README.md
In mbanf/MERIT: MastEr Regulator Inference Tool
MERIT
About
MERIT
Contact the author in case you've found a bug.
Installation
The easiest way to install MERIT is through devtools (see OS specific notes on installing devtools at the end).
# install.packages("devtools")
library(devtools)
# depending on your system (Windows, Mac or linux) please use
install_github("https://github.com/mbanf/METACLUSTER")
# or
install_github("METACLUSTER","mbanf")
Usage
To run the MERIT with the A. thaliana data you can download all neccessary datasets from onedrive: datasets_athaliana_PMN
library(MERIT) # load package
setwd("/User/home/MERIT_Athaliana_PMN") # set working directory to the dataset files
Load datasets parameters:
filename.genes genes (rows of the expression datasets)filename.experiment_ids experimental datasets (columns of the expression datasets)filename.foldChange_differentialExpression differential expression data (fold changes)filename.pvalue_differentialExpression differential expression data (p-values)filename.experiment_condition_tissue_annotation experiment to treatment and tissue annotationfilename.transcriptionfactor_annotation transcription factor with family annotation filename.geneGroups gene group dataset (here metabolic domains)
l.data = load_datasets(filename.genes = "data/genes.txt",
filename.experiment_ids = "data/experiment_ids.txt",
filename.foldChange_differentialExpression = "data/m.foldChange_differentialExpression.txt",
filename.pvalue_differentialExpression = "data/m.pvalue_differentialExpression.txt",
filename.experiment_condition_tissue_annotation = "data/experiment_annotation.txt",
filename.transcriptionfactor_annotation = "data/df.transcriptionFactorAnnotation.txt",
filename.geneGroups = "data/df.enzymes_w_metabolic_domains.txt")
MERIT Parameter sets:
!We set b.load_grn_inference = "yes", b.load_TFBS_inference = "yes, and b.load_treatment_tissue_inference = "yes for the PMN 2017 A.thaliana gene cluster predictions data, as we have pre-computed and provided all co-differential expression datasets - for other datasets, set to "no"! - modular structrucar
b.load_grn_inference load precomputed grns ("yes", "no")b.load_TFBS_inference load precomputed TFBS ("yes","no")b.load_treatment_tissue_inference load precomputed annotation filgerung ("yes","no")m.foldChange_differentialExpression differential expression foldchange matrix - rows are genes, cols are experimentsm.pvalue_differentialExpression differential expression pvalue matrix - rows are genes, cols are experimentsdf.experiment_condition_annotation experiment condition annotationtb.condition_treatmentstb.condition_tissuesdf.transcriptionFactorAnnotationdf.geneGroups tb.geneGroupsv.geneGroups l.geneGroupsn.cpus number of cores usedseed (default = 1234)importance.measure (default = "impurity")n.trees (default = 1000)n.lead_method_expression_shuffling (default = 1)nbootstrap (default = 100)nstepsLARS (default = 5)th.lead_grn_method (default = 0.95)th.support_grn_methods (default = 0.95)n.grnSupport (default = 1)file.TF_to_Motif_IDsfile.TFBS_motifsfile.promoterSeqfile.geneSeqth.pre_tss (default = 1000)th.post_tss (default = 200)genome_nucleotide_distribution A,C,G,T nucleotide distritbution (default = c(0.3253439, 0.1746561, 0.1746561, 0.3253439))th.pval.known_motifs (default = 0.05)th.diffexp (default = 0.05)th.pval.treatment (default = 0.05) th.pval.tissue (default = 0.05)th.min.samples (default = 1)s.multipleTestCorrection (default = "none")th.min_number_targets (default = 2),th.min_number_MR_targets (default = 2),th.pval_masterRegulator (default = 0.05)foldername.tmp temp file folder name (default = tmp/)foldername.results results file folder name (default = results/)
l.results = run_MERIT(b.load_grn_inference = "yes",
b.load_TFBS_inference = "yes",
b.load_treatment_tissue_inference = "yes",
