Glycomics is rapidly emerging field in high-throughput biology that aims to systematically study glycan structures of a given protein, cell type or organic system. As within other high-throughput methods in biology (microarrays, metabolomics, proteomics), accuracy of high-throughput methods is highly affected by complicated experimental procedures leading to differences between replicates and the existence of batch effects, among others.

Glycanr package tries to fill the gap in N-glycan data analysis by providing easy to use functions for glycomics analysis. At the moment it is mostly oriented to data obtained by UPLC and LCMS analysis of Plasma and IgG glycome.

Targeted audience

As commented, the package is currently oriented to data analysts working with Plasma and IgG glycome data obtained by UPLC and LCMS analysis. More correctly, it is oriented to the analysts with the data resembling data from papers like Selman et al., Pučić et al. or Stöckmann et al..

The package helps in calculating different quantities like derived traits and preparing the data for analysts doing different statistical or computational modelling of the N-glycan data like in Vučković et al., Krištić et al or Barrios et al..

Working with glycanr package

At the moment glycanr package consists of 12 functions and 2 data sets.

Functions glyco.plot and glyco.outliers are handy functions to quickly explore the data. See the “plots” vignette for more information.

Functions ildt, iudt and phdt return derived traits obtained from basic glycans. They have a standardised input depending on the definition of derived traits (e.g. derived traits as defined in Huffman et al.). Names of the columns representing glycans for a specific definition can be obtained with the argument print.exp.names. Columns of the data frame used to store glycan measurement should be named according to the expected names. Subset of the expected names can also be used if some glycans weren't measured.

Functions ildt.translate and iudt.translate are useful to translate between computer friendly and human friendly names.

Functions mediannorm, medianquotientnorm, tanorm, refpeaknorm and quantilenorm are used to normalize the data with specific normalization techniques. See the “normalization” vignette for more info.

Some functions have a default scheme for column names representing glycans (e.g. columns starting with “GP” present glycan data) and additional argument glyco.names to change the default.