new tmt table

loosolab · Feb 7, 2019 · 37af5a3 · 37af5a3
1 parent 75eaba1
commit 37af5a3
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Showing 18 changed files with 132 additions and 30 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -18,10 +18,12 @@ Imports:
     assertive.sets,
     assertive.strings,
     assertive.types,
+    dplyr,
     jsonlite,
     pathological,
     rlist,
     stringi,
+    tidyr,
     magrittr
 Depends:
     R (>= 2.10)

diff --git a/R/load_term_matching_table_function.R b/R/load_term_matching_table_function.R
@@ -3,16 +3,16 @@
 #' @param instrument_list list of instruments in the raw file, get it with "instrument_names()".
 #' @param origin_key Specifies which information is required.
 #'   If empty, all information is used.
-#'   "jpr" for the requirements of the Journal of Proteome Research.
-#'   "mcp" for the requirements of the Molecular and Cellular Proteomics.
+#'   "jpr_guidelines_ms" for the requirements of the Journal of Proteome Research.
+#'   "miape" for The Minimal Information about a Proteomics Experiment (MIAPE) from the Proteomics Standards Initiative.
 #'
 #'
 #' @return A table needed to extract the meta data with match_terms().
 #'
 #' @examples
 #' term_matching_table <- create_term_match_table(
 #'   instrument_list = c("Thermo_EASY-nLC", "Q_Exactive_-_Orbitrap_MS"),
-#'   origin_key = "jpr")
+#'   origin_key = "jpr_guidelines_ms")
 #'
 #' @export
 
@@ -33,21 +33,35 @@ create_term_match_table <- function(instrument_list = c("Thermo_EASY-nLC", "Q Ex
 #' @param origin_key Specifies which information is required.
 #'   If empty, all information is used.
 #'   "jpr_guidelines_ms" for the requirements of the Journal of Proteome Research.
-#'   "mcp_guidelines_ms" for the requirements of the Molecular and Cellular Proteomics.
+#'   "miape" for The Minimal Information about a Proteomics Experiment (MIAPE) from the Proteomics Standards Initiative.
 #'
 #'
 #' @return Table with tmt part for this instrument.
 
-tmt_one_instrument <- function(instrument, origin_key = "")
+tmt_one_instrument <- function(instrument, origin_key = "all")
 {
   instrument %<>% gsub(pattern = " ", replacement = "_" )
 
   assertive.sets::assert_is_subset(instrument, names(MARMoSET::tmt_list))
-  assertive.properties::assert_is_non_empty(grep(MARMoSET::tmt_list[[instrument]][["origin"]], pattern = origin_key))
 
-  sub_index <- MARMoSET::tmt_list[[instrument]][["origin"]] %>%
-    grep(pattern = origin_key)
+  okey_is_ok <- function(x){ assertive.properties::has_rows(MARMoSET::tmt_list[[instrument]] %>%
+                                                                     tidyr::separate_rows(origin, sep=';') %>%
+                                                                     dplyr::filter(origin == origin_key)) ||
+                              (origin_key == "" || origin_key == "all")}
 
-  rbind( MARMoSET::tmt_list[[instrument]][sub_index,]) %>%
-  return()
+  assertive.base::assert_engine( okey_is_ok, origin_key,
+                                 msg = "origin_key is not a valid key!",
+                                 severity = "stop",
+                                 what = "any")
+
+  sub_index <- MARMoSET::tmt_list[[instrument]]
+
+  if(origin_key != "" && origin_key != "all")
+  {
+    sub_index %<>%
+      tidyr::separate_rows(origin, sep=';') %>%
+      dplyr::filter(origin == origin_key)
+  }
+
+  return(sub_index)
 }
diff --git a/R/output_functions.R b/R/output_functions.R
@@ -14,7 +14,7 @@
 #'
 #' term_matching_table <- create_term_match_table(
 #'   instrument_list = c("Thermo_EASY-nLC", "Q_Exactive_-_Orbitrap_MS"),
-#'   origin_key = "jpr")
+#'   origin_key = "jpr_guidelines_ms")
 #'
 #' vector_of_group_tables <- match_terms(flat_json, term_matching_table)
 #'
@@ -74,7 +74,7 @@ utils::globalVariables('.')
 #'
 #' term_matching_table <- create_term_match_table(
 #'   instrument_list = c("Thermo_EASY-nLC", "Q_Exactive_-_Orbitrap_MS"),
-#'   origin_key = "jpr")
+#'   origin_key = "jpr_guidelines_ms")
 #'
 #' vector_of_group_tables <- match_terms(flat_json, term_matching_table)
 #'