m.foldChange_differentialExpression=l.data$m.foldChange_differentialExpression,
m.pvalue_differentialExpression=l.data$m.pvalue_differentialExpression,
df.experiment_condition_annotation=l.data$df.experiment_condition_annotation,
tb.condition_treatments=l.data$tb.condition_treatments,
tb.condition_tissues=l.data$tb.condition_tissues,
df.transcriptionFactorAnnotation=l.data$df.transcriptionFactorAnnotation,
df.geneGroups=l.data$df.geneGroups,
tb.geneGroups=l.data$tb.geneGroups,
v.geneGroups=l.data$v.geneGroups,
l.geneGroups=l.data$l.geneGroups,
n.cpus = 5,
seed=1234,
importance.measure="impurity",
n.trees=1000,
n.lead_method_expression_shuffling = 1,
n.bootstrap=100,
n.stepsLARS=5,
th.lead_grn_method = 0.95,
th.support_grn_methods = 0.95,
n.grnSupport = 1,
file.TF_to_Motif_IDs = "data/TF_to_Motif_IDs.txt",
file.TFBS_motifs = "data/Transcription_factor_weight_matrix_Arabidopsis_thaliana.txt",
file.promoterSeq = "data/TAIR10_upstream_1000_20101104.txt",
file.geneSeq = "data/TAIR10_seq_20110103_representative_gene_model_updated.txt",
th.pre_tss = 1000,
th.post_tss = 200,
genome_nucleotide_distribution = c(A = 0.3253439, C = 0.1746561, G = 0.1746561, T = 0.3253439 ),
th.pval.known_motifs = 0.05,
th.diffexp = 0.05,
th.pval.treatment = 0.05,
th.pval.tissue = 0.05,
th.min.samples = 1,
s.multipleTestCorrection = "none",
th.min_number_targets = 2,
th.min_number_MR_targets = 2,
th.pval_masterRegulator = 0.05,
foldername.tmp = "tmp/",
foldername.results = "results/")
Next evaluate and store the results
# Results
# Step 1 - Gene regulatory network inference using ensemble regression with Monte Carlo based threshold selection
l.res.grn = l.results$l.res.grn
# Step 2 - Transcription factor direct target promoter binding based filtering of gene regulatory link predictions
l.res.grn_tfbs = l.results$l.res.grn_tfbs
# Step3 - Context specific annotation and filtering of gene regulatory link predictions
l.res.link_annotation = l.results$l.res.link_annotation
# Step 4 - Master regulator hierarchy inference
l.res.MR_hierarchy = l.results$l.res.MR_hierarchy
format_results(l.grn_subnetworks = l.res.link_annotation$l.grn_subnetworks,
tb.condition_tissue_differentialExpression = l.res.link_annotation$tb.condition_tissue_differentialExpression,
l.Hierarchy=l.res.MR_hierarchy$l.Hierarchy,
l.Hierarchy_tfs_per_tier=l.res.MR_hierarchy$l.Hierarchy_tfs_per_tier,
l.Hierarchy_nb_tfs_per_tier=l.res.MR_hierarchy$l.Hierarchy_nb_tfs_per_tier,
l.df.masterRegulatorHierarchy=l.res.MR_hierarchy$l.df.masterRegulatorHierarchy,
v.number_tiers=l.res.MR_hierarchy$v.number_tiers,
m.MR_vs_conditions = l.res.MR_hierarchy$m.MR_vs_conditions,
l.MR_vs_geneGroups_given_condition = l.res.MR_hierarchy$l.MR_vs_geneGroups_given_condition,
number_of_conditions_per_master_regulator=l.res.MR_hierarchy$number_of_conditions_per_master_regulator,
tb.condition_treatments=l.data$tb.condition_treatments,
tb.condition_tissues=l.data$tb.condition_tissues,
df.transcriptionFactorAnnotation=l.data$df.transcriptionFactorAnnotation,
df.geneGroups=l.data$df.geneGroups,
tb.geneGroups=l.data$tb.geneGroups,
v.geneGroups=l.data$v.geneGroups,
l.geneGroups=l.data$l.geneGroups,
th.pval = 0.05,
foldername.results = "results/",
file.subGeneGroups = "data/aracyc_pathways.20180327")
A) Overview of the MERIT framework.
Notes
Installation of devtools dependencies under Ubuntu (prior to installing devtools):
sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev
Subsequently, install devtools in R:
install.packages("devtools")
If you have difficulaties installing TFBSTOOLS
sudo apt-get install gsl-bin libgsl0-dev
mbanf/MERIT documentation built on June 16, 2021, 1:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
MERIT
About
MERIT
Contact the author in case you've found a bug.
Installation
The easiest way to install MERIT is through devtools (see OS specific notes on installing devtools at the end).
