Package 'glycanr'

Title: Tools for Analysing N-Glycan Data
Description: Useful utilities in N-glycan data analysis. This package tries to fill the gap in N-glycan data analysis by providing easy to use functions for basic operations on data (see <https://en.wikipedia.org/wiki/Glycomics> for more details on Glycomics). At the moment 'glycanr' is mostly oriented to data obtained by UPLC (Ultra Performance Liquid Chromatography) and LCMS (Liquid chromatography–mass spectrometry) analysis of Plasma and IgG glycome.
Authors: Ivo Ugrina [aut, cre, cph], Lucija Klaric [aut], Alyce Russell [aut], Frano Vuckovic [aut]
Maintainer: Ivo Ugrina <[email protected]>
License: MIT + file LICENSE
Version: 0.4.0
Built: 2025-02-21 03:45:17 UTC
Source: https://github.com/iugrina/glycanr

Help Index

Discover outliers in glycan data

Description

Returns outliers within every glycan structure

Usage

glyco.outliers(data, group = NULL, outlier.function = NULL, alpha = 1.5)

Arguments

data

data frame in long format containing glycan measurements
group

a possible grouping parameter on which stratification of data should be conducted. It should be a name of one of the columns in dataframe data and of type factor.
outlier.function

a function that checks for outliers in a vector. Receives one parameter representing a vector and returns logical vector indicating outliers.
alpha

If outlier.function parameter is set to NULL outliers are calculated as those points that are greater than upper quartile + alpha * IQR (interquartile range) or lower than lower quartile - alpha * IQR (interquartile range). If parameter outlier.function is not NULL parameter alpha is not used.

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values

Value

Returns a data.frame with outliers

Author(s)

Ivo Ugrina

Examples

data(mpiu)

glyco.outliers(mpiu)

# outliers per plate
glyco.outliers(mpiu, group="Plate")

Plot data representing Glycans in boxplot or violin mode using ggplot2

Description

This function constructs standard plots in exploratory analysis of N-Glycans.

Usage

glyco.plot(
  data,
  collapse = TRUE,
  violin = FALSE,
  group = NULL,
  all = TRUE,
  p.adjust.method = "holm",
  print.p.values = TRUE,
  log.transform = FALSE,
  glyco.names = NULL
)

Arguments

data

data frame which holds columns representing Glycans. These column names must start with 'GP'.
collapse

should Glycans be presented in one facet (default) or with more facets (one per Glycan).
violin

should Glycans be presented in a boxplot (default) or violin format.
group

this a possible grouping parameter on which stratification of data should be conducted. It should be a name of one of the columns in dataframe data and of type factor.
all

should all of the variables (default) be presented in the plot or only those that have significant p-values. This variable is meaningful only when group is not NULL since the testing of differences is conducted between different groups represented by group variable. If group has only 2 levels then Mann-Whitney-Wilcoxon (wilcox.test) test is conducted. Otherwise, Kruskal-Wallis test is conducted (kruskal.test). Obtained p-values are adjusted to multiple testing with p.adjust.
p.adjust.method

method used for adjustment of p-values to multiple testing. Variable p.adjust.method must be an element of p.adjust.methods.
print.p.values

should p-values be printed on plots
log.transform

should Glycans be log transform prior to plotting.
glyco.names

names of columns that represent glycan data. If NULL all columns starting with 'GP' in their names will be used

Value

Returns a list consisting of p-values, adjusted p-vales and the plot.

Author(s)

Ivo Ugrina

Examples

devAskNewPage(TRUE)
exampleData <- data.frame(ID=1:100, GP1=runif(100),
  GP2=rexp(100,0.2), GP3=rgamma(100, 3),
  Plate=factor(sample(1:2,100,replace=TRUE)))
glyco.plot(exampleData)
glyco.plot(exampleData, group='Plate', collapse=FALSE, log=TRUE)

Derived traits for Glycan peaks in IgG for LCMS

Description

Calculates values of derived traits for Glycan peaks in IgG for LCMS

Usage

ildt(data = NULL, method = "2014", print.exp.names = FALSE)

Arguments

data

data frame that holds columns representing Glycans.
method

year of the derived traits definition. By default 2014.
print.exp.names

If TRUE return expected column names representing glycans.

