R/getOSWData.R
In singjc/mstools: OpenSWATH Mass Spectrometry Data Exploration
#// **********************************************************************************************
#// getOSWData.R
#// **********************************************************************************************
#//
#//
#// **********************************************************************************************
#// @Maintainer: Justin Sing
#// @Author: Justin Sing
#' @export
#' @title Extract data from OpenSwathWorkflow .osw results file
#' @description This function can be used to extract information from the OSW results file obtained from
#' running OpenSwathWorkflow
#'
#' @param oswfile A character vector of the absolute path and filename of the osw results file. (Must be .osw format)
#' @param run_name A character vector for extraction of a specific run, this should be the same as the file name in the .OSW RUN table. (i.e. run_name='yanliu_I170114_016_PhosNoco4_SW.mzXML.gz', Default: '')
#' @param precursor_id A numeric value for a specific precursor id to extract information for. (Default: '')
#' @param peptide_id A numeric value for a specific peptide id to extract information for. (Default: '')
#' @param peptide_unmodified A string vector indicating a specific unmodified peptide sequence to extract information for. (Default: '')
#' @param peptide_modified A string vector indicating a specific modified peptide sequence to extract information for. (Default: '')
#' @param mod_peptide_id An array of two string vectors indicating a specific modified peptide sequence with both UniMod annotation and actual modification name to extract information for. I.e. c(ANS(Phos)SNSLK, ANS(UniMod:21)SNSLK) (Default: '')
#' @param mod_residue_position A numeric value indicating the position of a modification. (Default: '')
#' @param peak_group_rank_filter A Logical value for filtering OSW results by Peak-Group Rank of 1. (Default: FALSE)
#' @param report_top_single_result A logical value indificating to keep only the results with the lowest m_score. (Default: TRUE)
#' @param pep_list An arrary of string modified peptide sequences to extract information for. (Default: '')
#' @param mscore_filter A named numeric value to filter results by named q-value. (Default: '') Options: SCORE_MS2, SCORE_IPF, SCORE_PEPTIDE. Example: c(SCORE_MS2=0.01)
#' @param ipf_filter A numeric value to filter results by IPF PEP. (Default: '')
#' @param ipf_score A logical value to extract data using IPF Score results. (Default: FALSE)
#' @param ms2_score A logical value to extract data using MS2 Score results. (Default: TRUE)
#' @param decoy_filter A logical value to filter decoys out of final results. (Default: TRUE)
#' @param mod_exclusion A vector of unimod record id modifications to exclude, and replace by nothing. Example: c("35")
#' @param mod_grouping A dataframe contained uninod ids and random characters to group isomers by. Example: data.frame( record_id=c(4, 259, 267), rand_char_id="B", stringsAsFactors = F )
#' @param remove_KR_label A vector of UniMod Ids to remove from Arginine or Lysine. Example: c("UniMod:259", "UniMod:267")
#' @return A data.table containing OpenSwath Results information.
#'
#' @author Justin Sing \url{https://github.com/singjc}
#'
#' @import data.table
#' @importFrom DBI dbConnect dbDisconnect
#' @importFrom RSQLite SQLite
#' @importFrom dplyr collect tbl
#' @importFrom dbplyr sql
#' @importFrom MazamaCoreUtils logger.isInitialized logger.info logger.error logger.warn logger.trace
getOSWData_ <- function ( oswfile,
run_name='',
precursor_id='',
peptide_id='',
peptide_unmodified='',
peptide_modified='',
mod_peptide_id=c('',''),
mod_residue_position='',
peak_group_rank_filter=FALSE,
report_top_single_result=FALSE,
pep_list='',
mscore_filter=c(SCORE_=1),
ipf_filter='',
ipf_score=FALSE,
ms2_score=TRUE,
decoy_filter=TRUE,
inference_level='peptide_query',
mod_exclusion=NULL,
mod_grouping=NULL,
remove_KR_label=NULL){
if ( F ) {
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Synth_PhosoPep/Justin_Synth_PhosPep/results/lower_product_mz_threshold/pyprophet/group_id/merged_runs_group_id_MS1MS2_intergration_ipf.osw"
# run_name <- '/project/def-hroest/data/synth_phospho_pep/mzML/chludwig_K150309_013_SW_0.mzXML'
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Christian_Doerig_Dataset/results/U_pools_sgolay_24/pyprophet_parametric/merged_runs_group_id_MS1MS2_intergration.osw"
# run_name <- "/home/singjust/projects/def-hroest/data/christian_doerig_dataset/U_pools_mzML/sw/lgillet_L160915_024-Manchester_dirty_phospho_-_Pool_U12_-_SW.mzML.gz"
# run_name = "lgillet_L160915_028-Manchester_dirty_phospho_-_Pool_U14_-_SW.mzML.gz"
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Christian_Doerig_Dataset/results/M_pools_re-run/pyprophet_parametric_no_precursor/merged_runs_group_id_MS1MS2_intergration.osw"
# run_name <- "lgillet_L160918_002-Manchester_dirty_phospho_-_Pool_M1_-_SW.mzML.gz"
# run_name <- "lgillet_L160915_002-Manchester_dirty_phospho_-_Pool_U1_-_SW.mzML.gz"
# Load Requried Libraries
# library(dplyr)
# library(dbplyr)
oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Synth_PhosoPep/From_George/02032020/ipfm_nl_JS/psgs.osw"
run_name <- "chludwig_K150309_001b_SW_1_64"
run_name <- "chludwig_K150309_006b_SW_1_8.mzXML.gz"
run_name <- "chludwig_K150309_013_SW_0.mzXML.gz"
run_name <- "chludwig_K150309_004b_SW_1_16.mzXML.gz"
mod_exclusion=c(35)
mod_grouping=data.frame( record_id=c(4, 259, 267), rand_char_id="B", stringsAsFactors = F )
ipf_score=F
remove_KR_label <- c("UniMod:259", "UniMod:267")
oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/phospho_enriched_U2OS/Results/05062020_results_george_lib_repeat/yanliu_I170114_040_PhosNoco10_SW/yanliu_I170114_040_PhosNoco10_SW_MSDATA_RESULTS.osw"
}
# Check if logging has been initialized
if( !MazamaCoreUtils::logger.isInitialized() ){
log_setup()
}
# Connect to database
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Connecting To Database: %s\n", oswfile))
osw_db <- DBI::dbConnect( RSQLite::SQLite(), oswfile )
## Filter for a specific run
if ( run_name != '' ){
run_id_df = mstools::getRunID_(oswfile, run_name)
run_id_query = sprintf("AND RUN.ID=(%s)", run_id_df$ID)
} else {
run_id_query = ''
}
## Check if FEATURE_MS1 table exits
if( DBI::dbExistsTable( osw_db, "FEATURE_MS1" ) ){
join_feature_ms1 <- sprintf("LEFT JOIN FEATURE_MS1 ON FEATURE_MS1.FEATURE_ID = FEATURE.ID")
select_feature_ms1 <- sprintf("FEATURE_MS1.AREA_INTENSITY AS aggr_prec_Peak_Area,
FEATURE_MS1.APEX_INTENSITY AS aggr_prec_Peak_Apex,")
} else {
join_feature_ms1 <- ""
select_feature_ms1 <- ""
}
## Check is SCORE_MS1 table exists
if( DBI::dbExistsTable( osw_db, "SCORE_MS1" ) ){
join_score_ms1 <- sprintf("LEFT JOIN SCORE_MS1 ON SCORE_MS1.FEATURE_ID = FEATURE.ID")
