Package 'func2vis'

Title: Clean and Visualize Over Expression Results from 'ConsensusPathDB'
Description: Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale.
Authors: Raghvendra Mall [aut, cre]
Maintainer: Raghvendra Mall <[email protected]>
License: GPL (>= 3)
Version: 1.0-3
Built: 2025-01-25 04:19:54 UTC
Source: https://github.com/cran/func2vis

Help Index

Clean Gene Ontologies (GO) Terms

Description

Clean set of enriched goterms obtained from 'ConsensusPathDB' for gene set overexpression analysis. We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl.

Usage

clean_go_terms(df_case_vs_ctrl, df_goterms)

Arguments

df_case_vs_ctrl

Data frame which has at least 2 columns: <gene,fc>. Here gene represents the set of genes which are differentially expressed between case and control. Here fc represents the fold-change value for each gene.

df_goterms

The tab-separated data frame with the goterms information obtained after performing gene set overexpression analysis using 'ConsensusPathDB'.

Value

Returns clean enriched GO terms data frame.

Note

[email protected]

Author(s)

Raghvendra Mall

See Also

See Also as clean_pc, plot_go_terms

Examples

data("t.tests.treatment.sign")
data("enriched_goterms")
revised_goterms <- clean_go_terms(df_case_vs_ctrl=t.tests.treatment.sign,
                                  df_goterms = enriched_goterms)
print(head(revised_goterms))

Clean Enriched Pathways

Description

Clean set of enriched pathways obtained from 'ConsensusPathDB' for gene set overexpression analysis. We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl. We cluster pathways based on similarity of gene set using igraph's walktrap clustering algorithm. Within each cluster, pathways are ordered by most to least significant pathway in terms of p-values.

Usage

clean_pathways(df_case_vs_ctrl, df_pathway)

Arguments

df_case_vs_ctrl

Data frame which has at least 2 columns: <gene,fc>. Here gene represents the set of genes which are differentially expressed between case and control. Here fc represents the fold-change value for each gene.
df_pathway

The tab-separated data frame with the pathways information obtained after performing gene set overexpression analysis using 'ConsensusPathDB'.

Value

Returns clean enriched pathways data frame. The data frame has an additional column clusters highlighting the cluster to which each enriched pathway belongs.

Note

[email protected]

Author(s)

Raghvendra Mall

See Also

clean_go_terms, clean_pc

Examples

data("t.tests.treatment.sign")
data("enriched_pathways")
revised_pathway <- clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,
                                  df_pathway = enriched_pathways)
print(head(revised_pathway))

Clean Enriched Protein Complexes

Description

Clean set of enriched protein complexes obtained from 'ConsensusPathDB' for gene set overexpression analysis. We also append two columns indicating the number of up-regulated and number of down-regulated genes based on fold change information available in data frame case_vs_ctrl.

Usage

clean_pc(df_case_vs_ctrl,df_pc)

Arguments

df_case_vs_ctrl

Data frame which has at least 2 columns: <gene,fc>. Here gene represents the set of genes which are differentially expressed between case and control. Here fc represents the fold-change value for each gene.

df_pc

The tab-separated data frame with the protein complexes information obtained after performing gene set overexpression analysis using 'ConsensusPathDB'.

Value

Returns clean enriched protein complexes data frame.

Note

[email protected]

Author(s)

Raghvendra Mall

See Also

See Also as clean_go_terms, plot_go_terms

Examples

data("t.tests.treatment.sign")
data("enriched_pc")
revised_pc <- clean_pc(df_case_vs_ctrl=t.tests.treatment.sign,
                                  df_pc = enriched_pc)
print(head(revised_pc))

Sample Enriched Gene Ontologies (GO) Terms

Description

This dataset highlights enriched gene ontologies (GO) terms identified by using ConsensusPathDB while performing overexpression analysis for a sample set of genes.

Usage

data("enriched_goterms")

References

Kamburov, A., Stelzl, U., Lehrach, H. and Herwig, R., 2013. The ConsensusPathDB interaction database: 2013 update. Nucleic acids research, 41(D1), pp.D793-D800.

Examples

data(enriched_goterms)
## maybe str(enriched_goterms) ;

Sample Enriched Pathways

Description

This dataset highlights enriched pathways identified by using 'ConsensusPathDB' while performing overexpression analysis for a sample set of genes.

