value', 'mfi', 'mfi_coordinate', 'experiment_title', 'assay_purpose', 'measurement_technique',
# 'biosample_accession', 'specimen_type', 'specimen_subtype', 'elapsed_time_of_specimen_collection',
# 'time_point_reference', 'file_name')
measurement_types <- list("Cytokine_Quantification")
mbaa_df <- getMbaaResults(data_src, study_id, "")
if (nrow(mbaa_df) > 0) {
mbaa_df <- mbaa_df %>%
select(STUDYID = study_id, USUBJID = subject_id, ZASEQ = sequence, ZATEST = experiment_title,
ZACAT = assay_purpose, ZAMETHOD = measurement_technique, ZAANALYT = analyte, ZAORRES = concentration_value,
ZAORRESU = concentration_unit, ZASPEC = specimen_type, ZASPECSB = specimen_subtype,
ZASPTRT = specimen_treatment,
ZATRTAMV = treatment_amount_value, ZATRTAMU = treatment_amount_unit,
ZATRTDUV = treatment_duration_value, ZATRTDUU = treatment_duration_unit,
ZATRTTMV = treatment_temperature_value, ZATRTTMU = treatment_temperature_unit,
VISIT = visit_name, VISITNUM = visit_order, VISITMIN = visit_min_start_day, VISITMAX = visit_max_start_day,
ZAELTM = elapsed_time_of_specimen_collection,
ZATPTREF = time_point_reference,
ZAREFID = experiment_sample_accession, ZAREFIDP = biosample_accession,
ZAXFN = file_name, ZAMFI = mfi, ZAMFICRD = mfi_coordinate)
mbaa_df$DOMAIN <- "ZA"
qnam_values = c("ZAMFI", "ZAMFICRD", "ZASPECSB", "ZAREFIDP",
"VISITMIN", "VISITMAX",
"ZASPTRT",
"ZATRTAMV", "ZATRTAMU",
"ZATRTDUV", "ZATRTDUU",
"ZATRTTMV", "ZATRTTMU")
qlabel_values= c("MFI", "MFI Coordinate", "Specimen Subtype", "Source Specimen Identifier",
"Planned Visit Minimum Start Day", "Planned Visit Maximum Start Day",
"Specimen Treatment",
"Specimen Treatment Amount Value", "Specimen Treatment Amount Unit",
"Specimen Treatment Duration Value", "Specimen Treatment Duration Unit",
"Specimen Treatment Temperature Value", "Specimen Treatment Temperature Unit")
supp_mbaa_df <- melt(mbaa_df,
id = c("STUDYID", "DOMAIN", "USUBJID", "ZASEQ"),
measure = qnam_values,
variable.name = "QNAM",
value.name = "QVAL")
supp_mbaa_df <- transform(supp_mbaa_df, QLABEL = unlist(qlabel_values[QNAM]))
supp_mbaa_df <- plyr::rename(supp_mbaa_df, c("DOMAIN" = "RDOMAIN", "ZASEQ" = "IDVARVAL"))
supp_mbaa_df$IDVAR <- "ZASEQ"
supp_mbaa_df <- supp_mbaa_df[supppq_cols]
# remove rows that have empty QVAL values
supp_mbaa_df <- subset(supp_mbaa_df,QVAL!="")
mbaa_df <- subset(mbaa_df, select = -c(ZAMFI, ZAMFICRD, ZASPECSB, ZAREFIDP,
VISITMIN, VISITMAX,
ZASPTRT,
ZATRTAMV, ZATRTAMU,
ZATRTDUV, ZATRTDUU,
ZATRTTMV, ZATRTTMU))
mbaa_df <- mbaa_df[, pq_cols]
pq_df <- rbind(pq_df, mbaa_df)
supppq_df <- rbind(supppq_df, supp_mbaa_df)
}
}
} else {
l <- loadSerializedStudyData(data_src, study_id, "Protein Quantification")
if (assay_type == "ALL") {
pq_df <- l[[1]]
supppq_df <- l[[2]]
} else {
}
}
cat("done", "\n")
pq_l <- list()
if (nrow(pq_df) > 0)
pq_l <- list(za_df=pq_df, suppza_df=supppq_df)
pq_l
}