select_score_ms1 <- sprintf("SCORE_MS1.PEP AS ms1_pep,")
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS1 Table...\n" )
join_score_ms1 <- ""
select_score_ms1 <- ""
}
## Use IPF scores
if ( ipf_score ){
### Check if SCORE_IPF table exits
if( DBI::dbExistsTable( osw_db, "SCORE_IPF" ) ){
join_score_ipf <- sprintf("INNER JOIN SCORE_IPF ON SCORE_IPF.FEATURE_ID = FEATURE.ID")
select_score_ipf <- sprintf("SCORE_IPF.PRECURSOR_PEAKGROUP_PEP AS precursor_pep,
SCORE_IPF.PEP AS ipf_pep,
SCORE_IPF.QVALUE AS m_score,")
join_peptide <- sprintf("INNER JOIN PEPTIDE ON ( PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID OR PEPTIDE.ID = SCORE_IPF.PEPTIDE_ID )")
miscellaneous_score_ipf_control <- "AND SCORE_IPF.QVALUE IS NOT NULL AND PEPTIDE.MODIFIED_SEQUENCE NOT LIKE '%UniMod%'"
which_m_score <- "m_score"
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
} else {
MazamaCoreUtils::logger.error(sprintf("[mstools::getOSWData_] There was no SCORE_IPF table found in: %s\nFalling back to SCORE_MS2 scores only", oswfile))
join_score_ipf <- ""
select_score_ipf <- ""
join_peptide <- sprintf("INNER JOIN PEPTIDE ON PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID")
miscellaneous_score_ipf_control <- ""
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
}
} else {
join_score_ipf <- ""
select_score_ipf <- ""
join_peptide <- sprintf("INNER JOIN PEPTIDE ON PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID")
miscellaneous_score_ipf_control <- ""
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
}
## Filter for a specific precursor id
if ( precursor_id != '' ){
precursor_query = sprintf("AND PRECURSOR.ID = (%s)", precursor_id)
} else {
precursor_query = ''
}
## Filter for a specific peptide id
if( peptide_id != ''){
peptide_query = sprintf("AND PEPTIDE.ID = (%s)", peptide_id)
} else {
peptide_query = ''
}
## Filter for a specific unmodified peptide sequence
if( peptide_unmodified != ''){
peptide_unmodified_query = sprintf("AND PEPTIDE.UNMODIFIED_SEQUENCE = ('%s')", peptide_unmodified)
} else {
peptide_unmodified_query = ''
}
## Filter for a specific modified peptide sequence
if( peptide_modified != ''){
peptide_modified_query = sprintf("AND PEPTIDE.MODIFIED_SEQUENCE = ('%s')", peptide_modified)
} else {
peptide_modified_query = ''
}
## Filter Out Decoys
if ( decoy_filter==TRUE ){
decoy_filter_query <- "AND PRECURSOR.DECOY=0"
} else {
decoy_filter_query <- ""
}
## Filter Peak Group Rank
if ( peak_group_rank_filter & DBI::dbExistsTable( osw_db, "SCORE_MS2") ){
peak_group_rank_filter_query <- "AND SCORE_MS2.RANK=1"
} else {
peak_group_rank_filter_query <- ""
}
## Filter for a specifc Q_VALUE/M_SCORE
if ( names(mscore_filter)=="SCORE_MS2" & DBI::dbExistsTable( osw_db, "SCORE_MS2") ){
m_score_filter_query <- sprintf( "AND SCORE_MS2.QVALUE<%s", mscore_filter )
} else if ( names(mscore_filter)=="SCORE_IPF" & DBI::dbExistsTable( osw_db, "SCORE_IPF") ){
m_score_filter_query <- sprintf( "AND SCORE_IPF.QVALUE<%s", mscore_filter )
} else if ( names(mscore_filter)=="SCORE_PEPTIDE" & DBI::dbExistsTable( osw_db, "SCORE_PEPTIDE") ){
m_score_filter_query <- sprintf( "AND SCORE_PEPTIDE.QVALUE<%s", mscore_filter )
} else {
m_score_filter_query <- ""
}
## Filter for a specific modified peptied sequence
if ( mod_peptide_id[1]!='' & mod_peptide_id[2]!='' ){
if( ipf_score == TRUE){
mod_peptide_query = sprintf("WHERE PEPTIDE_IPF.MODIFIED_SEQUENCE=('%s') OR PEPTIDE_IPF.MODIFIED_SEQUENCE=('%s')", mod_peptide_id[1], mod_peptide_id[2]) # PEPTIDE.MODIFIED_SEQUENCE use to correspond with PEPTIDE_IPF.MODIFIED_SEQUENCE. @ Justin
} else {
mod_peptide_query = sprintf("WHERE PEPTIDE.MODIFIED_SEQUENCE=('%s') OR PEPTIDE.MODIFIED_SEQUENCE=('%s')", mod_peptide_id[1], mod_peptide_id[2])
}
} else {
mod_peptide_query = ''
}
## Filter for a specific modification position
if (mod_residue_position!=''){
if ((mscore_filter != '') && (ipf_filter != '')){
mod_residue_position_query = sprintf("AND INSTR(FullPeptideName,'(Phospho)')=(%s) OR INSTR(FullPeptideName,'(UniMod:21)')=(%s)", mod_residue_position, mod_residue_position)
} else {
mod_residue_position_query = sprintf("WHERE INSTR(FullPeptideName,'(Phospho)')=(%s) OR INSTR(FullPeptideName,'(UniMod:21)')=(%s)", mod_residue_position, mod_residue_position)
}
} else {
mod_residue_position_query = ''
}
## Use context level
if ( inference_level=="peptide_inference" ){
inference_level_query <- "INNER JOIN SCORE_PEPTIDE ON SCORE_PEPTIDE.PEPTIDE_ID=PEPTIDE.ID"
select_level_query <- sprintf(", SCORE_PEPTIDE.CONTEXT as context,
SCORE_PEPTIDE.PEP as peptide_pep,
SCORE_PEPTIDE.SCORE as peptide_d_score,
SCORE_PEPTIDE.QVALUE as peptide_m_score")
} else if ( inference_level=="protein_inference" ){
inference_level_query <- "INNER JOIN SCORE_PROTEIN ON SCORE_PROTEIN.PEPTIDE_ID=PEPTIDE.ID"
} else {
inference_level_query <- ""
}
# if (ipf_score == TRUE && ms2_score == TRUE){
# select_as_stmt = sprintf("SELECT FEATURE.ID AS feature_id,
# PEPTIDE_IPF.ID AS id_ipf_peptide,
# PEPTIDE.ID AS id_peptide,
# PRECURSOR.ID AS id_precursor,
# --PEPTIDE_IPF.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PRECURSOR.DECOY AS decoy,
# RUN.ID AS run_id,
# RUN.FILENAME AS filename,
# FEATURE.EXP_RT AS RT,
# FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
# FEATURE.DELTA_RT AS delta_rt,
# FEATURE.NORM_RT AS iRT,
# PRECURSOR.LIBRARY_RT AS assay_iRT,
# FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
# FEATURE.ID AS id,
# --PEPTIDE_IPF.UNMODIFIED_SEQUENCE AS Sequence,
# --PEPTIDE_IPF.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
# PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE_IPF.MODIFIED_SEQUENCE AS ipf_FullPeptideName,
# PRECURSOR.CHARGE AS Charge,
# PRECURSOR.PRECURSOR_MZ AS mz,
# FEATURE_MS2.AREA_INTENSITY AS Intensity,
# %s
# FEATURE.LEFT_WIDTH AS leftWidth,
# FEATURE.RIGHT_WIDTH AS rightWidth,
# %s
# SCORE_MS2.PEP AS ms2_pep,
# SCORE_IPF.PRECURSOR_PEAKGROUP_PEP AS precursor_pep,
# SCORE_IPF.PEP AS ipf_pep,
# SCORE_MS2.RANK AS peak_group_rank,
# SCORE_MS2.SCORE AS d_score,
# SCORE_MS2.QVALUE AS ms2_m_score,
# SCORE_IPF.QVALUE AS m_score", select_feature_ms1, select_score_ms1 )
# include_ipf_score = 'LEFT JOIN SCORE_IPF ON SCORE_MS2.FEATURE_ID = SCORE_IPF.FEATURE_ID'
#
# if (peak_group_rank_filter == TRUE){
# pk_grp_rnk_fil_query = 'INNER JOIN PEPTIDE AS PEPTIDE_IPF ON SCORE_IPF.PEPTIDE_ID = PEPTIDE_IPF.ID '
# } else {
# pk_grp_rnk_fil_query = 'LEFT JOIN PEPTIDE AS PEPTIDE_IPF ON SCORE_IPF.PEPTIDE_ID = PEPTIDE_IPF.ID'
# }
# } else if ((ipf_score == FALSE && ms2_score == TRUE)){
# select_as_stmt = sprintf("SELECT FEATURE.ID AS feature_id,
# PEPTIDE.ID AS id_peptide,
# PRECURSOR.ID AS id_precursor,
# PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PRECURSOR.DECOY AS decoy,
# RUN.ID AS run_id,
# RUN.FILENAME