README.md
In vpalombo/PANEV: PaNeV: an R package for a pathway-based network visualization
Pathway Network Visualizer (PaNeV)
Package: PANEV
Type: R Package
Version: 1.0
Date: 2019-12-13
Author: Valentino Palombo, Marco Milanesi, Gabriella Sferra, Stefano Capomaccio, Sandy Sgorlon, Mariasilvia D'Andrea
Maintainer: Valentino Palombo valentino.palombo@gmail.com
Description: PANEV is an R package set for pathway-based network gene visualization. Based on information available on KEGG, it maps and visualizes genes within a network of upstream and downstream-connected pathways (from 1 to n levels). The graph helps to interpret functional profiles of cluster of genes.
License: Artistic-2.0
Abstract
Nowadays, the increase of data generated by genomic and transcriptomic analyses requires strategies to solve the challenge of data mining. In this sense, the use of tools for pathway analysis and/or functional enrichment is de facto standard for the post analytic interrogation. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and, ultimately, identify functional candidate genes affecting the trait under investigation.
This manual describes the Pathways Network Visualizer (PANEV) R package. The idea is to create a 'functional' network between a set of main pathways of interest (first level - 1L) and n levels of upstream/downstream interconnected pathways, to identify which genes, from a given list of genes of interest, are involved. The network refers to connected pathways and genes participating in sequential steps and thus presumed to affect the phenotype/condition of interest.
More in detail, PANEV is based on information available on KEGG database. The network is created based on the number (n) of levels required for the investigation. Starting from the 1L chosen by the user, PANEV uncovers the connections between 1L and upstream/downstream pathways (from 1 to n levels). Then, once the framework of pathways is created, PANEV highlights genes, among a list of provided ones, belonging to the multiple investigated levels and provides a pathways/genes-based network visualization. The highlighted genes may be considered good candidates for the trait/condition of interest because supported by functional evidence. Overall, PANEV in combination with classical functional enrichment analysis and/or pathway analysis can facilitate the interpretation of complex biological processes.
Since the package relies on KEGG, PANEV returns meaningful results only for genes with functional information available. The package is able to analyze genomic or transcriptomic outcomes for all species annotated in KEGG. Access to KEGG repositories has specific copyright conditions (https://www.kegg.jp/kegg/legal.html). PANEV uses the KEGGREST package (https://bioconductor.org/packages/release/bioc/html/KEGGREST.html) functions to download individual pathway graphs and data files through API or HTTP access, which is freely available for academic and non-commercial uses.
Installation
This manual focuses on PANEV v.1.0. PANEV requires the pre-installation of the ‘devtools’ package from CRAN (https://cran.r-project.org/package=devtools). Once satisfied the requirement, the R scripts and package described in this manual can be downloaded and installed from the GitHub link (https://github.com/vpalombo/panev) using:
#install devtools
install.packages("devtools") #if needed
#install PANEV
library("devtools")
install_github("vpalombo/PANEV")
To load the package into the R environment (R ≥ 3.5.0) type:
library("PANEV")
PANEV functions and workflow
PANEV functions could be divided in two different steps: data preparation and data analyses.
Since PANEV interrogates biomaRt and/or KEGG database, a working internet connection is required to properly run PANEV functions.
Example files
The trial data files used in the help pages can be created with the command:
# Copy the example data in the current working directory
panev.example()
The files comprise:
- for the genomic dataset:
- entrez_genelist.txt and ensembl_genelist.txt. Two examples of genes list files for genomic data preparation with entrez ID and ensembl ID annotation, respectively;
- data.txt. An example of a gene list file properly formatted, ready to run PANEV main functions.
- For the transcriptomic dataset:
- entrez_expr_genelist.txt and ensembl_expr_genelist.txt. Two examples of gene list files for the transcriptomic data preparation with corresponding FC and p-value in entrez ID and ensembl ID annotation, respectively;
- exprdata.txt. An example of differentially expressed genes (DEG) list properly formatted and ready to run PANEV main functions;
- expr_listPath.txt. An example file containing the list of the main pathways of interest (1L) with the corresponding expression estimated scores [i.e. flux values obtained by Dynamic Impact Approach (Bionaz et al., 2012)].
Input files used for the validation in the reference publication can be created using the command:
# Copy the example data in the current working directory
panev.example(type="validation")
In this case, the files comprise:
-
genelist_annotated_qiu2014.txt. The gene list from a publicly available dataset containing 166 out of 171 'novel' genes discovered by a gene-based association study on human type 1 diabetes mellitus - T1DM (Qiu et al., 2014), properly formatted and ready to run PANEV functions;
-
genelist_enrichment_qui2014.txt. The gene list containing all the 452 genes discovered by Qiu et al. (2014);
-
genelist_expr_Levy2012.txt. Part of the previous gene list with the corresponding FC and p-values, from a publicly available expression results obtained by Levy et al. (2012) and used by Qiu et al. (2014);
-
pathlist_expr_Levy2012.txt. The list of 1L pathways.
Data preparation
The preparation of a properly formatted list of genes and main pathways of interest (1L) is the first step before running the main PANEV functions.
Gene list preparation
Since PANEV relies on KEGG database information, the entrez gene identifiers are needed.
To enhance the user experience, two specific functions, panev.dataPreparation and panev.exprdataPreparation, are provided to retrieve the correct ID annotation for genomic and transcriptomic data, respectively. These functions are based on the biomaRt R package, which uses the ensembl.org website resource, which allows up-to-date identifiers switching.
The ensembl correct organism code is needed to run properly data preparation functions: these codes are obtainable with the panev.biomartSpecies command.
To create a list of all the organisms available for biomaRt annotation:
# Create a list of all the available organisms for biomaRt annotation
list <- panev.biomartSpecies(string = NULL)
# The list of all the species available for gene id conversion and data preparation was created!
# Remember to use the correct organism code for the relative PANEV functions.
To obtain the list of the available code for a specific organism:
# Look for a specific organism matching a search string for biomaRt annotation
list <- panev.biomartSpecies(string = "cow")
# The list of available species matched your string was created!
# Remember to use the correct organism code for relative PANEV functions.
list
# organism_code description version
# 1 btaurus_gene_ensembl Cow genes (ARS-UCD1.2) ARS-UCD1.2
biomart.species.bos <- as.character(list[1,1])
list <- panev.biomartSpecies(string = "pig")
# The list of available species matched your string was created!
# Remember to use the correct organism code for relative PANEV functions.
list
# organism_code description version
# 1 caperea_gene_ensembl Brazilian guinea pig genes (CavAp1.0) CavAp1.0
# 2 cporcellus_gene_ensembl Guinea Pig genes (Cavpor3.0) Cavpor3.0
# 3 mnemestrina_gene_ensembl Pig-tailed macaque genes (Mnem_1.0) Mnem_1.0
# 4 sscrofa_gene_ensembl Pig genes (Sscrofa11.1) Sscrofa11.1
biomart.species.sus <- as.character(list[4,1])
The use of data preparation functions is recommended but not mandatory. In fact, it must be emphasized that the correct performance depends on the availability to biomaRt access to data for the desired species and on its correct entrez and/or ensembl ID annotation. For this reason, we strongly suggest to double-check all-possible gene annotations.
For genomic dataset analysis
For genomic data, PANEV requires a specific format of dataset. The gene list of interest must be provided as '.txt' file, stored in the working directory, containing three columns labeled as ensembl_gene_id, entrezgene and external_gene_name, respectively.
Users can create a properly formatted dataset from a simple gene list using the panev.dataPreparation function. This function will convert the single gene list from ensembl to entrez gene ID (or vice versa), and it will add, at the same time, the gene symbol.
It is recommended to run this function before using the panev.network command.
Preparation of formatted gene list from ensembl gene ID:
# Example of gene list from genomic dataset with ensembl id
genelist <- read.table("ensembl_genelist.txt")
head(genelist)
# V1
# 1 ENSBTAG00000000039
# 2 ENSBTAG00000000040
# 3 ENSBTAG00000000042
# 4 ENSBTAG00000000044
# 5 ENSBTAG00000001521
# 6 ENSBTAG00000001522
# Prepare the dataset for panev.network function converting gene name from ensembl to entrez id
genelist.converted <- panev.dataPreparation(in.file = "ensembl_genelist.txt",
gene_id = "ensembl",
biomart.species = biomart.species.bos)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 37 out of 47 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.converted)
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
Preparation of a formatted gene list from entrez gene ID:
# Example of gene list from genomic dataset with entrez id
genelist <- read.table("entrez_genelist.txt")
head(genelist)
# V1
# 1 104564307
# 2 100313156
# 3 100297382
# 4 100125314
# 5 782391
# 6 781828
# Preparation of the dataset for panev.network function, converting gene name from entrez to ensembl id
genelist.converted <- panev.dataPreparation(in.file = "entrez_genelist.txt",
gene_id = "entrez",
biomart.species = biomart.species.bos)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from entrez ID to ensembl ID ...
# DONE
# n. 34 out of 34 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.converted)
# entrezgene ensembl_gene_id external_gene_name
# 1 100125314 ENSBTAG00000046173 ALG12
# 2 100297382 ENSBTAG00000021566 PAX2
# 3 100313156 ENSBTAG00000044643 bta-mir-2346
# 4 104564307 ENSBTAG00000039738 TMIGD3
# 5 280880 ENSBTAG00000013898 NPB
# 6 281073 ENSBTAG00000007591 CHUK
For transcriptomic dataset analysis
For transcriptomic data, PANEV requires a specific format dataset. The gene list, containing the differentially expressed genes (DEG) of interest, must be provided as '.txt' file, stored in the working directory, with five columns labelled as ensembl_gene_id, entrezgene, external_gene_name, FC (fold change) and pvalue.
Users can create a PANEV formatted dataset using the panev.exprdataPreparation function. This function will convert the DEG list with the corresponding FC and p-value from ensembl to entrez gene ID (or vice versa), and add at the same time gene symbols.
It is recommended to run this function before using the panev.exprnetwork command.