diff --git a/R/term_matching_functions.R b/R/term_matching_functions.R
@@ -15,7 +15,7 @@
 #'
 #' term_matching_table <- create_term_match_table(
 #'   instrument_list = instrument_names(json, 1),
-#'   origin_key = "jpr")
+#'   origin_key = "jpr_guidelines_ms")
 #'
 #' vector_of_group_tables <- match_terms(flat_json, term_matching_table)
 #'
@@ -52,7 +52,7 @@ match_terms <- function(flat_json, term_matching_table)
 #'
 #' term_matching_table <- create_term_match_table(
 #'   instrument_list = instrument_names(json, 1),
-#'   origin_key = "jpr")
+#'   origin_key = "jpr_guidelines_ms")
 #'
 #' meta_data_table <- one_group_match_terms(flat_json, term_matching_table, 1)
 #'

diff --git a/README.Rmd b/README.Rmd
@@ -75,12 +75,12 @@ flat_json <- flatten_json(json = json)
 ``` 
 
 Since raw files include a huge amount of meta data and only several of this information is required by journals there is the need to sort out. Therefore a table linking which information is essential and where to find it in the flattened JSON is useful. Such a here refered to as 'term matching table' can be created with `term_matching_table()` by submitting two arguments. The first, `instrument_list` takes a vector with the names of the instruments represented in the JSON file.
-The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr'` stands for the requirements of the Journal of Proteome Research, `'mcp'` chooses the requirements of the Molecular and Cellular Proteomics.
+The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr_guidelines_ms'` stands for the requirements of the Journal of Proteome Research, `'miape'` for the Minimal Information about a Proteomics Experiment (MIAPE) from the Proteomics Standards Initiative.
 
 ```{r}
 term_matching_table <- create_term_match_table(
   instrument_list = c('Thermo EASY-nLC', 'Q Exactive - Orbitrap_MS'),
-  origin_key = 'jpr')
+  origin_key = 'jpr_guidelines_ms')
 ```
 
 The names of the instruments can be shown for each group with `instrument_names()` with the json and the group number as arguments.

diff --git a/README.md b/README.md
@@ -66,12 +66,12 @@ To allow an easier access to the JSON file it needs to be flattened, this works
 flat_json <- flatten_json(json = json)
 ```
 
-Since raw files include a huge amount of meta data and only several of this information is required by journals there is the need to sort out. Therefore a table linking which information is essential and where to find it in the flattened JSON is useful. Such a here refered to as 'term matching table' can be created with `term_matching_table()` by submitting two arguments. The first, `instrument_list` takes a vector with the names of the instruments represented in the JSON file. The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr'` stands for the requirements of the Journal of Proteome Research, `'mcp'` chooses the requirements of the Molecular and Cellular Proteomics.
+Since raw files include a huge amount of meta data and only several of this information is required by journals there is the need to sort out. Therefore a table linking which information is essential and where to find it in the flattened JSON is useful. Such a here refered to as 'term matching table' can be created with `term_matching_table()` by submitting two arguments. The first, `instrument_list` takes a vector with the names of the instruments represented in the JSON file. The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr_guidelines_ms'` stands for the requirements of the Journal of Proteome Research, `'miape'` for the Minimal Information about a Proteomics Experiment (MIAPE) from the Proteomics Standards Initiative..
 
 ``` r
 term_matching_table <- create_term_match_table(
   instrument_list = c('Thermo EASY-nLC', 'Q Exactive - Orbitrap_MS'),
-  origin_key = 'jpr')
+  origin_key = 'jpr_guidelines_ms')
 ```
 
 The names of the instruments can be shown for each group with `instrument_names()` with the json and the group number as arguments.

