GCalignR 1.0.3

• Added fill = TRUE as a parameter in utils::read.table when reading data from text within internal functions. Loading GC data with utils::read.table failed in cases of missing values in a column (i.e. empty). This is the correct behaviour as missing data should always be coded explicitly by ‘NA’
• Tibbles are now coerced to data frames
• Added a new boolean parameter remove_empty for the main function align_chromatograms. If samples are empty (ie. no peak) this parameter allows to remove those cases from the dataset to avoid problems in post-hoc analyses. By default FALSE, ie. all but the blank samples are kept.
• Added a new boolean parameter permute for the functions align_chromatograms and align_peaks. This allows to change the default behaviour of random permutation of samples during the alignment and might be useful if exact replication is needed.

GCalignR 1.0.2

• The accompanying manuscript is published https://doi.org/10.1371/journal.pone.0198311 and the citation has been added to GCalignR
• The function beta read_empower2 allows to import HPLC data that has been generated using the EMPOWER 2 software

GCalignR 1.0.1

Bugfixes

• A bugfix was applied for handling multiple blanks correctly.
• Progressbars are removed in non-interactive R sessions

GCalignR 1.0.0

New functions implemented

• choose_optimal_reference offers an automatism to pick suitable references.
• draw_chromatograms allows to represent a peak list in form of chromatogram.
• remove_blanksallows to get rid of peaks that represent contamination after aligning a dataset
• remove_singletons allows to remove single peaks from the dataset after aligning
• merge_redundant_rows allows to merge rows that were not recognised as redundant during the alignment by increasing the threshold value for the evaluation of similarity

Algorithm

• Using pbapply, we implemented progress bars to inform the user about the progress and the estimated running time of intermediate steps in the alignment of peak lists.
• By implementing more efficient code, we were able to speed up the processing, especially picking references is faster by an order of magnitude.
• Retention times are not rounded to two decimals anymore. Calculations still capture a precision of two decimals for computational reasons.
• Within the aligned results, retention times correspond to the input values. Linear adjustments are only used internally and are documented within the Logfile accessible in the output.
• Reference samples that are used for the coarse alignment of retention times can be picked using a novel algorithm that determines the average similarity across the dataset.

warning messages

• Warnings addressing formatting issues are now more explicit and partly rephrased to avoid ambiguity.

Plots

• Added horizontal axis to barplots summarising peak numbers in plot.GCalign.
• Changed to more prominent colours in binary heatmaps with gc_heatmap.
• The function draw_chromatograms was added as another visualisation tool.

Vignettes

• We included a second vignette that explains the algorithm and the supported data in detail.

Documentation

• Helpfiles were rewritten to enhance clarity.