From 594dd0f9f9b2a85dab78b6c8908d1d2ba5588ed6 Mon Sep 17 00:00:00 2001 From: Kiweler Date: Fri, 7 Jun 2019 09:58:39 +0200 Subject: [PATCH] update instrument list in readme --- README.Rmd | 13 ++++++++----- README.md | 12 ++++++++---- vignettes/using_MARMoSET.Rmd | 12 ++++++++++-- 3 files changed, 26 insertions(+), 11 deletions(-) diff --git a/README.Rmd b/README.Rmd index 9b7a157..f56eb27 100644 --- a/README.Rmd +++ b/README.Rmd @@ -12,16 +12,17 @@ knitr::opts_chunk$set( ``` # MARMoSET - A package to extract meta data out of Thermo Fischer raw files. Meta data refers to what is found in a raw file as parts of the file header, sample information, instrument method and mass spectrometer tune data. Works with the following instruments: * Thermo EASY-nLC +* RSLCnano * Q Exactive Plus - Orbitrap MS * Q Exactive HF - Orbitrap MS * Q Exactive - Orbitrap MS +* Orbitrap Fusion Lumos ## Motivation: @@ -47,7 +48,7 @@ The c# command line tool can be used in R with `generate_json()` to create a JSO This JSON file can also be created by using the MARMoSET.exe externally. -Due to the design of the RawFileReader the MARMoSET.exe and therefore this command is running as 64 bit code on Windows only. To use this package with another operating system, it is necessarry to already have the JSON file created. To open a JSON file in R the package `jsonlite` provides the function `fromJSON()`. +Due to the design of the RawFileReader, the MARMoSET.exe and therefore this command is running as 64 bit code on Windows only. To use this package with another operating system, it is necessarry to already have the JSON file created. To open a JSON file in R the package `jsonlite` provides the function `fromJSON()`. ```{r} library(MARMoSET) @@ -108,11 +109,13 @@ In the meta data of the 'Thermo EASY-nlc' is a gradient table. This table can be ```{r} gradient_table <- one_gradient_table(flat_json = flat_json, - group_number = 1) + group_number = 1, + lc_pump = "Thermo EASY-nLC") -vector_of_gradient_tables <- gradient_tables(flat_json = flat_json) +vector_of_gradient_tables <- gradient_tables(flat_json = flat_json, + lc_pump = "Thermo EASY-nLC") -head(gradient_table, 3) +head(gradient_table, 2) ``` Finally to export all tables in a vector or one specific, the functions `save_group_table()` and `save_all_groups()`are helpful. The output of `gradient_tables()` or `match_terms()` goes in as `groups_vector` whereas the second argument, `output_path` expects a path with filename. `save_all_groups()` adds the group numbers automatically to each filename, `save_group_table()` takes it as third argument but doesn't add. diff --git a/README.md b/README.md index a2f854a..cee1945 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,11 @@ A package to extract meta data out of Thermo Fischer raw files. Meta data refers Works with the following instruments: - Thermo EASY-nLC +- RSLCnano - Q Exactive Plus - Orbitrap MS - Q Exactive HF - Orbitrap MS - Q Exactive - Orbitrap MS +- Orbitrap Fusion Lumos Motivation: ----------- @@ -40,7 +42,7 @@ The c\# command line tool can be used in R with `generate_json()` to create a JS This JSON file can also be created by using the MARMoSET.exe externally. -Due to the design of the RawFileReader the MARMoSET.exe and therefore this command is running as 64 bit code on Windows only. To use this package with another operating system, it is necessarry to already have the JSON file created. To open a JSON file in R the package `jsonlite` provides the function `fromJSON()`. +Due to the design of the RawFileReader, the MARMoSET.exe and therefore this command is running as 64 bit code on Windows only. To use this package with another operating system, it is necessarry to already have the JSON file created. To open a JSON file in R the package `jsonlite` provides the function `fromJSON()`. ``` r library(MARMoSET) @@ -106,11 +108,13 @@ In the meta data of the 'Thermo EASY-nlc' is a gradient table. This table can be ``` r gradient_table <- one_gradient_table(flat_json = flat_json, - group_number = 1) + group_number = 1, + lc_pump = "Thermo EASY-nLC") -vector_of_gradient_tables <- gradient_tables(flat_json = flat_json) +vector_of_gradient_tables <- gradient_tables(flat_json = flat_json, + lc_pump = "Thermo EASY-nLC") -head(gradient_table, 3) +head(gradient_table, 2) #> Time[mm:ss] Duration[mm:ss] Flow[nl/min] Mixture[%B] #> 1 00:00 00:00 300 5 #> 2 01:00 01:00 300 5 diff --git a/vignettes/using_MARMoSET.Rmd b/vignettes/using_MARMoSET.Rmd index 3b0511d..c6dfd08 100644 --- a/vignettes/using_MARMoSET.Rmd +++ b/vignettes/using_MARMoSET.Rmd @@ -23,9 +23,11 @@ Meta data refers to what is found in a raw file as parts of the file header, sam Works with the following instruments: * Thermo EASY-nLC +* RSLCnano * Q Exactive Plus - Orbitrap MS * Q Exactive HF - Orbitrap MS * Q Exactive - Orbitrap MS +* Orbitrap Fusion Lumos ## Motivation: @@ -112,9 +114,11 @@ In the meta data of the 'Thermo EASY-nlc' is a gradient table. This table can be ```{r} gradient_table <- one_gradient_table(flat_json = flat_json, - group_number = 1) + group_number = 1, + lc_pump = "Thermo EASY-nLC") -vector_of_gradient_tables <- gradient_tables(flat_json = flat_json) +vector_of_gradient_tables <- gradient_tables(flat_json = flat_json, + lc_pump = "Thermo EASY-nLC") head(gradient_table, 2) ``` @@ -142,3 +146,7 @@ group_count(json) files_in_group(json, 1) ``` + +## How to cite: + +Kiweler M, Looso M and Graumann J. MARMoSET – Extracting Publication-Ready Mass Spectrometry Metadata from RAW Files. Molecular & Cellular Proteomics (2019), DOI: 10.1074/mcp.TIR119.001505