AS filename,
# FEATURE.EXP_RT AS RT,
# FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
# FEATURE.DELTA_RT AS delta_rt,
# FEATURE.NORM_RT AS iRT,
# PRECURSOR.LIBRARY_RT AS assay_iRT,
# FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
# FEATURE.ID AS id,
# PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
# PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE.MODIFIED_SEQUENCE AS ipf_FullPeptideName,
# PRECURSOR.CHARGE AS Charge,
# PRECURSOR.PRECURSOR_MZ AS mz,
# FEATURE_MS2.AREA_INTENSITY AS Intensity,
# %s
# FEATURE.LEFT_WIDTH AS leftWidth,
# FEATURE.RIGHT_WIDTH AS rightWidth,
# %s
# SCORE_MS2.PEP AS ms2_pep,
# SCORE_MS2.RANK AS peak_group_rank,
# SCORE_MS2.SCORE AS d_score,
# SCORE_MS2.QVALUE AS ms2_m_score", select_feature_ms1, select_score_ms1 )
# # print(select_as_stmt)
# if (peak_group_rank_filter == TRUE){
# pk_grp_rnk_fil_query = 'WHERE SCORE_MS2.RANK=1'
# } else {
# pk_grp_rnk_fil_query = ''
# }
# include_ipf_score = ''
#
# } else {
# cat( 'There was an error with the ms2_score or ipf_score argument!!!\n' )
# cat( sprintf("ms2_score: %s\n", ms2_score) )
# cat( sprintf("ipf_score: %s\n", ipf_score) )
# }
#
# if (pep_list != ''){
# filter_multiple_peps = sprintf("WHERE PEPTIDE.MODIFIED_SEQUENCE IN (%s)", paste("'" + paste(pep_list, collapse=",") + "'") )
# } else {
# filter_multiple_peps = ''
# }
#
# if ((mscore_filter != '') && (ipf_filter != '')){
# qval_filter_query = sprintf("WHERE SCORE_MS2.QVALUE < %s AND SCORE_IPF.PEP < %s", mscore_filter, ipf_filter)
# } else {
# qval_filter_query = ''
# }
## Construct Query Statement
# stmt = sprintf(
# "%s
# FROM PRECURSOR
# INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR.ID = PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID %s
# %s
# %s
# %s
# %s -- Join FEATURE_MS1 table
# LEFT JOIN FEATURE_MS2 ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
# %s -- Join SCORE_MS1
# LEFT JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID
# %s
# %s
# %s
# %s
# %s
# %s
# ORDER BY transition_group_id,
# peak_group_rank
# ", select_as_stmt, decoy_filter_query, peptide_query, precursor_query, run_id_query, join_feature_ms1, join_score_ms1, include_ipf_score, pk_grp_rnk_fil_query, filter_multiple_peps, qval_filter_query, mod_peptide_query, mod_residue_position_query)
#
stmt = sprintf(
"SELECT
FEATURE.ID AS feature_id,
PEPTIDE.ID AS id_peptide,
PRECURSOR.ID AS id_precursor,
PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
RUN.ID AS run_id,
RUN.FILENAME AS filename,
FEATURE.EXP_RT AS RT,
FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
FEATURE.DELTA_RT AS delta_rt,
FEATURE.NORM_RT AS iRT,
PRECURSOR.LIBRARY_RT AS assay_iRT,
FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PRECURSOR.CHARGE AS Charge,
PRECURSOR.PRECURSOR_MZ AS mz,
FEATURE_MS2.AREA_INTENSITY AS Intensity,
%s -- FEATURE_MS1 select statements #select_feature_ms1
FEATURE.LEFT_WIDTH AS leftWidth,
FEATURE.RIGHT_WIDTH AS rightWidth,
%s -- SCORE_MS1 select statements #select_score_ms1
%s -- SCORE_MS2 select statements #select_score_ms2
%s -- SCORE_IPF select statements #select_score_ipf
PRECURSOR.DECOY AS decoy
FROM FEATURE
INNER JOIN RUN ON RUN.ID = FEATURE.RUN_ID
%s -- Join FEATURE_MS1 Table if available #join_feature_ms1
INNER JOIN FEATURE_MS2 ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
%s -- Join SCORE_MS1 Table if available #join_score_ms1
%s -- Join SCORE_MS2 Table if available #join_score_ms2
%s -- Join SCORE_IPF Table if available #join_score_ipf
INNER JOIN PRECURSOR ON PRECURSOR.ID = FEATURE.PRECURSOR_ID %s -- Filter for Decoyrs #decoy_filter_query
INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID = FEATURE.PRECURSOR_ID
%s -- Join PEPTIDE Table #join_peptide
WHERE FEATURE.ID IS NOT NULL -- Default WHERE Being clause
%s -- Miscellaneous control statements for if SCORE_IPF is used #miscellaneous_score_ipf_control
%s -- Filter for a specific PRECURSOR.ID #precursor_query
%s -- Filter for a specific PEPTIDE.ID #peptide_query
%s -- Filter for a specific unmodified peptide sequence #peptide_unmodified_query
%s -- Filter for a specific modified peptide sequence #peptide_modified_query
%s -- Filter for specific RUN.ID #run_id_query
%s -- Filter for PeakGroupRank=1 #peak_group_rank_filter_query
%s -- Filter for a specific level of Q-Value. (SCORE_MS2, SCORE_IPF, SCORE_PEPTIDE)
ORDER BY transition_group_id
%s -- Add second level of order using peak_group_rank, if SCORE_MS2 is present. #order_peak_group_rank
", select_feature_ms1, select_score_ms1, select_score_ms2, select_score_ipf, join_feature_ms1, join_score_ms1, join_score_ms2, join_score_ipf, decoy_filter_query, join_peptide,
miscellaneous_score_ipf_control, precursor_query, peptide_query, peptide_unmodified_query, peptide_modified_query, run_id_query, peak_group_rank_filter_query, m_score_filter_query, order_peak_group_rank )
# Query Databasse
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] QueryingDatabase: %s\n", stmt))
df_osw <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(stmt)) )
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:394)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
## Remove Arginine and Lysine labels
if ( !is.null(remove_KR_label) ){
df_osw$FullPeptideName <- gsub( paste(paste('\\(', remove_KR_label, '\\)', sep=''), collapse = '|'), '', df_osw$FullPeptideName )
}
## Get Precursor Table for Precursor_Peptide_Mapping
stmt2 <- sprintf("SELECT
PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID AS id_peptide,
PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID AS id_precursor,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PEPTIDE.DECOY AS decoy,
PRECURSOR.TRAML_ID AS traml_id,
PRECURSOR.GROUP_LABEL AS group_label,
PRECURSOR.PRECURSOR_MZ AS precursor_mz,
PRECURSOR.CHARGE AS precursor_charge,
PRECURSOR.LIBRARY_INTENSITY AS library_int,
PRECURSOR.LIBRARY_RT AS library_rt
FROM PEPTIDE
INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID = PEPTIDE.ID
INNER JOIN PRECURSOR ON PRECURSOR.ID = PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID" )
df_precursor_peptide_mapping_table <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(stmt2)) )
## Get Peptide Table
df_peptide_table <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql("SELECT * FROM PEPTIDE")) )
## TODO: Need to make this more robust in the case that TRAML_ID is no longer valid
# df_osw %>%
# dplyr::filter( paste(df_osw$FullPeptideName, df_osw$id_precursor, sep="_") %in% unlist(lapply(seq(1:dim(df_precursor_peptide_mapping_table)[1]), function(x){ gsub("*\\d+$", df_precursor_peptide_mapping_table$ID[x], df_precursor_peptide_mapping_table$TRAML_ID[x]) } )) ) -> df_osw
#
df_osw <- data.table::as.data.table(df_osw)
df_osw[, FullPeptideName.unimod := mstools::codenameTounimod(FullPeptideName) ]