Preparation of formatted DEG list from ensembl gene ID:
# Example of gene list from transcriptomic dataset with ensembl id
genelist <- read.table("ensembl_expr_genelist.txt", header=TRUE)
head(genelist)
# ensembl_gene_id FC pvalue
# 1 ENSSSCG00000000042 3.377911 0.000712043
# 2 ENSSSCG00000011956 -1.00335002 0.093817937
# 3 ENSSSCG00000027351 -1.90736265 0.012180974
# 4 ENSSSCG00000026626 1.360528698 0.083137308
# 5 ENSSSCG00000011621 2.824720966 0.000442323
# 6 ENSSSCG00000000042 3.3779108 0.000712043
# Preparation of the dataset for panev.network function, converting gene name from ensembl to entrez id
genelist.expr.converted <- panev.exprdataPreparation(in.file = "ensembl_expr_genelist.txt",
gene_id = "ensembl",
biomart.species = biomart.species.sus)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 3708 of your 3774 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.expr.converted)
# ensembl_gene_id FC pvalue entrezgene external_gene_name
# 1 ENSSSCG00000000002 -4.992506 0.00002740 100520739 GTSE1
# 2 ENSSSCG00000000006 3.737968329 0.01211484 397239 PPARA
# 3 ENSSSCG00000000037 1.877643135 0.000200513 100523962 ARFGAP3
# 4 ENSSSCG00000000038 -1.201390429 0.052669253 100524254 CYB5R3
# 5 ENSSSCG00000000046 2.121964954 0.005759356 397687 CYP2D25
# 6 ENSSSCG00000000058 -1.426235589 0.001286279 100155420 SNU13
Preparation of formatted DEG list from entrez gene ID:
# Example of gene list from transcriptomic dataset with entrez id
genelist <- read.table("entrez_expr_genelist.txt", header=TRUE)
head(genelist)
# entrezgene FC pvalue
# 1 397239 3.737968 0.012114844
# 2 100523962 1.877643 0.000200513
# 3 100524254 -1.201390 0.052669253
# 4 397687 2.121965 0.005759356
# 5 100155420 -1.426236 0.001286279
# 6 100154200 -1.492203 0.000286301
# Preparation of the dataset for panev.network function, converting gene name from entrez to ensembl id
genelist.expr.converted <- panev.exprdataPreparation(in.file = "entrez_expr_genelist.txt",
gene_id = "entrez",
biomart.species = biomart.species.sus)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 3531 of your 3531 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.expr.converted)
# entrezgene FC pvalue ensembl_gene_id external_gene_name
# 1 387287 64.592010 0.000002350 ENSSSCG00000003601 HCRTR1
# 2 396564 1.248483 0.036568915 ENSSSCG00000012817 FUNDC2
# 3 396568 4.089142 0.006518320 ENSSSCG00000011802 KNG1
# 4 396574 -9.125993 0.006317556 ENSSSCG00000013387
# 5 396575 1.679256 0.002075938 ENSSSCG00000000272 SP1
# 6 396590 3.067011 0.003039118 ENSSSCG00000008857 MSMO1
Pathways list preparation
The list of main pathways of interest (first level pathways - 1L), coded as KEGG path ID, is mandatory to run the main PANEV functions. The list of all available pathways on KEGG is obtainable by the panev.pathList function, that returns a dataframe containing two columns labeled as path_description and path_ID, respectively. This function helps to retrieve the KEGG path ID if unknown.
To obtain the list of all available pathways in KEGG:
# Create a list of all available pathways for PANEV
list <- panev.pathList(string = NULL)
# The list of all available pathways was created!
# Remember to use the correct path Id(s) for relative PANEV functions.
To obtain the list of the available pathway(s) matching a search string:
# Look for a specific pathway(s) for PANEV, matching your search string
list <- panev.pathList(string = "lipid")
# The list of the pathway(s), matching your string, was created!
# Remember to use the correct path Id(s) for relative PANEV functions.
list
# path_description path_ID
# 1 Glycerolipid metabolism path:map00561
# 2 Glycerophospholipid metabolism path:map00564
# 3 Ether lipid metabolism path:map00565
# 4 Sphingolipid metabolism path:map00600
# 5 Glycosphingolipid biosynthesis - lacto and neolacto series path:map00601
# 6 Glycosphingolipid biosynthesis - globo and isoglobo series path:map00603
# 7 Glycosphingolipid biosynthesis - ganglio series path:map00604
# 8 Sphingolipid signaling pathway path:map04071
# 9 Antidyslipidemic agents path:map07052
For genomic dataset analysis
In the case of genomic dataset analysis, the 1L must be provided as a vector of PANEV pathways identifiers (path_ID) obtained with the panev.pathList command.
# Create a vector of the main pathways of interest (1L)
FL.gene <- c("path:map00061", "path:map00062", "path:map00071", "path:map00072")
For transcriptomic dataset analysis
In the case of transcriptomic dataset analysis, the list of pathways of interest must be provided as a '.txt' file, stored in the working directory. The file must contain the list of *path_ID, obtained with the panev.pathList* command, and relative expression estimated scores, obtained by a gene set enrichment analysis [e.g. flux value (Bionaz et al., 2012)].
# Example of the main pathways of interest (1L) for transcriptomic data analysis
FL.expr <- read.table("expr_listPath.txt", header=TRUE)
FL.expr
# path_ID value
# 1 path:map00010 324.22879
# 2 path:map00020 -21.31287
# 3 path:map00071 385.73774
Species code detection
The KEGG correct organism code is needed to run properly data analyses functions. The code is obtainable with panev.speciesCode.
To create a list of all available organisms in KEGG:
# Create a list of all available organisms in KEGG
list <- panev.speciesCode(string = NULL)
# The list of all species available for PANEV analysis was created!
# Remember to use the correct organisms code for relative PANEV functions.
# Number of available species in KEGG to date:
length(list$panev_code)
# [1] 5913
head(list)
# panev_code species
# hsa Homo sapiens (human)
# ptr Pan troglodytes (chimpanzee)
# pps Pan paniscus (bonobo)
# ggo Gorilla gorilla gorilla (western lowland gorilla)
# pon Pongo abelii (Sumatran orangutan)
# nle Nomascus leucogenys (northern white-cheeked gibbon)
To obtain the list of available code for a specific organism:
# Look for a specific organism in KEGG matching a search string
list <- panev.speciesCode(string = "bos")
# The list of available species matching your string was created!
# Remember to use the correct organism code for relative PANEV functions.
head(list)
# species panev_code
# 1 Bos taurus (cow) bta
# 2 Bos mutus (wild yak) bom
# 3 Bos indicus (zebu cattle) biu
# 4 Malassezia globosa mgl
# 5 Bosea sp. PAMC 26642 bop
# 6 Bosea sp. RAC05 bos
KEGG.species.bos <- as.character(list[1,2])
list <- panev.speciesCode(string = "pig")
# The list of available species matching your string was created!
# Remember to use the correct organism code for relative PANEV functions.
head(list)
# species panev_code
# 1 Sus scrofa (pig) ssc
# 2 Columba livia (rock pigeon) clv
# 3 Cajanus cajan (pigeon pea) ccaj
# 4 Pigmentiphaga sp. H8 pig
# 5 Desulfovibrio piger dpg
# 6 Salipiger profundus tpro
KEGG.species.sus <- as.character(list[1,2])
Data analyses
PANEV analysis and visualization
PANEV automates the process of the functional classification of the genes of interest, detecting the genes inside a functional network of pathways. The network created from selected main pathways (1L) and upstream/downstream dependent ones, it helps to interpret functional profiles of genes, underlying complex biological processes.
Genomic dataset
For genomic data, a functional network is created, taking into account upstream and downstream pathways connected with 1L pathways. The network is formed by pathways involved in sequential steps, connected at more levels (from 1 to n), as required by the user. After that, PANEV highlights the genes inside the network and it provides pathways/genes-based network visualization. The genes highlighted may be considered good candidates for the trait/condition of interest.
The panev.network function requires:
-
a properly formatted gene list;
-
a vector of 1L pathways;
-
species code;
-
the number of levels.
Please see the above "Data preparation" section to understand how to prepare the correct input files.
### Run if it is necessary ###
# Copy the example data file 'data.txt' in your current working directory
# panev.example()
# Head of input file "data.txt"
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
# Perform PANEV
panev.network(in.file = "data.txt",
out.file = "example",
species = KEGG.species.bos,
FL = FL.gene,
levels = 2)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# PANEV analysis completed and relative '.txt' files exported!
#
# Preparing PANEV diagram visualization!
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Well done! Diagram visualization was created and exported.
A new folder is created and files resulting from the analysis are saved inside.
The function generates n text files, with n equal to the number of levels required for the investigation. Every single .txt file is a table containing the genes and the related pathways for a specific level.
#Summary of PANEV results at 1L level obtained with the example of dataset
genes.1L <- read.table("PANEV_RESULTS_FA/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSBTAG00000015980 281152 FASN Fatty acid biosynthesis path:bta00061
# 2 ENSBTAG00000015178 505355 ECI2 Fatty acid degradation path:bta00071
Along with the tabular format, the function gets also the diagram visualization of PANEV results, saved in an interactive '.html' file. The diagram allows zooming in on all the contents for optimal readability.
The 'html' diagram is interactive. Users can select nodes by ID/label (e.g FASN gene).
Transcriptomic dataset
For transcriptomic data, PANEV takes into account any possible connection within/between a list of pathways of interest and a list of differentially expressed genes (DEG).
The panev.exprnetwork function requires:
-
a properly formatted DEG list;
-
a properly formatted list of pathways of interest;
-
the species code;
-
a p-value cut-off (for filtering subset of genes in the DEG list).
Please see the above "Data preparation" section to understand how to prepare the correct input files.
### Run if it is necessary ###
# Copy the example data files "exprdata.txt" and 'expr_listPath.txt' in your current working directory
# panev.example()
# Head of "exprdata.txt" file
# ensembl_gene_id FC pvalue entrezgene external_gene_name
# 1 ENSSSCG00000000002 -4.992506 0.00002740 100520739 GTSE1
# 2 ENSSSCG00000000006 3.737968 0.012114844 397239 PPARA
# 3 ENSSSCG00000000037 1.877643 0.000200513 100523962 ARFGAP3
# 4 ENSSSCG00000000038 -1.201390 0.052669253 100524254 CYB5R3
# 5 ENSSSCG00000000046 2.121965 0.005759356 397687 CYP2D25
# 6 ENSSSCG00000000058 -1.426236 0.001286279 100155420 SNU13
# Head of "expr_listPath.txt" file
# path_ID value
# 1 path:map00010 324.22879
# 2 path:map00020 -21.31287
# 3 path:map00071 385.73774
# Perform PANEV on transcriptomic dataset
panev.exprnetwork(in.file = "exprdata.txt",
path.file = "expr_listPath.txt",
out.file = "example",
species = KEGG.species.sus,
pvalue = 0.05)
# Input file imported!
#
# Pathway input file imported!
#
# Gene list specified... and correct!
#
# Your path list colnames are correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways required!
# n. 3132 of 3774 genes passed the p-value filtering.
#
# Well done! Diagram visualization was created and exported.
A new folder is created and files resulting from the analysis are saved inside.
