My OME-TIFF mask text fields are all number-like strings, e.g., "001", "002", "003". The LabWorksheet.txt file has the correct values in the "segment" field, "001", "002", "003", etc. However, when the LabWorksheet is read in using readLabWorkshet(), https://github.com/Nanostring-Biostats/SpatialOmicsOverlay/blob/bcff923c03a3bd3d7460e327ea3f7f02e72081a5/R/utils.R#L57, the column is imported as fully numerical, e.g., "1", "2", "3". This makes the string matching within annotMatching() fail. This only happens when all "segment" values in the LabWorkseet file are number-like strings. If I add in a dummy string "forcestring" for an NTC, the entire column is read in as type character. This way, the "001", etc., format is maintained and the annotMatching() function returns a non-empty data structure.
My OME-TIFF mask text fields are all number-like strings, e.g., "001", "002", "003". The LabWorksheet.txt file has the correct values in the "segment" field, "001", "002", "003", etc. However, when the LabWorksheet is read in using
readLabWorkshet(), https://github.com/Nanostring-Biostats/SpatialOmicsOverlay/blob/bcff923c03a3bd3d7460e327ea3f7f02e72081a5/R/utils.R#L57, the column is imported as fully numerical, e.g., "1", "2", "3". This makes the string matching withinannotMatching()fail. This only happens when all "segment" values in the LabWorkseet file are number-like strings. If I add in a dummy string "forcestring" for an NTC, the entire column is read in as type character. This way, the "001", etc., format is maintained and theannotMatching()function returns a non-empty data structure.