Mercurial > repos > tomnl > assess_purity_msms
changeset 20:6c8c4452b8ea draft
planemo upload for repository https://github.com/computational-metabolomics/mspurity-galaxy commit 04023134d8f28e85927ca293373c506484149ead-dirty
| author | tomnl |
|---|---|
| date | Thu, 31 May 2018 09:08:55 -0400 |
| parents | 5ca214b3423e |
| children | f428f3fb82ad |
| files | create_sqlite_db.R frag4feature.R test-data/cpeakgroup_msms.tsv |
| diffstat | 3 files changed, 378 insertions(+), 344 deletions(-) [+] |
line wrap: on
line diff
--- a/create_sqlite_db.R Tue May 15 08:09:47 2018 -0400 +++ b/create_sqlite_db.R Thu May 31 09:08:55 2018 -0400 @@ -138,30 +138,65 @@ con <- DBI::dbConnect(RSQLite::SQLite(), db_pth) -add_extra_table_elucidation <- function(name, pth){ - - if (!is.null(pth)){ - - print(pth) - df <- read.table(pth, header = TRUE, sep='\t', stringsAsFactors = FALSE, comment.char = "") - # bug for repeating headers - df <- df[!df$UID=='UID',] +add_extra_table_elucidation <- function(name, pth, db_con, filter_Score=NA, filter_Rank=NA){ + if (is.null(pth)){ + return(0) + } + index <- 0 + chunkSize <- 5000 + print(pth) + con <- file(description=pth,open="r") + df <- read.table(con, nrows=chunkSize, header = TRUE, sep='\t', stringsAsFactors = FALSE, comment.char = "") + headers = colnames(df) + print(head(df)) + write_to_table(df, db_con, name, FALSE, filter_Score, filter_Rank) - # get peakid, an scan id - df_ids <- stringr::str_split_fixed(df$UID, '-', 3) - colnames(df_ids) <- c('grp_id', 'file_id', 'pid') - df <- cbind(df_ids, df) - # export to database - + repeat { + index <- index + 1 + print(paste('Processing rows:', index * chunkSize)) + + if (nrow(df) != chunkSize){ + print('Processed all files!') + break + } + + df <- read.table(con, nrows=chunkSize, skip=0, header = FALSE, sep='\t', stringsAsFactors = FALSE, comment.char = "") + colnames(df) <- headers - DBI::dbWriteTable(con, name=name, value=df, row.names=FALSE) + + write_to_table(df, db_con, name, TRUE, filter_Score, filter_Rank) - } + + break + } + close(con) } +write_to_table <- function(df, db_con, name, append, filter_Score, filter_Rank){ -add_probmetab <- function(pth){ + df <- df[!df$UID=='UID',] + print(filter_Score) + print(filter_Rank) + print('filter rank and score') + + if (!is.na(filter_Score)){ + df <- df[df$Score>=filter_Score,] + } + + if (!is.na(filter_Rank)){ + + df <- df[df$Rank<=filter_Rank,] + } + + # get peakid, an scan id + df_ids <- stringr::str_split_fixed(df$UID, '-', 3) + colnames(df_ids) <- c('grp_id', 'file_id', 'pid') + df <- cbind(df_ids, df) + DBI::dbWriteTable(db_con, name=name, value=df, row.names=FALSE, append=append) +} + +add_probmetab <- function(pth, con){ if (!is.null(pth)){ df <- read.table(pth, header = TRUE, sep='\t', stringsAsFactors = FALSE, comment.char = "") @@ -204,9 +239,9 @@ } -add_extra_table_elucidation('metfrag_results', opt$metfrag_result) -add_extra_table_elucidation('sirius_csifingerid_results', opt$sirius_csifingerid_result) -add_probmetab(opt$probmetab_result) +add_extra_table_elucidation('metfrag_results', opt$metfrag_result, con, filter_Score=0.6, filter_Rank=NA) +add_extra_table_elucidation('sirius_csifingerid_results', opt$sirius_csifingerid_result, con, filter_Score=NA, filter_Rank=5) +add_probmetab(opt$probmetab_result, con)
--- a/frag4feature.R Tue May 15 08:09:47 2018 -0400 +++ b/frag4feature.R Thu May 31 09:08:55 2018 -0400 @@ -122,7 +122,6 @@ grp_peaklist = opt$grp_peaklist } -print('heck') print(pa@fileList) print(names(pa@fileList)) print(xset@filepaths)
--- a/test-data/cpeakgroup_msms.tsv Tue May 15 08:09:47 2018 -0400 +++ b/test-data/cpeakgroup_msms.tsv Thu May 31 09:08:55 2018 -0400 @@ -38,9 +38,9 @@ 11 111.020205687498 111.013973139866 111.026665972223 88.223637 84.850038 91.376562 792 111.010894775391 111.117050170898 73.612908 97.102908 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 11 111.020205687498 111.013973139866 111.026665972223 88.223637 84.850038 91.376562 1819 111.010864257812 111.117050170898 73.70211 97.514118 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 112.043935634745 112.036344846661 112.05085569128 51.3780825 35.7968838 67.634034 8 112.021553039551 112.039581298828 29.0840262 53.7376638 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA -12 112.043935634745 112.036344846661 112.05085569128 51.3780825 35.7968838 67.634034 9 112.050651550293 112.087196350098 55.2769038 80.361672 384 466 3893860.25 112.050707073208 64.427298 462 466 468 112.050704956055 1 1 112.050704956055 1 1 1 1 1 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474 112.050743103027 1 1 112.050743103027 1 1 1 1 2 "dataset_19.dat" "LCMSMS_1.mzML" 12 112.043935634745 112.036344846661 112.05085569128 51.3780825 35.7968838 67.634034 793 111.914756774902 112.039566040039 23.012628 47.1993852 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 112.043935634745 112.036344846661 112.05085569128 51.3780825 35.7968838 67.634034 794 112.050514221191 112.200119018555 49.1208918 73.934526 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 112.043935634745 112.036344846661 112.05085569128 51.3780825 35.7968838 67.634034 1820 112.021423339844 112.039527893066 23.4470886 47.6065998 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA @@ -49,9 +49,9 @@ 13 112.923083776231 112.89596558352 112.950196506384 37.118376 32.8786392 41.0984136 401 112.89582824707 112.941200256348 28.3160286 52.3652958 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 13 112.923083776231 112.89596558352 112.950196506384 37.118376 32.8786392 41.0984136 795 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