The function generates a diagram visualization, taking into account all possible connections within/between the 1L pathways and DEGs. The diagram helps to interpret the results obtained from gene expression experiments showing the nodes (i.e. genes and pathways) colored according to their FCs and pathway expression estimated scores [e.g. flux values (Bionaz et al., 2012)], respectively.
The classification into upregulated/downregulated genes/pathways is done considering the top FC/estimated score values.
| gene/pathway classification | with FC/estimated score value |
|:---------------------------:|:-----------------------------------------------------------:|
| low up/downregulated | <25% of top up/downregulated gene/pathway |
| moderate up/downregulated | >= 25% and < 50% of top up/downregulated gene/pathway |
| high up/downregulated | >= 50% and < 75% of top up/downregulated gene/pathway |
| strong up/downregulated | >= 75% of top up/downregulated gene/pathway |
The 'html' diagram is interactive and allows zooming in on all the contents for optimal readability.
![Fig.4: An example of the 'html' file with the network-based visualization of PANEV result considering an expression dataset. The circles represent the genes coloured based on their fold change (FC) values. The diamonds represent the pathways of interest coloured based on their expression estimated scores [i.e. flux values obtained by Dynamic Impact Approach (Bionaz et al., 2012)]. The diagram shows the relationships among genes and pathways and allows identifying functionally related entities with possible coordinated expression changes.](https://github.com/vpalombo/PANEV/tree/master/vignettes/images/fig4.jpg)
The 'html' diagram is also interactive. Users can select nodes by ID/label (e.g. ALDH2 gene).
Enrichment analysis
PANEV provides also the ancillary functions panev.stats.enrichment() and panev.network.enrichment() to perform a gene enrichment analysis based on a hypergeometric test (one-sided Fisher exact test), as described by Simoes and Emmert-Streib (2012). In particular, while the former function allows the user to search against the default KEGG database, the latter allows computing the pathway enrichment of the genes highlighted by PANEV using the pathways generated in the network as a background.
The results are a series of '.txt' files with specific enrichment analysis results and with general descriptive information about gene and pathway occurrences.
More in details:
-
the *<out.file>_enrichment.txt* file contains the enrichment analysis result. A p-value is calculated for each pathway to estimate the probability of significant over-representation.
-
the *<out.file>_enrichment.txt* file contains the enrichment analysis result of the genes highlighted by PANEV using the pathways generated in the network as a background. A p-value is calculated for each pathway to estimate the probability of significant over-representation;
-
the *<out.file>_GxP.txt* file contains the list of pathways with gene occurrences.
-
the *<out.file>_PxG.txt* file contains the list of genes with pathway occurrences.
These files are useful to explore the list of interesting pathways, which might be possible candidates for further investigation, particularly when no previous restrictive biological assumptions on trait/condition of interest are available.
### Run if it is necessary ###
# Copy the example data file 'data.txt' in your current working directory
# panev.example()
# Head of input file "data.txt"
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
# Perform the enrichment analysis
panev.stats.enrichment(in.file = "data.txt",
out.file = "example",
species = KEGG.species.bos)
# Input file is imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Enrichment analysis started ...
# and results exported!
#
# Gene per pathway(s) table created and exported!
#
# Pathway per gene(s) table created and exported!
# <out.file>_enrichment.txt file
FA_enrich <- read.table("example_enrichment.txt", header = T)
head(FA_enrich)
# pathway_ID n_genes all_genes pvalue padj pathway_name
# 1 path:bta01100 8 1466 0.002440842 0.6758655 Metabolic pathways
# 2 path:bta04920 2 72 0.007663162 0.6758655 Adipocytokine signaling pathway
# 3 path:bta05212 2 74 0.008080410 0.6758655 Pancreatic cancer
# 4 path:bta04662 2 75 0.008292828 0.6758655 B cell receptor signaling pathway
# 5 path:bta00440 1 6 0.011094948 0.7233906 Phosphonate and phosphinate metabolism
# 6 path:bta04152 2 123 0.021290522 1.0000000 AMPK signaling pathway
# <out.file>_GxP.txt file
FA_GxP <- read.table("example_GxP.txt", header = T)
head(FA_GxP)
# n_genes pathway_name pathway_ID
# 1 8 Metabolic pathways path:bta01100
# 2 2 MAPK signaling pathway path:bta04010
# 3 2 Ras signaling pathway path:bta04014
# 4 2 FoxO signaling pathway path:bta04068
# 5 2 PI3K-Akt signaling pathway path:bta04151
# 6 2 AMPK signaling pathway path:bta04152
# <out.file>_PxG.txt file
FA_PxG <- read.table("example_PxG.txt", header = T)
head(FA_PxG)
# n_pathways entrez_gene_id ensembl_gene_id gene_symbol
# 1 42 281073 ENSBTAG00000007591 CHUK
# 2 23 619066 ENSBTAG00000022927 RAC3
# 3 12 369023 ENSBTAG00000016253 G6PC3
# 4 8 616871 ENSBTAG00000001521 UQCRB
# 5 5 281152 ENSBTAG00000015980 FASN
# 6 3 510274 ENSBTAG00000001868 PCYT2
# Perform the network enrichment analysis
panev.network.enrichment(in.file = in.file,
out.file = "example",
species = KEGG.species.bos,
FL = FL.gene,
levels = 3)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified...
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Enrichment analysis started ...
# and results exported!
# <out.file>_net.enrichment.txt file
# FA_net.enrich <- read.table("example_net.enrichment.txt", header = T)
# pathway_ID n_genes all_genes pvalue padj pathway_name
#1 path:bta00061 1 18 0.3105344 1 Fatty acid biosynthesis
#2 path:bta00500 1 32 0.4866391 1 Starch and sucrose metabolism
#3 path:bta00760 1 39 0.5578855 1 Nicotinate and nicotinamide metabolism
#4 path:bta00071 1 42 0.5854757 1 Fatty acid degradation
#5 path:bta00330 1 48 0.6358667 1 Arginine and proline metabolism
#6 path:bta00010 1 64 0.7435386 1 Glycolysis / Gluconeogenesis
Validation in publication
A publicly available data set from a study on human type 1 diabetes mellitus - T1DM (Qiu et al., 2014) was used as testing set for PANEV in our publication.
In the reference study, the authors carried out a gene-based genome-wide association analysis and identified 452 significant genes. Among these genes, 171 were newly identified for type 1 diabetes mellitus, since ignored in previous studies or in literature. The authors further supported the significance of 53 out of 171 discovered genes with replication studies and differential expression studies.
In particular, the authors reported four non-HLA genes (RASIP1, STRN4, BCAR1 and MYL2) and three HLA genes (FYN, HLA-J and PPP1R11) as validated by both the replication and differential expression studies. These genes represent the main result discussed by the authors.
We performed PANEV considering the list of 171 newly genes, to verify the possible contribution of PANEV visualization for candidate genes identification and, more broadly, for high-throughput data interpretation.
Genomic dataset analysis
'The Type I diabetes mellitus', 'Insulin resistance' and 'AGE-RAGE signaling pathway in diabetic complications' were chosen as 1L pathways, since clearly connected to the trait of interest (Greenbaum, 2002; Ramasamy et al., 2005).
Considering the complexity of the trait, the analysis was performed up to the third level (Field et al., 1997).
After data preparation, only 5 out of 171 genes had no entrez gene identifier. This list was used to run the PANEV functions.
### Run if it is necessary ###
# Copy the example files used as validation set in the publication in the current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_annotated_qiu2014.txt"
# Head of in.file
# external_gene_name entrezgene ensembl_gene_id
# 1 ADAD1 132612 ENSG00000164113
# 2 ASCL2 430 ENSG00000183734
# 3 ATF7IP 55729 ENSG00000171681
# 4 BAK1 578 ENSG00000030110
# 5 BCAR1 9564 ENSG00000050820
# 6 BCL2A1 597 ENSG00000140379
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
FL = c("path:map04940", "path:map04931", "path:map04933")
levels = 3
# Run the PANEV function
panev.network(in.file = in.file,
out.file = "validation",
species = species,
FL = FL,
levels = levels)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# PANEV analysis completed and relative '.txt' files exported!
#
# Preparing PANEV diagram visualization!
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Well done! Diagram visualization was created and exported.
Overall PANEV results obtained from the validation dataset are in line with the reference study outcomes (Qiu et al., 2014), confirming the effectiveness of our visualization approach.
In particular, 4 out of 7 genes validated both the replication and differential expression studies (Qui et al. 2014) were highlighted by PANEV: PTPN11, BCAR1, MYL2 and FYN. The other three genes (RASIP1, STRN4 and HLA-J) were not detected by PANEV, since, although present in KEGG database, they were not assigned yet to any pathway.
Along with these genes, PANEV highlighted also other interesting genes (ITPR3, BAK1, IL10, HMGB1 and MICA) not discussed by Qui et al. (2014), since validated only by the differential expression or by the replication studies only.
