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@jpquast jpquast released this 17 Feb 17:42
· 153 commits to master since this release
v0.7.0
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Learn about vigilant mode.

New features

  • correct_lip_for_abundance() was added. It corrects LiP-peptides for changes in protein abundance and calculates their significance using a t-test. The function is based on the MSstatsLiP package developed by the Vitek Lab. Big thanks to @FehrAaron for implementing it!
  • qc_cvs() received a new argument called max_cv that specifies the maximum CV that should be included in the plot.
  • peptide_profile_plot() received a new argument called complete_sample. If set to TRUE, each protein gets assigned all sample names that are found in the input data. This ensures that the plot always contains all samples on the x-axis even if there are no measured intensities for a specific sample. The default is FALSE, which is the original behaviour of the function.
  • volcano_plot() received the colour argument that allows the user to provide custom colours for points.
  • Increased the speed of find_peptide() and assign_peptide_type() by only computing on the smallest possible subset of data before joining back to the original data frame.
  • calculate_treatment_enrichment() can now be applied on data frames with multiple different groups. The enrichment will be calculated for each group separately. If the data is plotted, each group is displayed in a separate facet. The group is provided to the new group argument.
  • qc_pca(): If the condition argument is numeric a colour gradient is used instead.

Bug fixes

  • volcano_plot() now also works interactively if there are no significant hits.
  • fetch_chebi(): fixed an issue caused by na_if() that changed its behaviour after the recent dplyr update.
  • qc_proteome_coverage(): fixed the label order of fractions of proteins detected and not detected in the proteome. Fixes issue #194.
  • calculate_protein_abundance() now correctly retains columns if for_plot = TRUE. Previously the columns to retain were not joined considering the precursor column, which lead to duplications of information where it did not belong. Fixes issue #197.
  • fetch_kegg() now returns the pathway name correctly again.
  • qc_intensity_distribution(), qc_median_intensities(), qc_charge_states(), qc_contaminants(), qc_missed_cleavages(), qc_peptide_type(), qc_ids(): If the provided sample column is of type factor, the level order won't be overwritten anymore.
    *fit_drc_4p(): If there are no correlations an empty data frame is returned to prevent errors in parallel_fit_drc_4p().
  • calculate_sequence_coverage() does not fail anymore if a protein only contains NA peptide sequences.
  • qc_sequence_coverage() does not return a plot anymore if plot = FALSE. This fixes issue #207.
  • qc_data_completeness() if sample was of type factor the function did not properly facet the data when the digestion argument was provided. Now we filter out all 0% completeness values that come from factor levels that are not present in subsetted data.

Contributors

FehrAaron
Assets 2
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