This Publication Page is meant to only contain information specific to the publication it’s describing. Supplemental Methods that may be reused for another publication are supposed to go to the respective Experiment Page.
Possible examples for non-reuseable information that could go on this page:
Supplemental results
Supplemental figures
Download links to supplemental videos, e.g. on Zenodo
Data processing methods that are specific to the publication’s research question
Programming code for pulling the information on samples or individual scans is at the bottom of the page. Editing the samples required happens not here, but in the Input_mice.csv file. Do not edit the code below the demarcated line, to avoid breaking the functionality.
Supplemental Results
Here you can enter information about your results in text (with markdown format)
Links to protocol pages
Code
# | warning: false library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Code
# This code chunk gathers the rows from tables specified in the inputsinputs <-read.csv(file ='input_mice.csv')# Use data type and facility information to find the path to the relevant protocolsfolders <-unique(cbind(inputs$Data_type,inputs$Facility_name_mon_YYYY)) # find table's unique variations of experiment type and facilitypaths <-as.data.frame(file.path(apply(folders, 1, paste, collapse = .Platform$file.sep), 'protocols')) #put together the paths colnames(paths) <-'Protocols'# Add HTML code so that it becomes a clickable link paths$Protocols <-paste0('<a href=\'',paths$Protocols,'\' target=\'_blank\'>', paths$Protocols,'</a>')file.path(here::here(), apply(folders, 1, paste, collapse = .Platform$file.sep), 'protocols')
# | warning: false library(dplyr)# Take the relevant rows from the tables specified in inputsdata_used <-list()for (i in1:nrow(inputs)){ row_filepath <-file.path(here::here(), 'experiments',inputs$Data_type[i], inputs$Facility_name_mon_YYYY[i], inputs$Metadata_file[i]) data_used[[i]] <-read.csv(row_filepath) %>%filter(SubjectID == inputs$SubjectID[i]) # read table and filter subject}# Gather list elements in a table data_used <-do.call(rbind,data_used) data_used$Subject_pubID <- inputs$Subject_pubID # Add additional input column # add the subject index for publication as first variabledata_used <-relocate(data_used, Subject_pubID, .before =1)#Display table DT::datatable(data_used, filter ='top')