#Summary of PANEV results considering first level (1L) pathways
genes.1L <- read.table("PANEV_RESULTS_validation/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000179295 5781 PTPN11 Insulin resistance path:hsa04931
#Summary of PANEV results considering second level (2L) pathways
genes.2L <- read.table("PANEV_RESULTS_validation/2Lgenes.txt", header = TRUE)
genes.2L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000123374 1017 CDK2 PI3K-Akt signaling pathway path:hsa04151
# 2 ENSG00000096433 3710 ITPR3 Apoptosis path:hsa04210
# 3 ENSG00000030110 578 BAK1 Apoptosis path:hsa04210
# 4 ENSG00000140379 597 BCL2A1 Apoptosis path:hsa04210
# 5 ENSG00000010810 2534 FYN T cell receptor signaling pathway path:hsa04660
# 6 ENSG00000136634 3586 IL10 T cell receptor signaling pathway path:hsa04660
# 7 ENSG00000179295 5781 PTPN11 Adipocytokine signaling pathway path:hsa04920
# 8 ENSG00000101665 4092 SMAD7 TGF-beta signaling pathway path:hsa04350
#Summary of PANEV results considering third level (3L) pathways
genes.1L <- read.table("PANEV_RESULTS_validation/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000096433 3710 ITPR3 Calcium signaling pathway path:hsa04020
# 2 ENSG00000050820 9564 BCAR1 Chemokine signaling pathway path:hsa04062
# 3 ENSG00000140379 597 BCL2A1 NF-kappa B signaling pathway path:hsa04064
# 4 ENSG00000136634 3586 IL10 FoxO signaling pathway path:hsa04068
# 5 ENSG00000123374 1017 CDK2 FoxO signaling pathway path:hsa04068
# 6 ENSG00000096433 3710 ITPR3 Phosphatidylinositol signaling system path:hsa04070
# 7 ENSG00000123374 1017 CDK2 Cell cycle path:hsa04110
# 8 ENSG00000123374 1017 CDK2 p53 signaling pathway path:hsa04115
# 9 ENSG00000189403 3146 HMGB1 Autophagy - animal path:hsa04140
# 10 ENSG00000030110 578 BAK1 Protein processing in endoplasmic reticulum path:hsa04141
# 11 ENSG00000010810 2534 FYN Focal adhesion path:hsa04510
# 12 ENSG00000050820 9564 BCAR1 Focal adhesion path:hsa04510
# 13 ENSG00000111245 4633 MYL2 Focal adhesion path:hsa04510
# 14 ENSG00000099866 8174 MADCAM1 Cell adhesion molecules (CAMs) path:hsa04514
# 15 ENSG00000179295 5781 PTPN11 Jak-STAT signaling pathway path:hsa04630
# 16 ENSG00000136634 3586 IL10 Jak-STAT signaling pathway path:hsa04630
# 17 ENSG00000138378 6775 STAT4 Jak-STAT signaling pathway path:hsa04630
# 18 ENSG00000204520 100507436 MICA Natural killer cell mediated cytotoxicity path:hsa04650
# 19 ENSG00000010810 2534 FYN Natural killer cell mediated cytotoxicity path:hsa04650
# 20 ENSG00000179295 5781 PTPN11 Natural killer cell mediated cytotoxicity path:hsa04650
# 21 ENSG00000111245 4633 MYL2 Regulation of actin cytoskeleton path:hsa04810
# 22 ENSG00000050820 9564 BCAR1 Regulation of actin cytoskeleton path:hsa04810
PANEV highlighted also other genes not discussed in the reference study (Qiu et al., 2014) but reported in literature as being associated to the susceptibility to T1DM disease: CDK2 (Kim et al., 2017), MADCAM1 (Phillips et al., 2005), STAT4 (Bi et al., 2013), SMAD7 (Chen et al., 2011) and BCL2A1 (Beyan et al., 2010).
It is worth to note that, except for CDK2, all genes mentioned above refer to researches conducted before the reference study (Qiu at al., 2014).
Simultaneously, it is must be observed that some genes were not highlighted by PANEV, because:
-
they fell inside no-investigated pathway (BRAP, FUT2, GNS, HIPK1, NUPR1, OR2B3, HIST1H4E, HIST1H2BF, OR2B3, OR2B6, OR2J2, OR5V1, and SULT1A1 genes). About 8% of our investigated genes;
-
although present in KEGG database, they were not assigned yet to any pathway. About 44% of our investigated genes.
These drawbacks clearly represent the main PANEV limitation.
Enrichment analysis
In accordance with the reference study (Qui et al., 2014), we performed the functional annotation clustering analysis of the 452 identified T1DM genes, using the PANEV enrichment analysis function.
### Run if it is necessary ###
# Copy the example files used as a validation set in the publication in the current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_enrichment_qui2014.txt"
# Head of in.file
# external_gene_name entrezgene ensembl_gene_id
# 1 OLFML3 56944 not_available
# 2 HIPK1 204851 not_available
# 3 IL10 3586 not_available
# 4 NSL1 25936 not_available
# 5 FAM46B 115572 not_available
# 6 LHX9 56956 not_available
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
# Run the PANEV enrichment function
panev.stats.enrichment(in.file = in.file,
out.file = "validation",
species=species)
# Input file is imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Enrichment analysis started ...
# and results exported!
#
# Gene per pathway(s) table created and exported!
#
# Pathway per gene(s) table created and exported!
The results showed as the genes tend to be over-represented in immune diseases and immune system pathways, according to Qiu et al. (2014).
#Summary of PANEV enrichment result
enrichment.result <- read.table("validation_enrichment.txt", header = TRUE)
head(enrichment.result)
# pathway_ID n_genes all_genes pvalue padj pathway_name
# 1 path:hsa05322 26 133 1.338552e-22 4.403835e-20 Systemic lupus erythematosus
# 2 path:hsa05330 13 38 3.566961e-15 5.867652e-13 Allograft rejection
# 3 path:hsa04612 16 77 1.306151e-14 1.432412e-12 Antigen processing and presentation
# 4 path:hsa05320 14 53 1.763583e-14 1.450547e-12 Autoimmune thyroid disease
# 5 path:hsa04940 13 43 2.264972e-14 1.490352e-12 Type I diabetes mellitus
# 6 path:hsa05332 12 41 3.705738e-13 2.031980e-11 Graft-versus-host disease
Transcriptomic dataset analysis
By way of example, to explore the possibility to visualize gene expression values, we used in the publication the FC values obtained by Levy et al. (2012) study and considered by Qiu et al. (2014), as reference study for differential expression analysis validation.
Since the authors did not provide pathway estimated scores, we substituted these measures with gene occurrence values for each pathway of interest, obtained by PANEV enrichment analysis.
### Run if it is necessary ###
# Copy the example data files "genelist_expr_Levy2012.txt" and 'pathlist_expr_Levy2012.txt' in your current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_expr_Levy2012.txt"
#
# Head of "genelist_expr_Levy2012" file
# ensembl_gene_id external_gene_name entrezgene FC pvalue
# ENSG00000204252 HLA-DOA 3111 -0.535 0.000
# ENSG00000239457 HLA-DOB 3112 -0.017 0.955
# ENSG00000168384 HLA-DPA1 3113 -0.491 0.025
# ENSG00000206239 HLA-DQA1 3117 -0.846 0.000
# ENSG00000206237 HLA-DQB1 3119 -0.467 0.007
# ENSG00000204592 HLA-E 3133 0.298 0.014
#
path.file = "pathlist_expr_Levy2012.txt"
# Head of "pathlist_expr_Levy2012" file
# path_ID value
# path:map04514 16
# path:map04940 14
# path:map04151 6
# path:map04210 5
# path:map04630 5
# path:map04010 4
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
# Perform PANEV on transcriptomic dataset
panev.exprnetwork(in.file = in.file,
path.file = path.file,
out.file = "expression_data_validation",
species = species,
pvalue = 0.05)
# Input file imported!
#
# Pathway input file imported!
#
# Gene list specified... and correct!
#
# Your path list colnames are correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways required!
# n. 18 of 30 genes passed the p-value filtering.
#
# Well done! Diagram visualization was created and exported.
This PANEV visualization helped to interpret the results obtained from gene expression experiments by showing the nodes (i.e. genes) colored according to gene FC values. The diagram showed the relationships among genes and pathways and it allowed us to identify functionally related entities with possible coordinated expression changes.
Conclusions
PANEV represents an interesting visualization approach to reduce the complexity of the challenge of high-throughput data mining and identify candidate genes identification. PANEV, providing an integrated summary among pathways and genes of interest, facilitates the data interpretation.
The contribution of the PANEV tool could be significant not only for well-annotated species (i.e. Homo sapiens, Mus musculus) but also for all the organisms available in KEGG database. Although KEGG is a popular and constantly updated database for biological network information, the lack or incomplete information on KEGG could represent the main PANEV disadvantage. The effectiveness of PANEV analysis in terms of result coherency was confirmed by the validation study. In particular, PANEV produces timesaving advantages, pointing users to genes that are biologically involved with the investigated trait.
References
Beyan,H. et al. (2010) Monocyte gene-expression profiles associated with childhood-onset type 1 diabetes and disease risk: a study of identical twins. Diabetes, 59, 1751–1755. .
Bi,C. et al. (2013) Association study of STAT4 polymorphisms and type 1 diabetes in Northeastern Chinese Han population. Tissue Antigens, 81, 137–140. .
Bionaz,M. et al. (2012). A novel dynamic impact approach (DIA) for functional analysis of time-course omics studies: validation using the bovine mammary transcriptome. PLos One 7:e32455. .
Chen HY, Huang XR, Wang W, Li JH, Heuchel RL, Chung ACK, et al. The protective role of Smad7 in diabetic kidney disease: mechanism and therapeutic potential. Diabetes. 2011;60: 590-601.
Field,L.L. at al. (1997). Unravelling a complex trait: the genetics of insulin-dependent diabetes mellitus. Clin. Investig. Med. Med. Clin. Exp. 20:41–49.
Greenbaum, C.J. 2002. Insulin resistance in type 1 diabetes. Diabetes Metab. Res. Rev. 18:192–200. .
Kim,S.Y. et al. (2017) Loss of cyclin dependent kinase 2 in the pancreas links primary β-cell dysfunction to progressive depletion of β-cell mass and diabetes. J. Biol. Chem., .
Levy, H., X. Wang, M. Kaldunski, S. Jia, J. Kramer, S.J. Pavletich, M. Reske, T. Gessel, M. Yassai, M.W. Quasney, M.K. Dahmer, J. Gorski, and M.J. Hessner. 2012. Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes. Genes Immun. 13:593-604. .
Phillips,J.M. et al. (2005) MAdCAM-1 is needed for diabetes development mediated by the T cell clone, BDC-2·5. Immunology, 116, 525–531. .
Qiu,Y.-H. et al. (2014) Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis. J. Diabetes Investig., 5, 649–656. .
Ramasamy, R. et al. (2005). Advanced glycation end products and RAGE: a common thread in aging, diabetes, neurodegeneration, and inflammation. Glycobiology 15:16R-28R. .
Shi,A. et al. (2016) Genetic variants in vitamin D signaling pathways and risk of gestational diabetes mellitus. Oncotarget, 7, 67788–67795. .
Simoes, R. de M., and F. Emmert-Streib. 2012. Bagging statistical network inference from large-scale gene expression data. PLos One 7:e33624. .
vpalombo/PANEV documentation built on June 13, 2022, 1:11 p.m.
R Package Documentation
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Pathway Network Visualizer (PaNeV)
Package: PANEV
Type: R Package
Version: 1.0
Date: 2019-12-13
Author: Valentino Palombo, Marco Milanesi, Gabriella Sferra, Stefano Capomaccio, Sandy Sgorlon, Mariasilvia D'Andrea
Maintainer: Valentino Palombo valentino.palombo@gmail.com
Description: PANEV is an R package set for pathway-based network gene visualization. Based on information available on KEGG, it maps and visualizes genes within a network of upstream and downstream-connected pathways (from 1 to n levels). The graph helps to interpret functional profiles of cluster of genes.