## Get the original assay peptide being tested.
df_osw %>%
dplyr::group_by( feature_id ) %>%
dplyr::mutate( original_assay = id_precursor ) -> df_osw
df_osw %>%
dplyr::mutate( original_assay = plyr::mapvalues(x = original_assay, from = df_precursor_peptide_mapping_table$id_precursor, to = df_precursor_peptide_mapping_table$FullPeptideName, warn_missing = FALSE) ) %>%
dplyr::ungroup() -> df_osw
if ( report_top_single_result ) {
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_] Filtering for top single results based on %s: ", which_m_score, "\n"))
## If extracting for IPF results filter by feature id first
if ( ipf_score ){
## Filter by lowest ipf_m_score per feature id
df_osw %>%
dplyr::group_by( feature_id ) %>%
# dplyr::select( feature_id, FullPeptideName.unimod, ms2_pep, ms2_m_score ) %>%
# dplyr::filter( feature_id==-1898217479312465)
# dplyr::filter( FullPeptideName.unimod=="TYS(UniMod:21)LRNQRAPTAAELQAPPPPPSSTK(UniMod:259)") %>%
dplyr::filter( !!rlang::sym(which_m_score) == min(!!rlang::sym(which_m_score)) ) %>%
dplyr::ungroup()-> df_osw
}
## Filter by lowest ipf_m_score grouped by fullpeptidename.unimod
df_osw %>%
dplyr::group_by( FullPeptideName.unimod ) %>%
# dplyr::filter( FullPeptideName.unimod=="TYS(UniMod:21)LRNQRAPTAAELQAPPPPPSSTK(UniMod:259)") %>%
dplyr::filter( !!rlang::sym(which_m_score) == min(!!rlang::sym(which_m_score)) ) %>%
dplyr::ungroup()-> df_osw
}
# ## Filter by matching precursor id
# df_osw %>%
# dplyr::filter( paste(df_osw$FullPeptideName.unimod, df_osw$id_precursor, sep='_') %in% paste(df_precursor_peptide_mapping_table$FullPeptideName, df_precursor_peptide_mapping_table$id_precursor, sep='_') ) -> df_osw
## Remove tmp column
df_osw$FullPeptideName.unimod <- NULL
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:428)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
## Context Level Inference
if ( any(inference_level %in% "peptide_inference") ){
inference_level_query <- sprintf( "SELECT
SCORE_PEPTIDE.CONTEXT AS context,
SCORE_PEPTIDE.RUN_ID AS run_id,
SCORE_PEPTIDE.PEPTIDE_ID AS id_peptide,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PEPTIDE.DECOY as decoy,
SCORE_PEPTIDE.PEP AS peptide_pep,
SCORE_PEPTIDE.SCORE AS peptide_d_score,
SCORE_PEPTIDE.QVALUE AS peptide_m_score
FROM PEPTIDE
INNER JOIN SCORE_PEPTIDE ON PEPTIDE.ID=SCORE_PEPTIDE.PEPTIDE_ID" )
df_peptide_inference <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(inference_level_query)) )
if ( unique(df_peptide_inference$context)=='global' ){
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Results contain GLOBAL level contexts.. Returning single data.frame for GLOBAL level contexts"))
## Check if remove decoys is set to true
if ( decoy_filter==TRUE ){
df_peptide_inference %>%
dplyr::filter( decoy==0 ) -> df_peptide_inference
}
df_osw <- df_peptide_inference
} else if ( unique(df_peptide_inference$context)=='experiment-wide' ){
## Check if we need to filter for a specific run id
if ( run_name != '' ){
df_peptide_inference %>%
dplyr::filter( run_id==run_id_df$ID ) -> df_peptide_inference
}
## Check if remove decoys is set to true
if ( decoy_filter==TRUE ){
df_peptide_inference %>%
dplyr::filter( decoy==0 ) -> df_peptide_inference
}
df_peptide_inference$FullPeptideName <- unlist(lapply( df_peptide_inference$FullPeptideName, mstools::unimodTocodename )) # TODO: Need to make unimodTocodename work without doing a list loop
df_peptide_inference <- merge(df_peptide_inference, dplyr::select(df_peptide_table, c("ID", "MODIFIED_SEQUENCE")), by.x="FullPeptideName", by.y="MODIFIED_SEQUENCE", all.x = T)
df_osw <- merge(df_osw, dplyr::select(df_peptide_inference, c("FullPeptideName", "ID", "context", "peptide_pep", "peptide_d_score", "peptide_m_score")), by.x=c("FullPeptideName", "id_peptide"), by.y = c("FullPeptideName", "ID"), all.x=T)
} else if ( unique(df_peptide_inference$context)=='run-specific' ){
MazamaCoreUtils::logger.error( sprintf("[mstools::getOSWData_] run-specific context level is not yet supported... Returning Default MS2_Score/IPF_Score results.." ) )
} else {
MazamaCoreUtils::logger.error( sprintf("[mstools::getOSWData_] Unknown '%s' context level... Returning Default MS2_Score/IPF_Score results..", unique(df_peptide_inference$context) ) )
}
}
if ( any(inference_level %in% "protein_inference") ){
MazamaCoreUtils::logger.trace("[mstools::getOSWData_] Protein level inference is not yet supported. Submit Feature Request on GitHub to singjc/mstools")
}
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:405)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
# Ensure UniMod Mapping Convention
df_osw <- data.table::as.data.table(df_osw)
df_osw[, FullPeptideName := mstools::codenameTounimod(FullPeptideName) ]
# Append isomer character id
df_osw[, isomer_char_id := mstools::unimodTouniisoid( FullPeptideName, mod_exclusion=mod_exclusion, mod_grouping=mod_grouping ) ]
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:490)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
# Disconnect from database
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Disconnecting From Database: %s\n", oswfile))
DBI::dbDisconnect(osw_db)
return( df_osw )
}
singjc/mstools documentation built on June 28, 2020, 8:10 p.m.
