Package 'KOGMWU'

Title: Functional Summary and Meta-Analysis of Gene Expression Data
Description: Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Authors: Mikhail V. Matz
Maintainer: Mikhail V. Matz <[email protected]>
License: GPL-3
Version: 1.2
Built: 2024-10-30 03:19:25 UTC
Source: https://github.com/cran/KOGMWU

Help Index

Functional summary and meta-analysis of gene expression data

Description

Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.

Details

Package: KOGMWU
Type: Package
Version: 1.2
Date: 2019-02-19
License: GPL-3

The most important function is kog.mwu, which performs a series of Mann-Whitney U tests when given two data tables: one, containing measures of interest for each gene (for example, log fold-change), and another, listing the association of each gene with a KOG class. The KOG class annotations for a collection of genes can be obtained using eggNOG-mapper: http://eggnogdb.embl.de/#/app/emapper. To extract KOG annotations understood by this package out of the eggNOG-mapper output, see here: https://github.com/z0on/emapper_to_GOMWU_KOGMWU

Author(s)

Mikhail V. Matz

Maintainer: Mikhail V. Matz <[email protected]>

References

Dixon, G. B., Davies, S. W., Aglyamova, G. V., Meyer, E., Bay, L. K. and Matz, M. V. Genomic determinants of coral heat tolerance across latitudes. Science 2015, 348:1460-1462. eggNOG-mapper to obtain KOG annotations: http://eggnogdb.embl.de/#/app/emapper To extract KOG annotations from eggNOG-mapper output: https://github.com/z0on/emapper_to_GOMWU_KOGMWU

Examples

## Not run: 
data(adults.3dHeat.logFoldChange)
data(larvae.longTerm)
data(larvae.shortTerm)
data(gene2kog)

# Analyzing adult coral response to 3-day heat stress:
alfc.lth=kog.mwu(adults.3dHeat.logFoldChange,gene2kog) 
alfc.lth 

# coral larvae response to 5-day heat stress:
l.lth=kog.mwu(larvae.longTerm,gene2kog)
l.lth

# coral larvae response to 4-hour heat stress 
l.sth=kog.mwu(larvae.shortTerm,gene2kog)
l.sth

# compiling a table of delta-ranks to compare these results:
ktable=makeDeltaRanksTable(list("adults.long"=alfc.lth,"larvae.long"=l.lth,"larvae.short"=l.sth))

# Making a heatmap with hierarchical clustering trees: 
pheatmap(as.matrix(ktable),clustering_distance_cols="correlation") 

# exploring correlations between datasets
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor)
# p-values of these correlations in the upper panel:
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor.pval)

# plotting individual delta-rank correlations:
corrPlot(x="adults.long",y="larvae.long",ktable)
corrPlot(x="larvae.short",y="larvae.long",ktable)

## End(Not run)

Heat stress response of adult coral

Description

Acropora millepora (adult) response to three days of heat stress (31.5oC) log-fold-changes inferred using DESeq package from tag-based RNA-seq data.

Usage

data("adults.3dHeat.logFoldChange")

Format

A data frame with 44363 observations on the following 2 variables.

gene

gene id, a factor with 44363 levels

lfc

log fold-change, a numeric vector

Plots a pairwise correlation with linear regression line

Description

Plots Pearson's correlation between two columns in a dataframe, identified by column names. Also plots linear regression line and lists the correlation coefficient (r) and cor.test p-value.

Usage

corrPlot(x, y, data, ...)

Arguments

x

Name of the column to form X axis

y

Name of the column to form Y axis

data

The dataframe containing the two columns

...

Additional options for plot()

References

Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes.

KOG class annotations

Description

KOG class annotations for Acropora millerpoa transcriptome.

Usage

data("gene2kog")

Format

A data frame with 16175 observations on the following 2 variables.

V1

a factor with 16175 levels

V2

a factor with 23 levels

Source

https://dl.dropboxusercontent.com/u/37523721/amillepora_transcriptome_july2014.zip

References

Transcriptome assembly: Moya et al (2012), Mol Ecol 21:2440-2454. Transcriptome annotation: Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes.

One-tailed Fisher's exact test for KOG enrichment.

Description

Accessory function to kog.mwu()

Usage

kog.ft(gos)

Arguments

gos

A dataframe with three columns, 'seq' (gene id),'term' (KOG class) and 'value' (either 0 or 1, indicating significance).

Value

A dataframe with three columns: 'term', 'nseqs', 'pval' and 'padj'

Author(s)

Mikhail V. Matz

References

Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes.

Tests for KOG class enrichment.

Description

Determines whether some KOG classes are significantly enriched with up- or down-regulated genes (Mann-Whitney U test for continuous measure), or whether some KOG classes are significantly over-represented among "significant" genes (one-tailed Fisher's exact test for binary measure, 0 or 1).

Usage

kog.mwu(data, gene2kog, Alternative = "t")

Arguments

data

Two-column dataframe: gene id, measure of significance.

gene2kog

Two-column dataframe of gene annotations: gene id, KOG class. The gene list can be longer or shorter than the first column in the 'data' item.