License: Artistic-2.0
Abstract
Nowadays, the increase of data generated by genomic and transcriptomic analyses requires strategies to solve the challenge of data mining. In this sense, the use of tools for pathway analysis and/or functional enrichment is de facto standard for the post analytic interrogation. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and, ultimately, identify functional candidate genes affecting the trait under investigation.
This manual describes the Pathways Network Visualizer (PANEV) R package. The idea is to create a 'functional' network between a set of main pathways of interest (first level - 1L) and n levels of upstream/downstream interconnected pathways, to identify which genes, from a given list of genes of interest, are involved. The network refers to connected pathways and genes participating in sequential steps and thus presumed to affect the phenotype/condition of interest.
More in detail, PANEV is based on information available on KEGG database. The network is created based on the number (n) of levels required for the investigation. Starting from the 1L chosen by the user, PANEV uncovers the connections between 1L and upstream/downstream pathways (from 1 to n levels). Then, once the framework of pathways is created, PANEV highlights genes, among a list of provided ones, belonging to the multiple investigated levels and provides a pathways/genes-based network visualization. The highlighted genes may be considered good candidates for the trait/condition of interest because supported by functional evidence. Overall, PANEV in combination with classical functional enrichment analysis and/or pathway analysis can facilitate the interpretation of complex biological processes.
Since the package relies on KEGG, PANEV returns meaningful results only for genes with functional information available. The package is able to analyze genomic or transcriptomic outcomes for all species annotated in KEGG. Access to KEGG repositories has specific copyright conditions (https://www.kegg.jp/kegg/legal.html). PANEV uses the KEGGREST package (https://bioconductor.org/packages/release/bioc/html/KEGGREST.html) functions to download individual pathway graphs and data files through API or HTTP access, which is freely available for academic and non-commercial uses.
Installation
This manual focuses on PANEV v.1.0. PANEV requires the pre-installation of the ‘devtools’ package from CRAN (https://cran.r-project.org/package=devtools). Once satisfied the requirement, the R scripts and package described in this manual can be downloaded and installed from the GitHub link (https://github.com/vpalombo/panev) using:
#install devtools
install.packages("devtools") #if needed
#install PANEV
library("devtools")
install_github("vpalombo/PANEV")
To load the package into the R environment (R ≥ 3.5.0) type:
library("PANEV")
PANEV functions and workflow
PANEV functions could be divided in two different steps: data preparation and data analyses.
Since PANEV interrogates biomaRt and/or KEGG database, a working internet connection is required to properly run PANEV functions.
Example files
The trial data files used in the help pages can be created with the command:
# Copy the example data in the current working directory
panev.example()
The files comprise:
- for the genomic dataset:
- entrez_genelist.txt and ensembl_genelist.txt. Two examples of genes list files for genomic data preparation with entrez ID and ensembl ID annotation, respectively;
- data.txt. An example of a gene list file properly formatted, ready to run PANEV main functions.
- For the transcriptomic dataset:
- entrez_expr_genelist.txt and ensembl_expr_genelist.txt. Two examples of gene list files for the transcriptomic data preparation with corresponding FC and p-value in entrez ID and ensembl ID annotation, respectively;
- exprdata.txt. An example of differentially expressed genes (DEG) list properly formatted and ready to run PANEV main functions;
- expr_listPath.txt. An example file containing the list of the main pathways of interest (1L) with the corresponding expression estimated scores [i.e. flux values obtained by Dynamic Impact Approach (Bionaz et al., 2012)].
Input files used for the validation in the reference publication can be created using the command:
# Copy the example data in the current working directory
panev.example(type="validation")
In this case, the files comprise:
-
genelist_annotated_qiu2014.txt. The gene list from a publicly available dataset containing 166 out of 171 'novel' genes discovered by a gene-based association study on human type 1 diabetes mellitus - T1DM (Qiu et al., 2014), properly formatted and ready to run PANEV functions;
-
genelist_enrichment_qui2014.txt. The gene list containing all the 452 genes discovered by Qiu et al. (2014);
-
genelist_expr_Levy2012.txt. Part of the previous gene list with the corresponding FC and p-values, from a publicly available expression results obtained by Levy et al. (2012) and used by Qiu et al. (2014);
-
pathlist_expr_Levy2012.txt. The list of 1L pathways.
Data preparation
The preparation of a properly formatted list of genes and main pathways of interest (1L) is the first step before running the main PANEV functions.
Gene list preparation
Since PANEV relies on KEGG database information, the entrez gene identifiers are needed.
To enhance the user experience, two specific functions, panev.dataPreparation and panev.exprdataPreparation, are provided to retrieve the correct ID annotation for genomic and transcriptomic data, respectively. These functions are based on the biomaRt R package, which uses the ensembl.org website resource, which allows up-to-date identifiers switching.
The ensembl correct organism code is needed to run properly data preparation functions: these codes are obtainable with the panev.biomartSpecies command.
To create a list of all the organisms available for biomaRt annotation:
# Create a list of all the available organisms for biomaRt annotation
list <- panev.biomartSpecies(string = NULL)
# The list of all the species available for gene id conversion and data preparation was created!
# Remember to use the correct organism code for the relative PANEV functions.
To obtain the list of the available code for a specific organism:
# Look for a specific organism matching a search string for biomaRt annotation
list <- panev.biomartSpecies(string = "cow")
# The list of available species matched your string was created!
# Remember to use the correct organism code for relative PANEV functions.
list
# organism_code description version
# 1 btaurus_gene_ensembl Cow genes (ARS-UCD1.2) ARS-UCD1.2
biomart.species.bos <- as.character(list[1,1])
list <- panev.biomartSpecies(string = "pig")
# The list of available species matched your string was created!
# Remember to use the correct organism code for relative PANEV functions.
list
# organism_code description version
# 1 caperea_gene_ensembl Brazilian guinea pig genes (CavAp1.0) CavAp1.0
# 2 cporcellus_gene_ensembl Guinea Pig genes (Cavpor3.0) Cavpor3.0
# 3 mnemestrina_gene_ensembl Pig-tailed macaque genes (Mnem_1.0) Mnem_1.0
# 4 sscrofa_gene_ensembl Pig genes (Sscrofa11.1) Sscrofa11.1
biomart.species.sus <- as.character(list[4,1])
The use of data preparation functions is recommended but not mandatory. In fact, it must be emphasized that the correct performance depends on the availability to biomaRt access to data for the desired species and on its correct entrez and/or ensembl ID annotation. For this reason, we strongly suggest to double-check all-possible gene annotations.
For genomic dataset analysis
For genomic data, PANEV requires a specific format of dataset. The gene list of interest must be provided as '.txt' file, stored in the working directory, containing three columns labeled as ensembl_gene_id, entrezgene and external_gene_name, respectively.
Users can create a properly formatted dataset from a simple gene list using the panev.dataPreparation function. This function will convert the single gene list from ensembl to entrez gene ID (or vice versa), and it will add, at the same time, the gene symbol.
It is recommended to run this function before using the panev.network command.
Preparation of formatted gene list from ensembl gene ID:
# Example of gene list from genomic dataset with ensembl id
genelist <- read.table("ensembl_genelist.txt")
head(genelist)
# V1
# 1 ENSBTAG00000000039
# 2 ENSBTAG00000000040
# 3 ENSBTAG00000000042
# 4 ENSBTAG00000000044
# 5 ENSBTAG00000001521
# 6 ENSBTAG00000001522
# Prepare the dataset for panev.network function converting gene name from ensembl to entrez id
genelist.converted <- panev.dataPreparation(in.file = "ensembl_genelist.txt",
gene_id = "ensembl",
biomart.species = biomart.species.bos)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 37 out of 47 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.converted)
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
Preparation of a formatted gene list from entrez gene ID:
# Example of gene list from genomic dataset with entrez id
genelist <- read.table("entrez_genelist.txt")
head(genelist)
# V1
# 1 104564307
# 2 100313156
# 3 100297382
# 4 100125314
# 5 782391
# 6 781828
# Preparation of the dataset for panev.network function, converting gene name from entrez to ensembl id
genelist.converted <- panev.dataPreparation(in.file = "entrez_genelist.txt",
gene_id = "entrez",
biomart.species = biomart.species.bos)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from entrez ID to ensembl ID ...
# DONE
# n. 34 out of 34 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.converted)
# entrezgene ensembl_gene_id external_gene_name
# 1 100125314 ENSBTAG00000046173 ALG12
# 2 100297382 ENSBTAG00000021566 PAX2
# 3 100313156 ENSBTAG00000044643 bta-mir-2346
# 4 104564307 ENSBTAG00000039738 TMIGD3
# 5 280880 ENSBTAG00000013898 NPB
# 6 281073 ENSBTAG00000007591 CHUK
For transcriptomic dataset analysis
For transcriptomic data, PANEV requires a specific format dataset. The gene list, containing the differentially expressed genes (DEG) of interest, must be provided as '.txt' file, stored in the working directory, with five columns labelled as ensembl_gene_id, entrezgene, external_gene_name, FC (fold change) and pvalue.
Users can create a PANEV formatted dataset using the panev.exprdataPreparation function. This function will convert the DEG list with the corresponding FC and p-value from ensembl to entrez gene ID (or vice versa), and add at the same time gene symbols.
It is recommended to run this function before using the panev.exprnetwork command.