Usage

data("enriched_pathways")

References

Kamburov, A., Stelzl, U., Lehrach, H. and Herwig, R., 2013. The ConsensusPathDB interaction database: 2013 update. Nucleic acids research, 41(D1), pp.D793-D800.

Examples

data(enriched_pathways)
## maybe str(enriched_pathways) ;

Sample Enriched Protein Complexes

Description

This dataset highlights protein complexes identified by using 'ConsensusPathDB' while performing overexpression analysis for a sample set of genes.

Usage

data("enriched_pc")

References

Kamburov, A., Stelzl, U., Lehrach, H. and Herwig, R., 2013. The ConsensusPathDB interaction database: 2013 update. Nucleic acids research, 41(D1), pp.D793-D800.

Examples

data(enriched_pc)
## maybe str(enriched_pc) ;

Bupple Plot for GO Terms

Description

Make a bubble plot for significantly enriched Gene Ontologies (GO) Terms obtained after performing gene set overexpression analysis using 'ConsensusPathDB'.

Usage

plot_go_terms(df_goterms, total_no_background_genes, 
              negative_log_10_p_value_cutoff, max_overlap)

Arguments

df_goterms

The tab-separated data frame with the GO terms information obtained after performing gene set overexpression analysis using 'ConsensusPathDB'.

total_no_background_genes

Total no of genes in the background set.

negative_log_10_p_value_cutoff

The threshold on -log10(pvalue) to be used to identify the GO terms to be highlighted in the plot.

max_overlap

To prevent overlapping text, set this paramater to a number >= 20.

Details

Plots the significantly enriched molecular function (m), cellular components (c) and biological processes (b) obtained via ConsensusPathDB.

Value

Returns a bubble plot of type ggplot.

Note

[email protected]

Author(s)

Raghvendra Mall

Examples

data("enriched_goterms")
g <- plot_go_terms(df_goterms = enriched_goterms, negative_log_10_p_value_cutoff=17)
g

Plot clean enriched pathways as a bubble plot

Description

Make a bubble plot of clean enriched pathways obtained from 'ConsensusPathDB' by performing gene set overexpression analysis. Colours represent the clusters to which each pathway belongs. You need to run the function clean_pathways to obtain the input data frame.

Usage

plot_pathways(final_df_pathway, total_no_background_genes, fontsize)

Arguments

final_df_pathway

Clean and clustered pathways obtained using clean_pathways.

total_no_background_genes

Total no of genes in the background set.

fontsize

Font size of the pathways to be displayed on y-axis.

Value

Returns a bubble plot of type ggplot. Colours represent the clusters to which each pathway belongs.

Note

[email protected]

Author(s)

Raghvendra Mall

See Also

See Also as clean_pathways, plot_pathways_stacked_barplot, plot_go_terms

Examples

data("t.tests.treatment.sign")
data("enriched_pathways")
revised_pathway <- clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,
                                  df_pathway = enriched_pathways)
p <- plot_pathways(revised_pathway)
p

Stacked Barplot of Cleaned Pathways

Description

Make a stacked barplot like the one available in Ingenuity Pathway Analysis highlighting percentage of up, down and non-differentially expressed genes in the set of clean enriched pathways obtained from 'ConsensusPathDB' by performing gene set overexpression analysis. You need to run the function clean_pathways to obtain the input data frame

Usage

plot_pathways_stacked_barplot(final_df_pathway)

Arguments

final_df_pathway

Clean and clustered pathways obtained using clean_pathways.

Value

Returns a stacked barplot of type ggplot.

Note

[email protected]

Author(s)

Raghvendra Mall

See Also

clean_pathways, plot_go_terms

Examples

data("t.tests.treatment.sign")
data("enriched_pathways")
revised_pathway <- clean_pathways(df_case_vs_ctrl=t.tests.treatment.sign,
                                  df_pathway = enriched_pathways)
p <- plot_pathways_stacked_barplot(revised_pathway)
p

List of differentially expressed genes

Description

Consist of list of differentially expressed genes (DEG) with fold-change information i.e. up and down regulated genes between case and control.

Usage

data("t.tests.treatment.sign")

Format

A data frame with 1820 observations on the following 8 variables.

gene

a character vector

p.value

a numeric vector

p.value.fdr

a numeric vector

fc

a numeric vector

mean.A

a numeric vector

mean.B

a numeric vector

sd.A

a numeric vector

sd.B

a numeric vector

Examples

data(t.tests.treatment.sign)
## maybe str(t.tests.treatment.sign) ;