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#// **********************************************************************************************
#// getOSWData.R
#// **********************************************************************************************
#//
#//
#// **********************************************************************************************
#// @Maintainer: Justin Sing
#// @Author: Justin Sing
#' @export
#' @title Extract data from OpenSwathWorkflow .osw results file
#' @description This function can be used to extract information from the OSW results file obtained from
#' running OpenSwathWorkflow
#'
#' @param oswfile A character vector of the absolute path and filename of the osw results file. (Must be .osw format)
#' @param run_name A character vector for extraction of a specific run, this should be the same as the file name in the .OSW RUN table. (i.e. run_name='yanliu_I170114_016_PhosNoco4_SW.mzXML.gz', Default: '')
#' @param precursor_id A numeric value for a specific precursor id to extract information for. (Default: '')
#' @param peptide_id A numeric value for a specific peptide id to extract information for. (Default: '')
#' @param peptide_unmodified A string vector indicating a specific unmodified peptide sequence to extract information for. (Default: '')
#' @param peptide_modified A string vector indicating a specific modified peptide sequence to extract information for. (Default: '')
#' @param mod_peptide_id An array of two string vectors indicating a specific modified peptide sequence with both UniMod annotation and actual modification name to extract information for. I.e. c(ANS(Phos)SNSLK, ANS(UniMod:21)SNSLK) (Default: '')
#' @param mod_residue_position A numeric value indicating the position of a modification. (Default: '')
#' @param peak_group_rank_filter A Logical value for filtering OSW results by Peak-Group Rank of 1. (Default: FALSE)
#' @param report_top_single_result A logical value indificating to keep only the results with the lowest m_score. (Default: TRUE)
#' @param pep_list An arrary of string modified peptide sequences to extract information for. (Default: '')
#' @param mscore_filter A named numeric value to filter results by named q-value. (Default: '') Options: SCORE_MS2, SCORE_IPF, SCORE_PEPTIDE. Example: c(SCORE_MS2=0.01)
#' @param ipf_filter A numeric value to filter results by IPF PEP. (Default: '')
#' @param ipf_score A logical value to extract data using IPF Score results. (Default: FALSE)
#' @param ms2_score A logical value to extract data using MS2 Score results. (Default: TRUE)
#' @param decoy_filter A logical value to filter decoys out of final results. (Default: TRUE)
#' @param mod_exclusion A vector of unimod record id modifications to exclude, and replace by nothing. Example: c("35")
#' @param mod_grouping A dataframe contained uninod ids and random characters to group isomers by. Example: data.frame( record_id=c(4, 259, 267), rand_char_id="B", stringsAsFactors = F )
#' @param remove_KR_label A vector of UniMod Ids to remove from Arginine or Lysine. Example: c("UniMod:259", "UniMod:267")
#' @return A data.table containing OpenSwath Results information.
#'
#' @author Justin Sing \url{https://github.com/singjc}
#'
#' @import data.table
#' @importFrom DBI dbConnect dbDisconnect
#' @importFrom RSQLite SQLite
#' @importFrom dplyr collect tbl
#' @importFrom dbplyr sql
#' @importFrom MazamaCoreUtils logger.isInitialized logger.info logger.error logger.warn logger.trace
getOSWData_ <- function ( oswfile,
run_name='',
precursor_id='',
peptide_id='',
peptide_unmodified='',
peptide_modified='',
mod_peptide_id=c('',''),
mod_residue_position='',
peak_group_rank_filter=FALSE,
report_top_single_result=FALSE,
pep_list='',
mscore_filter=c(SCORE_=1),
ipf_filter='',
ipf_score=FALSE,
ms2_score=TRUE,
decoy_filter=TRUE,
inference_level='peptide_query',
mod_exclusion=NULL,
mod_grouping=NULL,
remove_KR_label=NULL){
if ( F ) {
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Synth_PhosoPep/Justin_Synth_PhosPep/results/lower_product_mz_threshold/pyprophet/group_id/merged_runs_group_id_MS1MS2_intergration_ipf.osw"
# run_name <- '/project/def-hroest/data/synth_phospho_pep/mzML/chludwig_K150309_013_SW_0.mzXML'
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Christian_Doerig_Dataset/results/U_pools_sgolay_24/pyprophet_parametric/merged_runs_group_id_MS1MS2_intergration.osw"
# run_name <- "/home/singjust/projects/def-hroest/data/christian_doerig_dataset/U_pools_mzML/sw/lgillet_L160915_024-Manchester_dirty_phospho_-_Pool_U12_-_SW.mzML.gz"
# run_name = "lgillet_L160915_028-Manchester_dirty_phospho_-_Pool_U14_-_SW.mzML.gz"
# oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Christian_Doerig_Dataset/results/M_pools_re-run/pyprophet_parametric_no_precursor/merged_runs_group_id_MS1MS2_intergration.osw"
# run_name <- "lgillet_L160918_002-Manchester_dirty_phospho_-_Pool_M1_-_SW.mzML.gz"
# run_name <- "lgillet_L160915_002-Manchester_dirty_phospho_-_Pool_U1_-_SW.mzML.gz"
# Load Requried Libraries
# library(dplyr)
# library(dbplyr)
oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/Synth_PhosoPep/From_George/02032020/ipfm_nl_JS/psgs.osw"
run_name <- "chludwig_K150309_001b_SW_1_64"
run_name <- "chludwig_K150309_006b_SW_1_8.mzXML.gz"
run_name <- "chludwig_K150309_013_SW_0.mzXML.gz"
run_name <- "chludwig_K150309_004b_SW_1_16.mzXML.gz"
mod_exclusion=c(35)
mod_grouping=data.frame( record_id=c(4, 259, 267), rand_char_id="B", stringsAsFactors = F )
ipf_score=F
remove_KR_label <- c("UniMod:259", "UniMod:267")
oswfile <- "/media/justincsing/ExtraDrive1/Documents2/Roest_Lab/Github/PTMs_Project/phospho_enriched_U2OS/Results/05062020_results_george_lib_repeat/yanliu_I170114_040_PhosNoco10_SW/yanliu_I170114_040_PhosNoco10_SW_MSDATA_RESULTS.osw"
}
# Check if logging has been initialized
if( !MazamaCoreUtils::logger.isInitialized() ){
log_setup()
}
# Connect to database
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Connecting To Database: %s\n", oswfile))
osw_db <- DBI::dbConnect( RSQLite::SQLite(), oswfile )
## Filter for a specific run
if ( run_name != '' ){
run_id_df = mstools::getRunID_(oswfile, run_name)
run_id_query = sprintf("AND RUN.ID=(%s)", run_id_df$ID)
} else {
run_id_query = ''
}
## Check if FEATURE_MS1 table exits
if( DBI::dbExistsTable( osw_db, "FEATURE_MS1" ) ){
join_feature_ms1 <- sprintf("LEFT JOIN FEATURE_MS1 ON FEATURE_MS1.FEATURE_ID = FEATURE.ID")
select_feature_ms1 <- sprintf("FEATURE_MS1.AREA_INTENSITY AS aggr_prec_Peak_Area,
FEATURE_MS1.APEX_INTENSITY AS aggr_prec_Peak_Apex,")
} else {
join_feature_ms1 <- ""
select_feature_ms1 <- ""
}
## Check is SCORE_MS1 table exists
if( DBI::dbExistsTable( osw_db, "SCORE_MS1" ) ){
join_score_ms1 <- sprintf("LEFT JOIN SCORE_MS1 ON SCORE_MS1.FEATURE_ID = FEATURE.ID")
select_score_ms1 <- sprintf("SCORE_MS1.PEP AS ms1_pep,")
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS1 Table...\n" )
join_score_ms1 <- ""
select_score_ms1 <- ""
}