Preparation of formatted DEG list from ensembl gene ID:
# Example of gene list from transcriptomic dataset with ensembl id
genelist <- read.table("ensembl_expr_genelist.txt", header=TRUE)
head(genelist)
# ensembl_gene_id FC pvalue
# 1 ENSSSCG00000000042 3.377911 0.000712043
# 2 ENSSSCG00000011956 -1.00335002 0.093817937
# 3 ENSSSCG00000027351 -1.90736265 0.012180974
# 4 ENSSSCG00000026626 1.360528698 0.083137308
# 5 ENSSSCG00000011621 2.824720966 0.000442323
# 6 ENSSSCG00000000042 3.3779108 0.000712043
# Preparation of the dataset for panev.network function, converting gene name from ensembl to entrez id
genelist.expr.converted <- panev.exprdataPreparation(in.file = "ensembl_expr_genelist.txt",
gene_id = "ensembl",
biomart.species = biomart.species.sus)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 3708 of your 3774 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.expr.converted)
# ensembl_gene_id FC pvalue entrezgene external_gene_name
# 1 ENSSSCG00000000002 -4.992506 0.00002740 100520739 GTSE1
# 2 ENSSSCG00000000006 3.737968329 0.01211484 397239 PPARA
# 3 ENSSSCG00000000037 1.877643135 0.000200513 100523962 ARFGAP3
# 4 ENSSSCG00000000038 -1.201390429 0.052669253 100524254 CYB5R3
# 5 ENSSSCG00000000046 2.121964954 0.005759356 397687 CYP2D25
# 6 ENSSSCG00000000058 -1.426235589 0.001286279 100155420 SNU13
Preparation of formatted DEG list from entrez gene ID:
# Example of gene list from transcriptomic dataset with entrez id
genelist <- read.table("entrez_expr_genelist.txt", header=TRUE)
head(genelist)
# entrezgene FC pvalue
# 1 397239 3.737968 0.012114844
# 2 100523962 1.877643 0.000200513
# 3 100524254 -1.201390 0.052669253
# 4 397687 2.121965 0.005759356
# 5 100155420 -1.426236 0.001286279
# 6 100154200 -1.492203 0.000286301
# Preparation of the dataset for panev.network function, converting gene name from entrez to ensembl id
genelist.expr.converted <- panev.exprdataPreparation(in.file = "entrez_expr_genelist.txt",
gene_id = "entrez",
biomart.species = biomart.species.sus)
# Input file imported!
#
# BiomaRt species correct!
#
# Gene id correct!
#
# Convertion from ensembl ID to entrez ID ...
# DONE
# n. 3531 of your 3531 genes have a corresponding gene in KEGG database.
# Gene list exported!
head(genelist.expr.converted)
# entrezgene FC pvalue ensembl_gene_id external_gene_name
# 1 387287 64.592010 0.000002350 ENSSSCG00000003601 HCRTR1
# 2 396564 1.248483 0.036568915 ENSSSCG00000012817 FUNDC2
# 3 396568 4.089142 0.006518320 ENSSSCG00000011802 KNG1
# 4 396574 -9.125993 0.006317556 ENSSSCG00000013387
# 5 396575 1.679256 0.002075938 ENSSSCG00000000272 SP1
# 6 396590 3.067011 0.003039118 ENSSSCG00000008857 MSMO1
Pathways list preparation
The list of main pathways of interest (first level pathways - 1L), coded as KEGG path ID, is mandatory to run the main PANEV functions. The list of all available pathways on KEGG is obtainable by the panev.pathList function, that returns a dataframe containing two columns labeled as path_description and path_ID, respectively. This function helps to retrieve the KEGG path ID if unknown.
To obtain the list of all available pathways in KEGG:
# Create a list of all available pathways for PANEV
list <- panev.pathList(string = NULL)
# The list of all available pathways was created!
# Remember to use the correct path Id(s) for relative PANEV functions.
To obtain the list of the available pathway(s) matching a search string:
# Look for a specific pathway(s) for PANEV, matching your search string
list <- panev.pathList(string = "lipid")
# The list of the pathway(s), matching your string, was created!
# Remember to use the correct path Id(s) for relative PANEV functions.
list
# path_description path_ID
# 1 Glycerolipid metabolism path:map00561
# 2 Glycerophospholipid metabolism path:map00564
# 3 Ether lipid metabolism path:map00565
# 4 Sphingolipid metabolism path:map00600
# 5 Glycosphingolipid biosynthesis - lacto and neolacto series path:map00601
# 6 Glycosphingolipid biosynthesis - globo and isoglobo series path:map00603
# 7 Glycosphingolipid biosynthesis - ganglio series path:map00604
# 8 Sphingolipid signaling pathway path:map04071
# 9 Antidyslipidemic agents path:map07052
For genomic dataset analysis
In the case of genomic dataset analysis, the 1L must be provided as a vector of PANEV pathways identifiers (path_ID) obtained with the panev.pathList command.
# Create a vector of the main pathways of interest (1L)
FL.gene <- c("path:map00061", "path:map00062", "path:map00071", "path:map00072")
For transcriptomic dataset analysis
In the case of transcriptomic dataset analysis, the list of pathways of interest must be provided as a '.txt' file, stored in the working directory. The file must contain the list of *path_ID, obtained with the panev.pathList* command, and relative expression estimated scores, obtained by a gene set enrichment analysis [e.g. flux value (Bionaz et al., 2012)].
# Example of the main pathways of interest (1L) for transcriptomic data analysis
FL.expr <- read.table("expr_listPath.txt", header=TRUE)
FL.expr
# path_ID value
# 1 path:map00010 324.22879
# 2 path:map00020 -21.31287
# 3 path:map00071 385.73774
Species code detection
The KEGG correct organism code is needed to run properly data analyses functions. The code is obtainable with panev.speciesCode.
To create a list of all available organisms in KEGG:
# Create a list of all available organisms in KEGG
list <- panev.speciesCode(string = NULL)
# The list of all species available for PANEV analysis was created!
# Remember to use the correct organisms code for relative PANEV functions.
# Number of available species in KEGG to date:
length(list$panev_code)
# [1] 5913
head(list)
# panev_code species
# hsa Homo sapiens (human)
# ptr Pan troglodytes (chimpanzee)
# pps Pan paniscus (bonobo)
# ggo Gorilla gorilla gorilla (western lowland gorilla)
# pon Pongo abelii (Sumatran orangutan)
# nle Nomascus leucogenys (northern white-cheeked gibbon)
To obtain the list of available code for a specific organism:
# Look for a specific organism in KEGG matching a search string
list <- panev.speciesCode(string = "bos")
# The list of available species matching your string was created!
# Remember to use the correct organism code for relative PANEV functions.
head(list)
# species panev_code
# 1 Bos taurus (cow) bta
# 2 Bos mutus (wild yak) bom
# 3 Bos indicus (zebu cattle) biu
# 4 Malassezia globosa mgl
# 5 Bosea sp. PAMC 26642 bop
# 6 Bosea sp. RAC05 bos
KEGG.species.bos <- as.character(list[1,2])
list <- panev.speciesCode(string = "pig")
# The list of available species matching your string was created!
# Remember to use the correct organism code for relative PANEV functions.
head(list)
# species panev_code
# 1 Sus scrofa (pig) ssc
# 2 Columba livia (rock pigeon) clv
# 3 Cajanus cajan (pigeon pea) ccaj
# 4 Pigmentiphaga sp. H8 pig
# 5 Desulfovibrio piger dpg
# 6 Salipiger profundus tpro
KEGG.species.sus <- as.character(list[1,2])
Data analyses
PANEV analysis and visualization
PANEV automates the process of the functional classification of the genes of interest, detecting the genes inside a functional network of pathways. The network created from selected main pathways (1L) and upstream/downstream dependent ones, it helps to interpret functional profiles of genes, underlying complex biological processes.
Genomic dataset
For genomic data, a functional network is created, taking into account upstream and downstream pathways connected with 1L pathways. The network is formed by pathways involved in sequential steps, connected at more levels (from 1 to n), as required by the user. After that, PANEV highlights the genes inside the network and it provides pathways/genes-based network visualization. The genes highlighted may be considered good candidates for the trait/condition of interest.
The panev.network function requires:
-
a properly formatted gene list;
-
a vector of 1L pathways;
-
species code;
-
the number of levels.
Please see the above "Data preparation" section to understand how to prepare the correct input files.
### Run if it is necessary ###
# Copy the example data file 'data.txt' in your current working directory
# panev.example()
# Head of input file "data.txt"
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
# Perform PANEV
panev.network(in.file = "data.txt",
out.file = "example",
species = KEGG.species.bos,
FL = FL.gene,
levels = 2)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# PANEV analysis completed and relative '.txt' files exported!
#
# Preparing PANEV diagram visualization!
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Well done! Diagram visualization was created and exported.
A new folder is created and files resulting from the analysis are saved inside.
The function generates n text files, with n equal to the number of levels required for the investigation. Every single .txt file is a table containing the genes and the related pathways for a specific level.
#Summary of PANEV results at 1L level obtained with the example of dataset
genes.1L <- read.table("PANEV_RESULTS_FA/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSBTAG00000015980 281152 FASN Fatty acid biosynthesis path:bta00061
# 2 ENSBTAG00000015178 505355 ECI2 Fatty acid degradation path:bta00071
Along with the tabular format, the function gets also the diagram visualization of PANEV results, saved in an interactive '.html' file. The diagram allows zooming in on all the contents for optimal readability.
The 'html' diagram is interactive. Users can select nodes by ID/label (e.g FASN gene).
Transcriptomic dataset
For transcriptomic data, PANEV takes into account any possible connection within/between a list of pathways of interest and a list of differentially expressed genes (DEG).
The panev.exprnetwork function requires:
-
a properly formatted DEG list;
-
a properly formatted list of pathways of interest;
-
the species code;
-
a p-value cut-off (for filtering subset of genes in the DEG list).
Please see the above "Data preparation" section to understand how to prepare the correct input files.
### Run if it is necessary ###
# Copy the example data files "exprdata.txt" and 'expr_listPath.txt' in your current working directory
# panev.example()
# Head of "exprdata.txt" file
# ensembl_gene_id FC pvalue entrezgene external_gene_name
# 1 ENSSSCG00000000002 -4.992506 0.00002740 100520739 GTSE1
# 2 ENSSSCG00000000006 3.737968 0.012114844 397239 PPARA
# 3 ENSSSCG00000000037 1.877643 0.000200513 100523962 ARFGAP3
# 4 ENSSSCG00000000038 -1.201390 0.052669253 100524254 CYB5R3
# 5 ENSSSCG00000000046 2.121965 0.005759356 397687 CYP2D25
# 6 ENSSSCG00000000058 -1.426236 0.001286279 100155420 SNU13
# Head of "expr_listPath.txt" file
# path_ID value
# 1 path:map00010 324.22879
# 2 path:map00020 -21.31287
# 3 path:map00071 385.73774
# Perform PANEV on transcriptomic dataset
panev.exprnetwork(in.file = "exprdata.txt",
path.file = "expr_listPath.txt",
out.file = "example",
species = KEGG.species.sus,
pvalue = 0.05)
# Input file imported!
#
# Pathway input file imported!
#
# Gene list specified... and correct!
#
# Your path list colnames are correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways required!
# n. 3132 of 3774 genes passed the p-value filtering.
#
# Well done! Diagram visualization was created and exported.
A new folder is created and files resulting from the analysis are saved inside.