diff --git a/data/tmt_list.rda b/data/tmt_list.rda
diff --git a/man/create_term_match_table.Rd b/man/create_term_match_table.Rd
diff --git a/man/grapes-greater-than-grapes.Rd b/man/grapes-greater-than-grapes.Rd
diff --git a/man/grapes-less-than-greater-than-grapes.Rd b/man/grapes-less-than-greater-than-grapes.Rd
diff --git a/man/match_terms.Rd b/man/match_terms.Rd
diff --git a/man/one_group_match_terms.Rd b/man/one_group_match_terms.Rd
diff --git a/man/save_all_groups.Rd b/man/save_all_groups.Rd
diff --git a/man/save_group_table.Rd b/man/save_group_table.Rd
diff --git a/man/tmt_one_instrument.Rd b/man/tmt_one_instrument.Rd
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
@@ -0,0 +1,2 @@
+*.html
+*.R
diff --git a/vignettes/manipulate_term_match_table.Rmd b/vignettes/manipulate_term_match_table.Rmd
@@ -0,0 +1,84 @@
+---
+title: "Term matching tables"
+author: "Marina Kiweler"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Vignette Title}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+library(MARMoSET)
+```
+
+## Structure
+
+The term matching table contains links between the information needed to create the output table.
+Some general term matching tables can be created by the function `create_term_matching_table()` by specifying the used instruments and the origin key.
+
+This function falls back on the included list of data frames `tmt_list` where each table is designed to fit one instrument. By calling the function with only one instrument name it will just extract the specific data frame. If called with more than one instrument (ordinarily 2) it will bind the tables together.
+
+```{r}
+tmt_LC_pump <- create_term_match_table(
+  instrument_list = c("Thermo EASY-nLC"))
+  # no origin key
+```
+
+The table always consists of 6 columns whose order is important. The count of rows differ depending on the choosen requirements and the type of instrument.
+```{r}
+colnames(tmt_LC_pump)
+```
+
+The first collumn of the resulting table is always the `term`. It is a short and unique description of the row. While the second, `term_verbose` contains a more detailed and readable title.
+
+```{r}
+head(tmt_LC_pump[1:2])
+```
+
+The third collumn `origin` contains keys determining which requirements to satisfy. These keys were read in the function `create_term_matching_table()` and compared to the value of `origin_key`.
+
+```{r}
+head(tmt_LC_pump[3])
+```
+In the fourth column `handle_type` specifys how to interpret the fifth column `handle`.
+"list_path" indicates that `handle` is a path in the flattened json.
+"literal" leads to just copy the value of the handle.
+With "parameter" the row stays empty because the information is not represented in the json.
+
+```{r}
+head(tmt_LC_pump[4:5])
+```
+
+The last collumn shows an example of which value the row could have.
+
+```{r}
+head(tmt_LC_pump[c(2,6)])
+```
+
+## Create an own combination of the available requirements
+
+First extract a term matching table for the used instruments without specifying the origin key.
+
+```{r}
+full_tmt <- create_term_match_table(
+  instrument_list = c("Thermo EASY-nLC", "Q Exactive - Orbitrap_MS"))
+  # no origin key
+```
+
+Now it is possible to subset or delete some rows with r tools.
+
+
+New rows can be added too. 
+
+It is necessary to fill in `term_verbose`, `handle_type` and `handle` in their specific column.
+`term_verbose` should be a short desription for the collumn as it will show up as key in the final table.
+If `handle` contains a string that just needs to be copied to the output table the collumn `handle_type` should contain `"literal"`. 
+
+If the row is meant to show an entry of the json `handle_type` needs to be `"list_path"` and `handle` should contain the path to the information in the flattened json after the group number. For example: The entry `flat_json[["Group.1.Instruments.Thermo EASY-nLC.InstrumentFriendName"]]` can be accessed with the value of handle = `"Instruments.Thermo EASY-nLC.InstrumentFriendName`"
+
diff --git a/vignettes/using_MARMoSET.Rmd b/vignettes/using_MARMoSET.Rmd
@@ -79,12 +79,12 @@ flat_json <- flatten_json(json = json)
 ``` 
 
 Since raw files include a huge amount of meta data and only several of this information is required by journals there is the need to sort out. Therefore a table linking which information is essential and where to find it in the flattened JSON is useful. Such a here refered to as 'term matching table' can be created with `term_matching_table()` by submitting two arguments. The first, `instrument_list` takes a vector with the names of the instruments represented in the JSON file.
-The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr'` stands for the requirements of the Journal of Proteome Research, `'mcp'` chooses the requirements of the Molecular and Cellular Proteomics.
+The second one, `origin_key` specifies which requirements should be met. If it stays empty, all journals are selected. While `'jpr_guidelines_ms'` stands for the requirements of the Journal of Proteome Research, `'miape'` for the Minimal Information about a Proteomics Experiment (MIAPE) from the Proteomics Standards Initiative.
 
 ```{r}
 term_matching_table <- create_term_match_table(
   instrument_list = c('Thermo EASY-nLC', 'Q Exactive - Orbitrap_MS'),
-  origin_key = 'jpr')
+  origin_key = 'jpr_guidelines_ms')
 ```
 
 The names of the instruments can be shown for each group with `instrument_names()` with the json and the group number as arguments.