## Use IPF scores
if ( ipf_score ){
### Check if SCORE_IPF table exits
if( DBI::dbExistsTable( osw_db, "SCORE_IPF" ) ){
join_score_ipf <- sprintf("INNER JOIN SCORE_IPF ON SCORE_IPF.FEATURE_ID = FEATURE.ID")
select_score_ipf <- sprintf("SCORE_IPF.PRECURSOR_PEAKGROUP_PEP AS precursor_pep,
SCORE_IPF.PEP AS ipf_pep,
SCORE_IPF.QVALUE AS m_score,")
join_peptide <- sprintf("INNER JOIN PEPTIDE ON ( PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID OR PEPTIDE.ID = SCORE_IPF.PEPTIDE_ID )")
miscellaneous_score_ipf_control <- "AND SCORE_IPF.QVALUE IS NOT NULL AND PEPTIDE.MODIFIED_SEQUENCE NOT LIKE '%UniMod%'"
which_m_score <- "m_score"
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
} else {
MazamaCoreUtils::logger.error(sprintf("[mstools::getOSWData_] There was no SCORE_IPF table found in: %s\nFalling back to SCORE_MS2 scores only", oswfile))
join_score_ipf <- ""
select_score_ipf <- ""
join_peptide <- sprintf("INNER JOIN PEPTIDE ON PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID")
miscellaneous_score_ipf_control <- ""
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
}
} else {
join_score_ipf <- ""
select_score_ipf <- ""
join_peptide <- sprintf("INNER JOIN PEPTIDE ON PEPTIDE.ID = PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID")
miscellaneous_score_ipf_control <- ""
if( DBI::dbExistsTable( osw_db, "SCORE_MS2" ) ){
which_m_score <- "ms2_m_score"
join_score_ms2 <- "INNER JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID"
select_score_ms2 <- sprintf("SCORE_MS2.PEP AS ms2_pep,
SCORE_MS2.RANK AS peak_group_rank,
SCORE_MS2.SCORE AS d_score,
SCORE_MS2.QVALUE AS ms2_m_score,")
order_peak_group_rank <- ", peak_group_rank"
} else {
MazamaCoreUtils::logger.warn( "[mstools::getOSWData_] There was no SCORE_MS2 Table. Pyprophet scoring was most likely not performed...\n" )
which_m_score <- NULL
join_score_ms2 <- ""
select_score_ms2 <- ""
order_peak_group_rank <- ""
}
}
## Filter for a specific precursor id
if ( precursor_id != '' ){
precursor_query = sprintf("AND PRECURSOR.ID = (%s)", precursor_id)
} else {
precursor_query = ''
}
## Filter for a specific peptide id
if( peptide_id != ''){
peptide_query = sprintf("AND PEPTIDE.ID = (%s)", peptide_id)
} else {
peptide_query = ''
}
## Filter for a specific unmodified peptide sequence
if( peptide_unmodified != ''){
peptide_unmodified_query = sprintf("AND PEPTIDE.UNMODIFIED_SEQUENCE = ('%s')", peptide_unmodified)
} else {
peptide_unmodified_query = ''
}
## Filter for a specific modified peptide sequence
if( peptide_modified != ''){
peptide_modified_query = sprintf("AND PEPTIDE.MODIFIED_SEQUENCE = ('%s')", peptide_modified)
} else {
peptide_modified_query = ''
}
## Filter Out Decoys
if ( decoy_filter==TRUE ){
decoy_filter_query <- "AND PRECURSOR.DECOY=0"
} else {
decoy_filter_query <- ""
}
## Filter Peak Group Rank
if ( peak_group_rank_filter & DBI::dbExistsTable( osw_db, "SCORE_MS2") ){
peak_group_rank_filter_query <- "AND SCORE_MS2.RANK=1"
} else {
peak_group_rank_filter_query <- ""
}
## Filter for a specifc Q_VALUE/M_SCORE
if ( names(mscore_filter)=="SCORE_MS2" & DBI::dbExistsTable( osw_db, "SCORE_MS2") ){
m_score_filter_query <- sprintf( "AND SCORE_MS2.QVALUE<%s", mscore_filter )
} else if ( names(mscore_filter)=="SCORE_IPF" & DBI::dbExistsTable( osw_db, "SCORE_IPF") ){
m_score_filter_query <- sprintf( "AND SCORE_IPF.QVALUE<%s", mscore_filter )
} else if ( names(mscore_filter)=="SCORE_PEPTIDE" & DBI::dbExistsTable( osw_db, "SCORE_PEPTIDE") ){
m_score_filter_query <- sprintf( "AND SCORE_PEPTIDE.QVALUE<%s", mscore_filter )
} else {
m_score_filter_query <- ""
}
## Filter for a specific modified peptied sequence
if ( mod_peptide_id[1]!='' & mod_peptide_id[2]!='' ){
if( ipf_score == TRUE){
mod_peptide_query = sprintf("WHERE PEPTIDE_IPF.MODIFIED_SEQUENCE=('%s') OR PEPTIDE_IPF.MODIFIED_SEQUENCE=('%s')", mod_peptide_id[1], mod_peptide_id[2]) # PEPTIDE.MODIFIED_SEQUENCE use to correspond with PEPTIDE_IPF.MODIFIED_SEQUENCE. @ Justin
} else {
mod_peptide_query = sprintf("WHERE PEPTIDE.MODIFIED_SEQUENCE=('%s') OR PEPTIDE.MODIFIED_SEQUENCE=('%s')", mod_peptide_id[1], mod_peptide_id[2])
}
} else {
mod_peptide_query = ''
}
## Filter for a specific modification position
if (mod_residue_position!=''){
if ((mscore_filter != '') && (ipf_filter != '')){
mod_residue_position_query = sprintf("AND INSTR(FullPeptideName,'(Phospho)')=(%s) OR INSTR(FullPeptideName,'(UniMod:21)')=(%s)", mod_residue_position, mod_residue_position)
} else {
mod_residue_position_query = sprintf("WHERE INSTR(FullPeptideName,'(Phospho)')=(%s) OR INSTR(FullPeptideName,'(UniMod:21)')=(%s)", mod_residue_position, mod_residue_position)
}
} else {
mod_residue_position_query = ''
}
## Use context level
if ( inference_level=="peptide_inference" ){
inference_level_query <- "INNER JOIN SCORE_PEPTIDE ON SCORE_PEPTIDE.PEPTIDE_ID=PEPTIDE.ID"
select_level_query <- sprintf(", SCORE_PEPTIDE.CONTEXT as context,
SCORE_PEPTIDE.PEP as peptide_pep,
SCORE_PEPTIDE.SCORE as peptide_d_score,
SCORE_PEPTIDE.QVALUE as peptide_m_score")
} else if ( inference_level=="protein_inference" ){
inference_level_query <- "INNER JOIN SCORE_PROTEIN ON SCORE_PROTEIN.PEPTIDE_ID=PEPTIDE.ID"
} else {
inference_level_query <- ""
}
# if (ipf_score == TRUE && ms2_score == TRUE){
# select_as_stmt = sprintf("SELECT FEATURE.ID AS feature_id,
# PEPTIDE_IPF.ID AS id_ipf_peptide,
# PEPTIDE.ID AS id_peptide,
# PRECURSOR.ID AS id_precursor,
# --PEPTIDE_IPF.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PRECURSOR.DECOY AS decoy,
# RUN.ID AS run_id,
# RUN.FILENAME AS filename,
# FEATURE.EXP_RT AS RT,
# FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
# FEATURE.DELTA_RT AS delta_rt,
# FEATURE.NORM_RT AS iRT,
# PRECURSOR.LIBRARY_RT AS assay_iRT,
# FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
# FEATURE.ID AS id,
# --PEPTIDE_IPF.UNMODIFIED_SEQUENCE AS Sequence,
# --PEPTIDE_IPF.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
# PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE_IPF.MODIFIED_SEQUENCE AS ipf_FullPeptideName,
# PRECURSOR.CHARGE AS Charge,
# PRECURSOR.PRECURSOR_MZ AS mz,
# FEATURE_MS2.AREA_INTENSITY AS Intensity,
# %s
# FEATURE.LEFT_WIDTH AS leftWidth,
# FEATURE.RIGHT_WIDTH AS rightWidth,
# %s
# SCORE_MS2.PEP AS ms2_pep,
# SCORE_IPF.PRECURSOR_PEAKGROUP_PEP AS precursor_pep,
# SCORE_IPF.PEP AS ipf_pep,
# SCORE_MS2.RANK AS peak_group_rank,
# SCORE_MS2.SCORE AS d_score,
# SCORE_MS2.QVALUE AS ms2_m_score,
# SCORE_IPF.QVALUE AS m_score", select_feature_ms1, select_score_ms1 )
# include_ipf_score = 'LEFT JOIN SCORE_IPF ON SCORE_MS2.FEATURE_ID = SCORE_IPF.FEATURE_ID'
#
# if (peak_group_rank_filter == TRUE){
# pk_grp_rnk_fil_query = 'INNER JOIN PEPTIDE AS PEPTIDE_IPF ON SCORE_IPF.PEPTIDE_ID = PEPTIDE_IPF.ID '
# } else {
# pk_grp_rnk_fil_query = 'LEFT JOIN PEPTIDE AS PEPTIDE_IPF ON SCORE_IPF.PEPTIDE_ID = PEPTIDE_IPF.ID'
# }
# } else if ((ipf_score == FALSE && ms2_score == TRUE)){
# select_as_stmt = sprintf("SELECT FEATURE.ID AS feature_id,
# PEPTIDE.ID AS