GCalignR 1.0.7

GCalignR 1.0.6

Bugfix in choose_optimal_reference that always selected the first sample as a reference. Thanks to Heberto del Rio who pointed this out on https://github.com/mottensmann/GCalignR/issues/27

Speedboost when setting max_diff_peak2mean = 0: In this special case there is no need to use a time-consuming iterative approach but peaks can be sorted simply based on absolute values. This is implemented in two steps. (1) Across all samples, unique retention times are extracted, sorted in increasing temporal order and written to a template data frame. (2) For each sample, peaks are matched to the corresponding row of the template data frame.
Small bug fixed that caused problems when plotting x-axis labels in gc_heatmap.
Added a test for detecting inconsistently ordered retention times within samples. Retention times are expected in increasing order, starting with the lowest number. If this assumption is violated, retention times are reordered and a warning is shown.

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 (i.e.. no peak) this parameter allows to remove those cases from the dataset to avoid problems in post-hoc analyses. By default FALSE, i.e.. 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.

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

Bugfixes

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

Vignettes

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

Documentation