Details

Calculates derived traits from basic glycan peaks. User can choose which definition of the derived traits he will use (see references for different versions/definitions of derived traits).

Value

Returns the data frame with derived traits

Author(s)

Ivo Ugrina

References

Jennifer E. Huffman et al. (2014) "Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research*" doi:10.1074/mcp.M113.037465

Translate names between computer readable and human readable for derived traits of IgG with LCMS

Description

Translates names between computer readable and human readable for derived traits of IgG with LCMS

Usage

ildt.translate(orignames, to = "inverse", method = "2014")

Arguments

orignames

vector; type string
to

type of translation. If inverse is used everything will be translated. For computer names will be translated to computer readable, and for human names will be translated to human readable.
method

year of the derived traits definition. By default 2014.

Details

User can choose which definition of the derived traits he will use (see references for different versions/definitions of derived traits).

Value

Returns a character vector with original and translated names

Author(s)

Ivo Ugrina

References

Jennifer E. Huffman et al. (2014) "Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research*" doi:10.1074/mcp.M113.037465

Derived traits for Glycan peaks in IgG for UPLC

Description

Calculates values of derived traits for Glycan peaks in IgG for UPLC

Usage

iudt(data = NULL, method = "2014", print.exp.names = FALSE)

Arguments

data

data frame that holds columns representing Glycans. These column names should start with 'GP'.
method

year of the derived traits definition. By default 2014.
print.exp.names

If TRUE return expected column names representing glycans.

Details

Calculates derived traits from basic glycan peaks. User can choose which definition of the derived traits he will use (see references for different versions/definitions of derived traits).

Value

Returns the data frame with derived traits

Author(s)

Ivo Ugrina, Frano Vučković

References

Jennifer E. Huffman et al. (2014) "Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research*" doi:10.1074/mcp.M113.037465

Translate names between computer readable and human readable for derived traits of IgG with UPLC

Description

Translates names between computer readable and human readable for derived traits of IgG with UPLC

Usage

iudt.translate(orignames, to = "inverse", method = "2014")

Arguments

orignames

vector; type string
to

type of translation. If inverse is used everything will be translated. For computer names will be translated to computer readable, and for human names will be translated to human readable.
method

year of the derived traits definition. By default 2014.

Details

User can choose which definition of the derived traits he will use (see references for different versions/definitions of derived traits).

Value

Returns a character vector with original and translated names

Author(s)

Ivo Ugrina

References

Jennifer E. Huffman et al. (2014) "Comparative Performance of Four Methods for High-throughput Glycosylation Analysis of Immunoglobulin G in Genetic and Epidemiological Research*" doi:10.1074/mcp.M113.037465

Median Normalization of glycan data

Description

Returns glycans normalized with Median Normalization approach.

Usage

mediannorm(d, grouping = FALSE)

Arguments

d

data frame in long format containing glycan measurements
grouping

should data be normalized per groups

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values
and if the grouping argument is TRUE it should also have column:
- groups - representing groupings (e.g. IgG1, IgG2 and IgG4)

Value

Returns a data.frame with original glycan values substituted by normalized ones

Author(s)

Ivo Ugrina, Lucija Klarić

Examples

data(mpiu)
mpiun <- mediannorm(mpiu)
head(mpiun)

Median Quotient Normalization of glycan data

Description

Returns glycans normalized with Median Quotient Normalization approach.

Usage

medianquotientnorm(d, grouping = FALSE)

Arguments

d

data frame in long format containing glycan measurements
grouping

should data be normalized per groups

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values
and if the grouping argument is TRUE it should also have column:
- groups - representing groupings (e.g. IgG1, IgG2 and IgG4)

Value

Returns a data.frame with original glycan values substituted by normalized ones

Author(s)

Ivo Ugrina, Lucija Klarić

References

Dieterle F,Ross A, Schlotterbeck G, Senn H.:
Probabilistic Quotient Normalization as Robust Method to Account for Diluition of Complex Biological Mixtures. Application in 1H NMR Metabolomics.
Anal Chem 2006;78:4281-90.
doi:10.1021/ac051632c

Examples

data(mpiu)
mpiun <- medianquotientnorm(mpiu)
head(mpiun)

Multiple plates IgG UPLC data example

Description

This is an example of the data obtained by UPLC while analysing IgG.