id_peptide,
# PRECURSOR.ID AS id_precursor,
# PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
# PRECURSOR.DECOY AS decoy,
# RUN.ID AS run_id,
# RUN.FILENAME AS filename,
# FEATURE.EXP_RT AS RT,
# FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
# FEATURE.DELTA_RT AS delta_rt,
# FEATURE.NORM_RT AS iRT,
# PRECURSOR.LIBRARY_RT AS assay_iRT,
# FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
# FEATURE.ID AS id,
# PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
# PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
# PEPTIDE.MODIFIED_SEQUENCE AS ipf_FullPeptideName,
# PRECURSOR.CHARGE AS Charge,
# PRECURSOR.PRECURSOR_MZ AS mz,
# FEATURE_MS2.AREA_INTENSITY AS Intensity,
# %s
# FEATURE.LEFT_WIDTH AS leftWidth,
# FEATURE.RIGHT_WIDTH AS rightWidth,
# %s
# SCORE_MS2.PEP AS ms2_pep,
# SCORE_MS2.RANK AS peak_group_rank,
# SCORE_MS2.SCORE AS d_score,
# SCORE_MS2.QVALUE AS ms2_m_score", select_feature_ms1, select_score_ms1 )
# # print(select_as_stmt)
# if (peak_group_rank_filter == TRUE){
# pk_grp_rnk_fil_query = 'WHERE SCORE_MS2.RANK=1'
# } else {
# pk_grp_rnk_fil_query = ''
# }
# include_ipf_score = ''
#
# } else {
# cat( 'There was an error with the ms2_score or ipf_score argument!!!\n' )
# cat( sprintf("ms2_score: %s\n", ms2_score) )
# cat( sprintf("ipf_score: %s\n", ipf_score) )
# }
#
# if (pep_list != ''){
# filter_multiple_peps = sprintf("WHERE PEPTIDE.MODIFIED_SEQUENCE IN (%s)", paste("'" + paste(pep_list, collapse=",") + "'") )
# } else {
# filter_multiple_peps = ''
# }
#
# if ((mscore_filter != '') && (ipf_filter != '')){
# qval_filter_query = sprintf("WHERE SCORE_MS2.QVALUE < %s AND SCORE_IPF.PEP < %s", mscore_filter, ipf_filter)
# } else {
# qval_filter_query = ''
# }
## Construct Query Statement
# stmt = sprintf(
# "%s
# FROM PRECURSOR
# INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR.ID = PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID %s
# %s
# %s
# %s
# %s -- Join FEATURE_MS1 table
# LEFT JOIN FEATURE_MS2 ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
# %s -- Join SCORE_MS1
# LEFT JOIN SCORE_MS2 ON SCORE_MS2.FEATURE_ID = FEATURE.ID
# %s
# %s
# %s
# %s
# %s
# %s
# ORDER BY transition_group_id,
# peak_group_rank
# ", select_as_stmt, decoy_filter_query, peptide_query, precursor_query, run_id_query, join_feature_ms1, join_score_ms1, include_ipf_score, pk_grp_rnk_fil_query, filter_multiple_peps, qval_filter_query, mod_peptide_query, mod_residue_position_query)
#
stmt = sprintf(
"SELECT
FEATURE.ID AS feature_id,
PEPTIDE.ID AS id_peptide,
PRECURSOR.ID AS id_precursor,
PEPTIDE.MODIFIED_SEQUENCE || '_' || PRECURSOR.ID AS transition_group_id,
RUN.ID AS run_id,
RUN.FILENAME AS filename,
FEATURE.EXP_RT AS RT,
FEATURE.EXP_RT - FEATURE.DELTA_RT AS assay_rt,
FEATURE.DELTA_RT AS delta_rt,
FEATURE.NORM_RT AS iRT,
PRECURSOR.LIBRARY_RT AS assay_iRT,
FEATURE.NORM_RT - PRECURSOR.LIBRARY_RT AS delta_iRT,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PRECURSOR.CHARGE AS Charge,
PRECURSOR.PRECURSOR_MZ AS mz,
FEATURE_MS2.AREA_INTENSITY AS Intensity,
%s -- FEATURE_MS1 select statements #select_feature_ms1
FEATURE.LEFT_WIDTH AS leftWidth,
FEATURE.RIGHT_WIDTH AS rightWidth,
%s -- SCORE_MS1 select statements #select_score_ms1
%s -- SCORE_MS2 select statements #select_score_ms2
%s -- SCORE_IPF select statements #select_score_ipf
PRECURSOR.DECOY AS decoy
FROM FEATURE
INNER JOIN RUN ON RUN.ID = FEATURE.RUN_ID
%s -- Join FEATURE_MS1 Table if available #join_feature_ms1
INNER JOIN FEATURE_MS2 ON FEATURE_MS2.FEATURE_ID = FEATURE.ID
%s -- Join SCORE_MS1 Table if available #join_score_ms1
%s -- Join SCORE_MS2 Table if available #join_score_ms2
%s -- Join SCORE_IPF Table if available #join_score_ipf
INNER JOIN PRECURSOR ON PRECURSOR.ID = FEATURE.PRECURSOR_ID %s -- Filter for Decoyrs #decoy_filter_query
INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID = FEATURE.PRECURSOR_ID
%s -- Join PEPTIDE Table #join_peptide
WHERE FEATURE.ID IS NOT NULL -- Default WHERE Being clause
%s -- Miscellaneous control statements for if SCORE_IPF is used #miscellaneous_score_ipf_control
%s -- Filter for a specific PRECURSOR.ID #precursor_query
%s -- Filter for a specific PEPTIDE.ID #peptide_query
%s -- Filter for a specific unmodified peptide sequence #peptide_unmodified_query
%s -- Filter for a specific modified peptide sequence #peptide_modified_query
%s -- Filter for specific RUN.ID #run_id_query
%s -- Filter for PeakGroupRank=1 #peak_group_rank_filter_query
%s -- Filter for a specific level of Q-Value. (SCORE_MS2, SCORE_IPF, SCORE_PEPTIDE)
ORDER BY transition_group_id
%s -- Add second level of order using peak_group_rank, if SCORE_MS2 is present. #order_peak_group_rank
", select_feature_ms1, select_score_ms1, select_score_ms2, select_score_ipf, join_feature_ms1, join_score_ms1, join_score_ms2, join_score_ipf, decoy_filter_query, join_peptide,
miscellaneous_score_ipf_control, precursor_query, peptide_query, peptide_unmodified_query, peptide_modified_query, run_id_query, peak_group_rank_filter_query, m_score_filter_query, order_peak_group_rank )
# Query Databasse
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] QueryingDatabase: %s\n", stmt))
df_osw <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(stmt)) )
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:394)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
## Remove Arginine and Lysine labels
if ( !is.null(remove_KR_label) ){
df_osw$FullPeptideName <- gsub( paste(paste('\\(', remove_KR_label, '\\)', sep=''), collapse = '|'), '', df_osw$FullPeptideName )
}
## Get Precursor Table for Precursor_Peptide_Mapping
stmt2 <- sprintf("SELECT
PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID AS id_peptide,
PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID AS id_precursor,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PEPTIDE.DECOY AS decoy,
PRECURSOR.TRAML_ID AS traml_id,
PRECURSOR.GROUP_LABEL AS group_label,
PRECURSOR.PRECURSOR_MZ AS precursor_mz,
PRECURSOR.CHARGE AS precursor_charge,
PRECURSOR.LIBRARY_INTENSITY AS library_int,
PRECURSOR.LIBRARY_RT AS library_rt
FROM PEPTIDE
INNER JOIN PRECURSOR_PEPTIDE_MAPPING ON PRECURSOR_PEPTIDE_MAPPING.PEPTIDE_ID = PEPTIDE.ID
INNER JOIN PRECURSOR ON PRECURSOR.ID = PRECURSOR_PEPTIDE_MAPPING.PRECURSOR_ID" )
df_precursor_peptide_mapping_table <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(stmt2)) )
## Get Peptide Table
df_peptide_table <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql("SELECT * FROM PEPTIDE")) )
## TODO: Need to make this more robust in the case that TRAML_ID is no longer valid
# df_osw %>%
# dplyr::filter( paste(df_osw$FullPeptideName, df_osw$id_precursor, sep="_") %in% unlist(lapply(seq(1:dim(df_precursor_peptide_mapping_table)[1]), function(x){ gsub("*\\d+$", df_precursor_peptide_mapping_table$ID[x], df_precursor_peptide_mapping_table$TRAML_ID[x]) } )) ) -> df_osw
#
df_osw <- data.table::as.data.table(df_osw)
df_osw[, FullPeptideName.unimod := mstools::codenameTounimod(FullPeptideName) ]