The function generates a diagram visualization, taking into account all possible connections within/between the 1L pathways and DEGs. The diagram helps to interpret the results obtained from gene expression experiments showing the nodes (i.e. genes and pathways) colored according to their FCs and pathway expression estimated scores [e.g. flux values (Bionaz et al., 2012)], respectively.
The classification into upregulated/downregulated genes/pathways is done considering the top FC/estimated score values.
| gene/pathway classification | with FC/estimated score value | |:---------------------------:|:-----------------------------------------------------------:| | low up/downregulated | <25% of top up/downregulated gene/pathway | | moderate up/downregulated | >= 25% and < 50% of top up/downregulated gene/pathway | | high up/downregulated | >= 50% and < 75% of top up/downregulated gene/pathway | | strong up/downregulated | >= 75% of top up/downregulated gene/pathway |
The 'html' diagram is interactive and allows zooming in on all the contents for optimal readability.
The 'html' diagram is also interactive. Users can select nodes by ID/label (e.g. ALDH2 gene).
Enrichment analysis
PANEV provides also the ancillary functions panev.stats.enrichment() and panev.network.enrichment() to perform a gene enrichment analysis based on a hypergeometric test (one-sided Fisher exact test), as described by Simoes and Emmert-Streib (2012). In particular, while the former function allows the user to search against the default KEGG database, the latter allows computing the pathway enrichment of the genes highlighted by PANEV using the pathways generated in the network as a background.
The results are a series of '.txt' files with specific enrichment analysis results and with general descriptive information about gene and pathway occurrences.
More in details:
-
the *<out.file>_enrichment.txt* file contains the enrichment analysis result. A p-value is calculated for each pathway to estimate the probability of significant over-representation.
-
the *<out.file>_enrichment.txt* file contains the enrichment analysis result of the genes highlighted by PANEV using the pathways generated in the network as a background. A p-value is calculated for each pathway to estimate the probability of significant over-representation;
-
the *<out.file>_GxP.txt* file contains the list of pathways with gene occurrences.
-
the *<out.file>_PxG.txt* file contains the list of genes with pathway occurrences.
These files are useful to explore the list of interesting pathways, which might be possible candidates for further investigation, particularly when no previous restrictive biological assumptions on trait/condition of interest are available.
### Run if it is necessary ###
# Copy the example data file 'data.txt' in your current working directory
# panev.example()
# Head of input file "data.txt"
# ensembl_gene_id entrezgene external_gene_name
# 1 ENSBTAG00000000039 505662 SIRT7
# 2 ENSBTAG00000000040 515219 MAFG
# 3 ENSBTAG00000000042 539606 PYCR1
# 4 ENSBTAG00000000044 617922 MYADML2
# 5 ENSBTAG00000001521 616871 UQCRB
# 6 ENSBTAG00000001522 526138 MTERF3
# Perform the enrichment analysis
panev.stats.enrichment(in.file = "data.txt",
out.file = "example",
species = KEGG.species.bos)
# Input file is imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Enrichment analysis started ...
# and results exported!
#
# Gene per pathway(s) table created and exported!
#
# Pathway per gene(s) table created and exported!
# <out.file>_enrichment.txt file
FA_enrich <- read.table("example_enrichment.txt", header = T)
head(FA_enrich)
# pathway_ID n_genes all_genes pvalue padj pathway_name
# 1 path:bta01100 8 1466 0.002440842 0.6758655 Metabolic pathways
# 2 path:bta04920 2 72 0.007663162 0.6758655 Adipocytokine signaling pathway
# 3 path:bta05212 2 74 0.008080410 0.6758655 Pancreatic cancer
# 4 path:bta04662 2 75 0.008292828 0.6758655 B cell receptor signaling pathway
# 5 path:bta00440 1 6 0.011094948 0.7233906 Phosphonate and phosphinate metabolism
# 6 path:bta04152 2 123 0.021290522 1.0000000 AMPK signaling pathway
# <out.file>_GxP.txt file
FA_GxP <- read.table("example_GxP.txt", header = T)
head(FA_GxP)
# n_genes pathway_name pathway_ID
# 1 8 Metabolic pathways path:bta01100
# 2 2 MAPK signaling pathway path:bta04010
# 3 2 Ras signaling pathway path:bta04014
# 4 2 FoxO signaling pathway path:bta04068
# 5 2 PI3K-Akt signaling pathway path:bta04151
# 6 2 AMPK signaling pathway path:bta04152
# <out.file>_PxG.txt file
FA_PxG <- read.table("example_PxG.txt", header = T)
head(FA_PxG)
# n_pathways entrez_gene_id ensembl_gene_id gene_symbol
# 1 42 281073 ENSBTAG00000007591 CHUK
# 2 23 619066 ENSBTAG00000022927 RAC3
# 3 12 369023 ENSBTAG00000016253 G6PC3
# 4 8 616871 ENSBTAG00000001521 UQCRB
# 5 5 281152 ENSBTAG00000015980 FASN
# 6 3 510274 ENSBTAG00000001868 PCYT2
# Perform the network enrichment analysis
panev.network.enrichment(in.file = in.file,
out.file = "example",
species = KEGG.species.bos,
FL = FL.gene,
levels = 3)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified...
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Enrichment analysis started ...
# and results exported!
# <out.file>_net.enrichment.txt file
# FA_net.enrich <- read.table("example_net.enrichment.txt", header = T)
# pathway_ID n_genes all_genes pvalue padj pathway_name
#1 path:bta00061 1 18 0.3105344 1 Fatty acid biosynthesis
#2 path:bta00500 1 32 0.4866391 1 Starch and sucrose metabolism
#3 path:bta00760 1 39 0.5578855 1 Nicotinate and nicotinamide metabolism
#4 path:bta00071 1 42 0.5854757 1 Fatty acid degradation
#5 path:bta00330 1 48 0.6358667 1 Arginine and proline metabolism
#6 path:bta00010 1 64 0.7435386 1 Glycolysis / Gluconeogenesis
Validation in publication
A publicly available data set from a study on human type 1 diabetes mellitus - T1DM (Qiu et al., 2014) was used as testing set for PANEV in our publication.
In the reference study, the authors carried out a gene-based genome-wide association analysis and identified 452 significant genes. Among these genes, 171 were newly identified for type 1 diabetes mellitus, since ignored in previous studies or in literature. The authors further supported the significance of 53 out of 171 discovered genes with replication studies and differential expression studies.
In particular, the authors reported four non-HLA genes (RASIP1, STRN4, BCAR1 and MYL2) and three HLA genes (FYN, HLA-J and PPP1R11) as validated by both the replication and differential expression studies. These genes represent the main result discussed by the authors.
We performed PANEV considering the list of 171 newly genes, to verify the possible contribution of PANEV visualization for candidate genes identification and, more broadly, for high-throughput data interpretation.
Genomic dataset analysis
'The Type I diabetes mellitus', 'Insulin resistance' and 'AGE-RAGE signaling pathway in diabetic complications' were chosen as 1L pathways, since clearly connected to the trait of interest (Greenbaum, 2002; Ramasamy et al., 2005).
Considering the complexity of the trait, the analysis was performed up to the third level (Field et al., 1997).
After data preparation, only 5 out of 171 genes had no entrez gene identifier. This list was used to run the PANEV functions.
### Run if it is necessary ###
# Copy the example files used as validation set in the publication in the current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_annotated_qiu2014.txt"
# Head of in.file
# external_gene_name entrezgene ensembl_gene_id
# 1 ADAD1 132612 ENSG00000164113
# 2 ASCL2 430 ENSG00000183734
# 3 ATF7IP 55729 ENSG00000171681
# 4 BAK1 578 ENSG00000030110
# 5 BCAR1 9564 ENSG00000050820
# 6 BCL2A1 597 ENSG00000140379
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
FL = c("path:map04940", "path:map04931", "path:map04933")
levels = 3
# Run the PANEV function
panev.network(in.file = in.file,
out.file = "validation",
species = species,
FL = FL,
levels = levels)
# Input file imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# PANEV analysis completed and relative '.txt' files exported!
#
# Preparing PANEV diagram visualization!
# Please wait... It could take a while depending on the number of pathways and levels required!
#
# Well done! Diagram visualization was created and exported.
Overall PANEV results obtained from the validation dataset are in line with the reference study outcomes (Qiu et al., 2014), confirming the effectiveness of our visualization approach.
In particular, 4 out of 7 genes validated both the replication and differential expression studies (Qui et al. 2014) were highlighted by PANEV: PTPN11, BCAR1, MYL2 and FYN. The other three genes (RASIP1, STRN4 and HLA-J) were not detected by PANEV, since, although present in KEGG database, they were not assigned yet to any pathway.
Along with these genes, PANEV highlighted also other interesting genes (ITPR3, BAK1, IL10, HMGB1 and MICA) not discussed by Qui et al. (2014), since validated only by the differential expression or by the replication studies only.