# Get count of Protein Quantification data of a specific study
#
# The function \code{getCountOfProteinQuantification} queries the ImmPort database for count
# of Protein Quantification data
#
# @param data_src A connection handle to ImmPort database instance
# @param assay_type Assay Type **optional**
# @param study_id Identifier of a specific study
# @return a count of Protein Quantification data
# @examples
# \dontrun{
# # get count of all of study SDY1's Protein Quantification data
# count <- getCountOfProteinQuantification(data_src, "SDY1")
#
# # get count of study SDY1's Protein Quantification data that was generated using ELISA assay
# count <- getCountOfProteinQuantification(data_src, "SDY1", "ELISA")
# }
getCountOfProteinQuantification <- function(data_src, study_id, assay_type="ALL") {
count <- 0
if ((assay_type == "ALL") || (assay_type =="ELISA"))
count <- count + getCountOfElisaResults(data_src, study_id)
if ((assay_type == "ALL") || (assay_type =="MBAA"))
count <- count + getCountOfMbaaResults(data_src, study_id)
count
}
##' Protein Quantification Domain Variables
##' @name ZA
##' @description {
##' \tabular{ll}{
##' \strong{Variable Name } \tab \strong{Variable Label} \cr
##' STUDYID \tab Study Identifier \cr
##' DOMAIN \tab Domain Abbreviation \cr
##' USUBJID \tab Unique Subject Identifier \cr
##' ZASEQ \tab Sequence Number \cr
##' ZATEST \tab Protein Quantification Test Name \cr
##' ZACAT \tab Category for Protein Quantification \cr
##' ZAMETHOD \tab Measurement Technique \cr
##' ZAANALYT \tab Analyte \cr
##' ZAORRES \tab Result or Finding in Original Units \cr
##' ZAORRESU \tab Original Units \cr
##' ZASPEC \tab Specimen Type \cr
##' VISITNUM \tab Visit Number \cr
##' VISIT \tab Visit Name \cr
##' ZAELTM \tab Planned Elapsed Time from Time Point Ref \cr
##' ZATPTREF \tab Time Point Reference \cr
##' ZAREFID \tab Specimen Identifier \cr
##' ZAXFN \tab Raw Data File or Life Science Identifier
##' }
##' }
NULL
#> NULL
##' Protein Quantification Domain Supplemental Variables
##' @name SUPPZA
##' @description {
##' \tabular{ll}{
##' \strong{Variable Name} \tab \strong{Variable Label} \cr
##' STUDYID \tab Study Identifier \cr
##' RDOMAIN \tab Related Domain Abbreviation \cr
##' USUBJID \tab Unique Subject Identifier \cr
##' IDVAR \tab Identifying Variable \cr
##' IDVARVAL \tab Identifying Variable Value \cr
##' QNAM \tab Qualifier Variable Name \cr
##' QLABEL \tab Qualifier Variable Label \cr
##' QVAL \tab Data Value
##' }
##' }
##' @note The following table enumerates the values in QNAM and QLABEL variables {
##' \tabular{ll}{
##' \strong{QNAM} \tab \strong{QLABEL} \cr
##' ZAMFI \tab MFI \cr
##' ZAMFICRD \tab MFI Coordinate \cr
##' ZASPECSB \tab Specimen Subtype \cr
##' ZAREFIDP \tab Source Specimen Identifier \cr
##' VISITMIN \tab Planned Visit Minimum Start Day \cr
##' VISITMAX \tab Planned Visit Maximum Start Day \cr
##' ZASPTRT \tab Specimen Treatment \cr
##' ZATRTAMV \tab Specimen Treatment Amount Value \cr
##' ZATRTAMU \tab Specimen Treatment Amount Unit \cr
##' ZATRTDUV \tab Specimen Treatment Duration Value \cr
##' ZATRTDUU \tab Specimen Treatment Duration Unit \cr
##' ZATRTTMV \tab Specimen Treatment Temperature Value \cr
##' ZATRTTMU \tab Specimen Treatment Temperature Unit
##' }
##' }
NULL
#> NULL
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