## Get the original assay peptide being tested.
df_osw %>%
dplyr::group_by( feature_id ) %>%
dplyr::mutate( original_assay = id_precursor ) -> df_osw
df_osw %>%
dplyr::mutate( original_assay = plyr::mapvalues(x = original_assay, from = df_precursor_peptide_mapping_table$id_precursor, to = df_precursor_peptide_mapping_table$FullPeptideName, warn_missing = FALSE) ) %>%
dplyr::ungroup() -> df_osw
if ( report_top_single_result ) {
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_] Filtering for top single results based on %s: ", which_m_score, "\n"))
## If extracting for IPF results filter by feature id first
if ( ipf_score ){
## Filter by lowest ipf_m_score per feature id
df_osw %>%
dplyr::group_by( feature_id ) %>%
# dplyr::select( feature_id, FullPeptideName.unimod, ms2_pep, ms2_m_score ) %>%
# dplyr::filter( feature_id==-1898217479312465)
# dplyr::filter( FullPeptideName.unimod=="TYS(UniMod:21)LRNQRAPTAAELQAPPPPPSSTK(UniMod:259)") %>%
dplyr::filter( !!rlang::sym(which_m_score) == min(!!rlang::sym(which_m_score)) ) %>%
dplyr::ungroup()-> df_osw
}
## Filter by lowest ipf_m_score grouped by fullpeptidename.unimod
df_osw %>%
dplyr::group_by( FullPeptideName.unimod ) %>%
# dplyr::filter( FullPeptideName.unimod=="TYS(UniMod:21)LRNQRAPTAAELQAPPPPPSSTK(UniMod:259)") %>%
dplyr::filter( !!rlang::sym(which_m_score) == min(!!rlang::sym(which_m_score)) ) %>%
dplyr::ungroup()-> df_osw
}
# ## Filter by matching precursor id
# df_osw %>%
# dplyr::filter( paste(df_osw$FullPeptideName.unimod, df_osw$id_precursor, sep='_') %in% paste(df_precursor_peptide_mapping_table$FullPeptideName, df_precursor_peptide_mapping_table$id_precursor, sep='_') ) -> df_osw
## Remove tmp column
df_osw$FullPeptideName.unimod <- NULL
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:428)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
## Context Level Inference
if ( any(inference_level %in% "peptide_inference") ){
inference_level_query <- sprintf( "SELECT
SCORE_PEPTIDE.CONTEXT AS context,
SCORE_PEPTIDE.RUN_ID AS run_id,
SCORE_PEPTIDE.PEPTIDE_ID AS id_peptide,
PEPTIDE.UNMODIFIED_SEQUENCE AS Sequence,
PEPTIDE.MODIFIED_SEQUENCE AS FullPeptideName,
PEPTIDE.DECOY as decoy,
SCORE_PEPTIDE.PEP AS peptide_pep,
SCORE_PEPTIDE.SCORE AS peptide_d_score,
SCORE_PEPTIDE.QVALUE AS peptide_m_score
FROM PEPTIDE
INNER JOIN SCORE_PEPTIDE ON PEPTIDE.ID=SCORE_PEPTIDE.PEPTIDE_ID" )
df_peptide_inference <- dplyr::collect( dplyr::tbl(osw_db, dbplyr::sql(inference_level_query)) )
if ( unique(df_peptide_inference$context)=='global' ){
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Results contain GLOBAL level contexts.. Returning single data.frame for GLOBAL level contexts"))
## Check if remove decoys is set to true
if ( decoy_filter==TRUE ){
df_peptide_inference %>%
dplyr::filter( decoy==0 ) -> df_peptide_inference
}
df_osw <- df_peptide_inference
} else if ( unique(df_peptide_inference$context)=='experiment-wide' ){
## Check if we need to filter for a specific run id
if ( run_name != '' ){
df_peptide_inference %>%
dplyr::filter( run_id==run_id_df$ID ) -> df_peptide_inference
}
## Check if remove decoys is set to true
if ( decoy_filter==TRUE ){
df_peptide_inference %>%
dplyr::filter( decoy==0 ) -> df_peptide_inference
}
df_peptide_inference$FullPeptideName <- unlist(lapply( df_peptide_inference$FullPeptideName, mstools::unimodTocodename )) # TODO: Need to make unimodTocodename work without doing a list loop
df_peptide_inference <- merge(df_peptide_inference, dplyr::select(df_peptide_table, c("ID", "MODIFIED_SEQUENCE")), by.x="FullPeptideName", by.y="MODIFIED_SEQUENCE", all.x = T)
df_osw <- merge(df_osw, dplyr::select(df_peptide_inference, c("FullPeptideName", "ID", "context", "peptide_pep", "peptide_d_score", "peptide_m_score")), by.x=c("FullPeptideName", "id_peptide"), by.y = c("FullPeptideName", "ID"), all.x=T)
} else if ( unique(df_peptide_inference$context)=='run-specific' ){
MazamaCoreUtils::logger.error( sprintf("[mstools::getOSWData_] run-specific context level is not yet supported... Returning Default MS2_Score/IPF_Score results.." ) )
} else {
MazamaCoreUtils::logger.error( sprintf("[mstools::getOSWData_] Unknown '%s' context level... Returning Default MS2_Score/IPF_Score results..", unique(df_peptide_inference$context) ) )
}
}
if ( any(inference_level %in% "protein_inference") ){
MazamaCoreUtils::logger.trace("[mstools::getOSWData_] Protein level inference is not yet supported. Submit Feature Request on GitHub to singjc/mstools")
}
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:405)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
# Ensure UniMod Mapping Convention
df_osw <- data.table::as.data.table(df_osw)
df_osw[, FullPeptideName := mstools::codenameTounimod(FullPeptideName) ]
# Append isomer character id
df_osw[, isomer_char_id := mstools::unimodTouniisoid( FullPeptideName, mod_exclusion=mod_exclusion, mod_grouping=mod_grouping ) ]
MazamaCoreUtils::logger.trace(paste("[mstools::getOSWData_(#R:490)] Dimensions of OSW Results file: ", paste(dim(df_osw), collapse = ", "), "\n"))
# Disconnect from database
MazamaCoreUtils::logger.trace(sprintf("[mstools::getOSWData_] Disconnecting From Database: %s\n", oswfile))
DBI::dbDisconnect(osw_db)
return( df_osw )
}
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