Alternative

Tailedness of the Mann-Whitney U test: two-tailed ("t"), greater ("g"), or less ("l")

Details

The measure can be continuous (such as log fold change), in which case Mann-Whitney U test will be performed, or binary (1 or 0: significant or not), in which case Fisher's exact test will be performed. The KOG class annotations for a collection of genes can be obtained using Weizhng Li's lab KOG BLAST server.

Value

For continuous measure, a dataframe with three columns: term : KOG class nseqs : Number of genes in this class delta.rank : Difference between the mean rank of genes belonging to this KOG class and all other genes pval : p-value of the Mann-Whitney U test padj : p-value adjusted using Benjamini-Hochberg 1995 "fdr" procedure

For binary measure, the output is similar but does not contain the delta.rank column.

Author(s)

Mikhail V. Matz <[email protected]>

References

Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes. Weizhong Li's KOG BLAST server: http://weizhong-lab.ucsd.edu/metagenomic-analysis/server/kog/

Examples

## Not run: 
data(adults.3dHeat.logFoldChange)
data(larvae.longTerm)
data(larvae.shortTerm)
data(gene2kog)

# Analyzing adult coral response to 3-day heat stress:
alfc.lth=kog.mwu(adults.3dHeat.logFoldChange,gene2kog) 
alfc.lth 

# coral larvae response to 5-day heat stress:
l.lth=kog.mwu(larvae.longTerm,gene2kog)
l.lth

# coral larvae response to 4-hour heat stress 
l.sth=kog.mwu(larvae.shortTerm,gene2kog)
l.sth

# compiling a table of delta-ranks to compare these results:
ktable=makeDeltaRanksTable(list("adults.long"=alfc.lth,"larvae.long"=l.lth,"larvae.short"=l.sth))

# Making a heatmap with hierarchical clustering trees: 
pheatmap(as.matrix(ktable),clustering_distance_cols="correlation") 

# exploring correlations between datasets
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor)
# p-values of these correlations in the upper panel:
pairs(ktable, lower.panel = panel.smooth, upper.panel = panel.cor.pval)

# plotting individual delta-rank correlations:
corrPlot(x="adults.long",y="larvae.long",ktable)
corrPlot(x="larvae.short",y="larvae.long",ktable)

## End(Not run)

Mann-Whitney U test for KOG enrichment.

Description

Accessory function to kog.mwu()

Usage

kog.mwut(gos, Alternative = "t")

Arguments

gos

A dataframe with three columns, 'seq' (gene id),'term' (KOG class) and 'value' (continuous measure, such as log fold-change).

Alternative

Tailedness of the MWU test: two-tailed ("t"), greater-than ("g"), or less-than ("l")

Value

A dataframe with three columns: 'term', 'nseqs', 'delta.rank', 'pval' and 'padj'

Author(s)

Mikhail V. Matz

References

Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes.

Long-term heat stress response of coral larvae

Description

Acropora millepora (larvae) response to five days of heat stress (31.5oC) log-fold-changes inferred using DESeq package from tag-based RNA-seq data from Meyer et al Mol Ecol 2011,17:3599-3616

Usage

data("larvae.longTerm")

Format

A data frame with 31844 observations on the following 2 variables.

gene

gene id, a factor with 31844 levels

lfc

log fold-change, a numeric vector

Short-term heat stress response of coral larvae

Description

Acropora millepora (larvae) response to four hours of heat stress (31.5oC) log-fold-changes inferred using DESeq package from tag-based RNA-seq data from Meyer et al Mol Ecol 2011,17:3599-3616.

Usage

data("larvae.shortTerm")

Format

A data frame with 32307 observations on the following 2 variables.

gene

gene id, a factor with 32307 levels

lfc

log fold-change, a numeric vector

Make a combined delta-ranks table from several kog.mwu() results.

Description

Extracts delta ranks from several kog.mwu() result tables and combines them into a single dataframe for heat map plotting and correlation analysis.

Usage

makeDeltaRanksTable(ll)

Arguments

ll

A list of dataframes output by kog.mwu() function.

Value

A dataframe of delta-ranks (rows - KOG classes, columns - delta-ranks in different datasets).

Author(s)

Mikhail V. Matz

References

Dixon GB, Davies SW, Aglyamova GA, Meyer E, Bay LK and Matz MV (2015) Genomic determinants of coral heat tolerance across latitudes.

accessory function for pairs() to display Pearson correlations

Description

works as upper.panel or lower.panel argument of pairs() (package graphics).

Usage

panel.cor(x, y, digits=2, cex.cor)

Arguments

x

x element of the pairs() matrix

y

y element of the pairs() matrix

digits

number of non-zero digits to leave at the end

cex.cor

scaling factor for displayed text

References

cannibalized from an example in ?pairs (package graphics)

accessory function for pairs() to display pvalue of the Pearson correlation

Description

works as upper.panel or lower.panel argument of pairs() (package graphics). Displays pvalues better than 0.1.

Usage

panel.cor.pval(x, y, digits = 2, cex.cor, p.cut=0.1)

Arguments

x

x element of the pairs() matrix

y

y element of the pairs() matrix

digits

number of non-zero digits to leave at the end

cex.cor

scaling factor for displayed text

p.cut

p-value cutoff

References

cannibalized from an example in ?pairs (package graphics)