#Summary of PANEV results considering first level (1L) pathways
genes.1L <- read.table("PANEV_RESULTS_validation/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000179295 5781 PTPN11 Insulin resistance path:hsa04931
#Summary of PANEV results considering second level (2L) pathways
genes.2L <- read.table("PANEV_RESULTS_validation/2Lgenes.txt", header = TRUE)
genes.2L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000123374 1017 CDK2 PI3K-Akt signaling pathway path:hsa04151
# 2 ENSG00000096433 3710 ITPR3 Apoptosis path:hsa04210
# 3 ENSG00000030110 578 BAK1 Apoptosis path:hsa04210
# 4 ENSG00000140379 597 BCL2A1 Apoptosis path:hsa04210
# 5 ENSG00000010810 2534 FYN T cell receptor signaling pathway path:hsa04660
# 6 ENSG00000136634 3586 IL10 T cell receptor signaling pathway path:hsa04660
# 7 ENSG00000179295 5781 PTPN11 Adipocytokine signaling pathway path:hsa04920
# 8 ENSG00000101665 4092 SMAD7 TGF-beta signaling pathway path:hsa04350
#Summary of PANEV results considering third level (3L) pathways
genes.1L <- read.table("PANEV_RESULTS_validation/1Lgenes.txt", header = TRUE)
genes.1L
# ensemblgene entrezgene gene_name path_description path_ID
# 1 ENSG00000096433 3710 ITPR3 Calcium signaling pathway path:hsa04020
# 2 ENSG00000050820 9564 BCAR1 Chemokine signaling pathway path:hsa04062
# 3 ENSG00000140379 597 BCL2A1 NF-kappa B signaling pathway path:hsa04064
# 4 ENSG00000136634 3586 IL10 FoxO signaling pathway path:hsa04068
# 5 ENSG00000123374 1017 CDK2 FoxO signaling pathway path:hsa04068
# 6 ENSG00000096433 3710 ITPR3 Phosphatidylinositol signaling system path:hsa04070
# 7 ENSG00000123374 1017 CDK2 Cell cycle path:hsa04110
# 8 ENSG00000123374 1017 CDK2 p53 signaling pathway path:hsa04115
# 9 ENSG00000189403 3146 HMGB1 Autophagy - animal path:hsa04140
# 10 ENSG00000030110 578 BAK1 Protein processing in endoplasmic reticulum path:hsa04141
# 11 ENSG00000010810 2534 FYN Focal adhesion path:hsa04510
# 12 ENSG00000050820 9564 BCAR1 Focal adhesion path:hsa04510
# 13 ENSG00000111245 4633 MYL2 Focal adhesion path:hsa04510
# 14 ENSG00000099866 8174 MADCAM1 Cell adhesion molecules (CAMs) path:hsa04514
# 15 ENSG00000179295 5781 PTPN11 Jak-STAT signaling pathway path:hsa04630
# 16 ENSG00000136634 3586 IL10 Jak-STAT signaling pathway path:hsa04630
# 17 ENSG00000138378 6775 STAT4 Jak-STAT signaling pathway path:hsa04630
# 18 ENSG00000204520 100507436 MICA Natural killer cell mediated cytotoxicity path:hsa04650
# 19 ENSG00000010810 2534 FYN Natural killer cell mediated cytotoxicity path:hsa04650
# 20 ENSG00000179295 5781 PTPN11 Natural killer cell mediated cytotoxicity path:hsa04650
# 21 ENSG00000111245 4633 MYL2 Regulation of actin cytoskeleton path:hsa04810
# 22 ENSG00000050820 9564 BCAR1 Regulation of actin cytoskeleton path:hsa04810
PANEV highlighted also other genes not discussed in the reference study (Qiu et al., 2014) but reported in literature as being associated to the susceptibility to T1DM disease: CDK2 (Kim et al., 2017), MADCAM1 (Phillips et al., 2005), STAT4 (Bi et al., 2013), SMAD7 (Chen et al., 2011) and BCL2A1 (Beyan et al., 2010).
It is worth to note that, except for CDK2, all genes mentioned above refer to researches conducted before the reference study (Qiu at al., 2014).
Simultaneously, it is must be observed that some genes were not highlighted by PANEV, because:
-
they fell inside no-investigated pathway (BRAP, FUT2, GNS, HIPK1, NUPR1, OR2B3, HIST1H4E, HIST1H2BF, OR2B3, OR2B6, OR2J2, OR5V1, and SULT1A1 genes). About 8% of our investigated genes;
-
although present in KEGG database, they were not assigned yet to any pathway. About 44% of our investigated genes.
These drawbacks clearly represent the main PANEV limitation.
Enrichment analysis
In accordance with the reference study (Qui et al., 2014), we performed the functional annotation clustering analysis of the 452 identified T1DM genes, using the PANEV enrichment analysis function.
### Run if it is necessary ###
# Copy the example files used as a validation set in the publication in the current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_enrichment_qui2014.txt"
# Head of in.file
# external_gene_name entrezgene ensembl_gene_id
# 1 OLFML3 56944 not_available
# 2 HIPK1 204851 not_available
# 3 IL10 3586 not_available
# 4 NSL1 25936 not_available
# 5 FAM46B 115572 not_available
# 6 LHX9 56956 not_available
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
# Run the PANEV enrichment function
panev.stats.enrichment(in.file = in.file,
out.file = "validation",
species=species)
# Input file is imported!
#
# Gene list specified... and correct!
#
# Species code specified... and correct!
#
# Enrichment analysis started ...
# and results exported!
#
# Gene per pathway(s) table created and exported!
#
# Pathway per gene(s) table created and exported!
The results showed as the genes tend to be over-represented in immune diseases and immune system pathways, according to Qiu et al. (2014).
#Summary of PANEV enrichment result
enrichment.result <- read.table("validation_enrichment.txt", header = TRUE)
head(enrichment.result)
# pathway_ID n_genes all_genes pvalue padj pathway_name
# 1 path:hsa05322 26 133 1.338552e-22 4.403835e-20 Systemic lupus erythematosus
# 2 path:hsa05330 13 38 3.566961e-15 5.867652e-13 Allograft rejection
# 3 path:hsa04612 16 77 1.306151e-14 1.432412e-12 Antigen processing and presentation
# 4 path:hsa05320 14 53 1.763583e-14 1.450547e-12 Autoimmune thyroid disease
# 5 path:hsa04940 13 43 2.264972e-14 1.490352e-12 Type I diabetes mellitus
# 6 path:hsa05332 12 41 3.705738e-13 2.031980e-11 Graft-versus-host disease
Transcriptomic dataset analysis
By way of example, to explore the possibility to visualize gene expression values, we used in the publication the FC values obtained by Levy et al. (2012) study and considered by Qiu et al. (2014), as reference study for differential expression analysis validation.
Since the authors did not provide pathway estimated scores, we substituted these measures with gene occurrence values for each pathway of interest, obtained by PANEV enrichment analysis.
### Run if it is necessary ###
# Copy the example data files "genelist_expr_Levy2012.txt" and 'pathlist_expr_Levy2012.txt' in your current working directory
# panev.example(type="validation")
# Parameters
in.file="genelist_expr_Levy2012.txt"
#
# Head of "genelist_expr_Levy2012" file
# ensembl_gene_id external_gene_name entrezgene FC pvalue
# ENSG00000204252 HLA-DOA 3111 -0.535 0.000
# ENSG00000239457 HLA-DOB 3112 -0.017 0.955
# ENSG00000168384 HLA-DPA1 3113 -0.491 0.025
# ENSG00000206239 HLA-DQA1 3117 -0.846 0.000
# ENSG00000206237 HLA-DQB1 3119 -0.467 0.007
# ENSG00000204592 HLA-E 3133 0.298 0.014
#
path.file = "pathlist_expr_Levy2012.txt"
# Head of "pathlist_expr_Levy2012" file
# path_ID value
# path:map04514 16
# path:map04940 14
# path:map04151 6
# path:map04210 5
# path:map04630 5
# path:map04010 4
#Look for the specie code matching the search string
list <- panev.speciesCode(string = "homo")
species=as.character(list[1,2]) # hsa
# Perform PANEV on transcriptomic dataset
panev.exprnetwork(in.file = in.file,
path.file = path.file,
out.file = "expression_data_validation",
species = species,
pvalue = 0.05)
# Input file imported!
#
# Pathway input file imported!
#
# Gene list specified... and correct!
#
# Your path list colnames are correct!
#
# Species code specified... and correct!
#
# Pathway(s) is specified... and correct!
#
# Prerequisite check passed!
#
# PANEV is running ...
# Please wait... It could take a while depending on the number of pathways required!
# n. 18 of 30 genes passed the p-value filtering.
#
# Well done! Diagram visualization was created and exported.
This PANEV visualization helped to interpret the results obtained from gene expression experiments by showing the nodes (i.e. genes) colored according to gene FC values. The diagram showed the relationships among genes and pathways and it allowed us to identify functionally related entities with possible coordinated expression changes.
Conclusions
PANEV represents an interesting visualization approach to reduce the complexity of the challenge of high-throughput data mining and identify candidate genes identification. PANEV, providing an integrated summary among pathways and genes of interest, facilitates the data interpretation.
The contribution of the PANEV tool could be significant not only for well-annotated species (i.e. Homo sapiens, Mus musculus) but also for all the organisms available in KEGG database. Although KEGG is a popular and constantly updated database for biological network information, the lack or incomplete information on KEGG could represent the main PANEV disadvantage. The effectiveness of PANEV analysis in terms of result coherency was confirmed by the validation study. In particular, PANEV produces timesaving advantages, pointing users to genes that are biologically involved with the investigated trait.
References
Beyan,H. et al. (2010) Monocyte gene-expression profiles associated with childhood-onset type 1 diabetes and disease risk: a study of identical twins. Diabetes, 59, 1751–1755. .
Bi,C. et al. (2013) Association study of STAT4 polymorphisms and type 1 diabetes in Northeastern Chinese Han population. Tissue Antigens, 81, 137–140. .
Bionaz,M. et al. (2012). A novel dynamic impact approach (DIA) for functional analysis of time-course omics studies: validation using the bovine mammary transcriptome. PLos One 7:e32455. .
Chen HY, Huang XR, Wang W, Li JH, Heuchel RL, Chung ACK, et al. The protective role of Smad7 in diabetic kidney disease: mechanism and therapeutic potential. Diabetes. 2011;60: 590-601.
Field,L.L. at al. (1997). Unravelling a complex trait: the genetics of insulin-dependent diabetes mellitus. Clin. Investig. Med. Med. Clin. Exp. 20:41–49.
Greenbaum, C.J. 2002. Insulin resistance in type 1 diabetes. Diabetes Metab. Res. Rev. 18:192–200. .
Kim,S.Y. et al. (2017) Loss of cyclin dependent kinase 2 in the pancreas links primary β-cell dysfunction to progressive depletion of β-cell mass and diabetes. J. Biol. Chem., .
Levy, H., X. Wang, M. Kaldunski, S. Jia, J. Kramer, S.J. Pavletich, M. Reske, T. Gessel, M. Yassai, M.W. Quasney, M.K. Dahmer, J. Gorski, and M.J. Hessner. 2012. Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes. Genes Immun. 13:593-604. .
Phillips,J.M. et al. (2005) MAdCAM-1 is needed for diabetes development mediated by the T cell clone, BDC-2·5. Immunology, 116, 525–531. .
Qiu,Y.-H. et al. (2014) Identification of novel risk genes associated with type 1 diabetes mellitus using a genome-wide gene-based association analysis. J. Diabetes Investig., 5, 649–656. .
Ramasamy, R. et al. (2005). Advanced glycation end products and RAGE: a common thread in aging, diabetes, neurodegeneration, and inflammation. Glycobiology 15:16R-28R. .
Shi,A. et al. (2016) Genetic variants in vitamin D signaling pathways and risk of gestational diabetes mellitus. Oncotarget, 7, 67788–67795. .
Simoes, R. de M., and F. Emmert-Streib. 2012. Bagging statistical network inference from large-scale gene expression data. PLos One 7:e33624. .
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