Changelog

Source: NEWS.md

rRMSAnalyzer 3.0.0

New features

Plots

• compute_pval(): Compute statistics for C-score based on condition column
• plot_comparison_median(): Gives comparison of median C-scores between biological conditions
• plot_global_profile(): Gives a line plot of 2’Ome profile per conditions
• plot_sites_by_IQR(): Gives IQR or variance per site
• plot_stat(): Gives significant differential sites between experimental conditions

Others

• report_diff_sites() : Statistical analysis report of differential 2’O-methylation sites across experimental conditions.

Breaking change

• heatmap_annotated(): Addition of outliers annotation
• plot_counts_fraction(): Addition of human theoretical fraction threshold
• report_2ome_sites(): Addition of (i) variant site threshold computation, (ii) variation summary, (iii) conclusion part
• report_qc(): Addition of (i) summary tables of outliers and (ii) conclusion part

rRMSAnalyzer 2.0.1

New feature

• get_annotation(): get a dataframe with all current annotation in a RiboClass.

• remove_annotation(): Added a new parameter to select a subset of annotation to remove.

rRMSAnalyzer 2.0.0

• Added a NEWS.md file to track changes to the package.

Breaking change

• plot_PCA()has been renamed to plot_pca().

New features

Plots

• Correspondence analysis for samples’ counts : plot_coa()
• Heatmaps: plot_heatmap() and plot_heatmap_corr()
• “Simple” boxplots : boxplot_count() (by sample) and boxplot_cscores() (by site)
• Boxplot comparing c-score by condition for sites considered as differential plot_diff_sites()
• And others: plot_counts_env(), plot_counts_fraction()…

Error messages

• Error messages are now using Cli package for better clarity

• More error messages to help the users

Bugfixes & improvements

• compute_cscore() has drastically been improved in terms of performance

rRMSAnalyzer 1.0.0

First public version of rRMSAnalyzer 🥳 !

New features

Only main features will be shown here :

• C-score computation for all positions (compute_cscore())

• Technical biases adjustment with CombatSeq (adjust_bias())

• Principal Component Analysis (plot_PCA())