Usage

mpiu

Format

An object of class data.frame with 13680 rows and 4 columns.

Normalized mpiu data

Description

This is an example of the normalized mpiu data. It is intended to be used for unit tests.

Usage

mpiunorm

Format

An object of class data.frame with 68400 rows and 5 columns.

Derived traits for Glycan peaks in PLASMA for HPLC

Description

Calculates values of derived traits for Glycan peaks in Plasma for HPLC

Usage

phdt(data = NULL, method = "2011", print.exp.names = FALSE)

Arguments

data

data frame that holds columns representing Glycans. These column names should start with 'GP'.
method

year of the derived traits definition. By default 2011.
print.exp.names

If TRUE return expected column names representing glycans.

Details

Calculates derived traits from basic glycan peaks. User can choose which definition of the derived traits he will use (see references for different versions/definitions of derived traits).

Value

Returns the data frame with derived traits

Author(s)

Ivo Ugrina, Lucija Klarić

References

Lu et al. (2011) "Screening Novel Biomarkers for Metabolic Syndrome by Profiling Human Plasma N-Glycans in Chinese Han and Croatian Populations" doi:10.1021/pr2004067 Irena Trbojevic-Akmacic et al. "Plasma N-glycome composition associates with chronic low back pain" doi:10.1016/j.bbagen.2018.07.003

Quantile Normalization of glycan data

Description

Returns glycans normalized with Quantile Normalization approach.

Usage

quantilenorm(d, grouping = FALSE, transpose = FALSE)

Arguments

d

data frame in long format containing glycan measurements
grouping

should data be normalized per groups
transpose

transpose the data prior to normalization

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values
and if the grouping argument is TRUE it should also have column:
- groups - representing groupings (e.g. IgG1, IgG2 and IgG4)

Value

Returns a data.frame with original glycan values substituted by normalized ones

Author(s)

Ivo Ugrina, Lucija Klarić

References

Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P.:
A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance.
Bioinformatics 19(2), p. 185-193, 2003.
doi:10.1093/bioinformatics/19.2.185

Examples

data(mpiu)
if(requireNamespace("preprocessCore", quietly=TRUE)){
  mpiun <- quantilenorm(mpiu)
  head(mpiun)

  # transpose (change) subjects and measurements
  mpiunt <- quantilenorm(mpiu, transpose=TRUE)
  head(mpiunt)
}

Reference Peak Normalization of glycan data

Description

Returns glycans normalized with Reference Peak Normalization approach.

Usage

refpeaknorm(d, grouping = FALSE, peak = NULL)

Arguments

d

data frame in long format containing glycan measurements
grouping

should data be normalized per groups
peak

glycan name to use as the reference peak. If NULL peak with maximal value (summed through all samples) is used

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values
and if the grouping argument is TRUE it should also have column:
- groups - representing groupings (e.g. IgG1, IgG2 and IgG4)

Value

Returns a data.frame with original glycan values substituted by normalized ones

Author(s)

Ivo Ugrina, Lucija Klarić

Examples

data(mpiu)
mpiun <- refpeaknorm(mpiu)
head(mpiun)

Total Area Normalization of glycan data

Description

Returns glycans normalized with Total Area Normalization approach.

Usage

tanorm(d, grouping = FALSE)

Arguments

d

data frame in long format containing glycan measurements
grouping

should data be normalized per groups

Details

Input data frame should have at least the following three columns:
- gid - representing a unique name of a sample
- glycan - representing glycan names
- value - representing measured values
and if the grouping argument is TRUE it should also have column:
- groups - representing groupings (e.g. IgG1, IgG2 and IgG4)

Value

Returns a data.frame with original glycan values substituted by normalized ones

Author(s)

Ivo Ugrina

Examples

data(mpiu)
mpiun <- tanorm(mpiu)
head(mpiun)