# install.packages("devtools")
library(devtools)
# depending on your system (Windows, Mac or linux) please use
install_github("https://github.com/mbanf/METACLUSTER")
# or
install_github("METACLUSTER","mbanf")
Usage
To run the MERIT with the A. thaliana data you can download all neccessary datasets from onedrive: datasets_athaliana_PMN
library(MERIT) # load package
setwd("/User/home/MERIT_Athaliana_PMN") # set working directory to the dataset files
Load datasets parameters:
filename.genesgenes (rows of the expression datasets)filename.experiment_idsexperimental datasets (columns of the expression datasets)filename.foldChange_differentialExpressiondifferential expression data (fold changes)filename.pvalue_differentialExpressiondifferential expression data (p-values)filename.experiment_condition_tissue_annotationexperiment to treatment and tissue annotationfilename.transcriptionfactor_annotationtranscription factor with family annotationfilename.geneGroupsgene group dataset (here metabolic domains)
l.data = load_datasets(filename.genes = "data/genes.txt",
filename.experiment_ids = "data/experiment_ids.txt",
filename.foldChange_differentialExpression = "data/m.foldChange_differentialExpression.txt",
filename.pvalue_differentialExpression = "data/m.pvalue_differentialExpression.txt",
filename.experiment_condition_tissue_annotation = "data/experiment_annotation.txt",
filename.transcriptionfactor_annotation = "data/df.transcriptionFactorAnnotation.txt",
filename.geneGroups = "data/df.enzymes_w_metabolic_domains.txt")
MERIT Parameter sets:
!We set b.load_grn_inference = "yes", b.load_TFBS_inference = "yes, and b.load_treatment_tissue_inference = "yes for the PMN 2017 A.thaliana gene cluster predictions data, as we have pre-computed and provided all co-differential expression datasets - for other datasets, set to "no"! - modular structrucar
b.load_grn_inferenceload precomputed grns ("yes", "no")b.load_TFBS_inferenceload precomputed TFBS ("yes","no")b.load_treatment_tissue_inferenceload precomputed annotation filgerung ("yes","no")m.foldChange_differentialExpressiondifferential expression foldchange matrix - rows are genes, cols are experimentsm.pvalue_differentialExpressiondifferential expression pvalue matrix - rows are genes, cols are experimentsdf.experiment_condition_annotationexperiment condition annotationtb.condition_treatmentstb.condition_tissuesdf.transcriptionFactorAnnotationdf.geneGroupstb.geneGroupsv.geneGroupsl.geneGroupsn.cpusnumber of cores usedseed(default = 1234)importance.measure(default = "impurity")n.trees(default = 1000)n.lead_method_expression_shuffling(default = 1)nbootstrap(default = 100)nstepsLARS(default = 5)th.lead_grn_method(default = 0.95)th.support_grn_methods(default = 0.95)n.grnSupport(default = 1)file.TF_to_Motif_IDsfile.TFBS_motifsfile.promoterSeqfile.geneSeqth.pre_tss(default = 1000)th.post_tss(default = 200)genome_nucleotide_distributionA,C,G,T nucleotide distritbution (default = c(0.3253439, 0.1746561, 0.1746561, 0.3253439))th.pval.known_motifs(default = 0.05)th.diffexp(default = 0.05)th.pval.treatment(default = 0.05)th.pval.tissue(default = 0.05)th.min.samples(default = 1)s.multipleTestCorrection(default = "none")th.min_number_targets(default = 2),th.min_number_MR_targets(default = 2),th.pval_masterRegulator(default = 0.05)foldername.tmptemp file folder name (default = tmp/)foldername.resultsresults file folder name (default = results/)
l.results = run_MERIT(b.load_grn_inference = "yes",
b.load_TFBS_inference = "yes",
b.load_treatment_tissue_inference = "yes",
m.foldChange_differentialExpression=l.data$m.foldChange_differentialExpression,
m.pvalue_differentialExpression=l.data$m.pvalue_differentialExpression,
df.experiment_condition_annotation=l.data$df.experiment_condition_annotation,
tb.condition_treatments=l.data$tb.condition_treatments,
tb.condition_tissues=l.data$tb.condition_tissues,
df.transcriptionFactorAnnotation=l.data$df.transcriptionFactorAnnotation,
df.geneGroups=l.data$df.geneGroups,
tb.geneGroups=l.data$tb.geneGroups,
v.geneGroups=l.data$v.geneGroups,
l.geneGroups=l.data$l.geneGroups,
n.cpus = 5,
seed=1234,
importance.measure="impurity",
n.trees=1000,
n.lead_method_expression_shuffling = 1,
n.bootstrap=100,
n.stepsLARS=5,
th.lead_grn_method = 0.95,
th.support_grn_methods = 0.95,
n.grnSupport = 1,
file.TF_to_Motif_IDs = "data/TF_to_Motif_IDs.txt",
file.TFBS_motifs = "data/Transcription_factor_weight_matrix_Arabidopsis_thaliana.txt",
file.promoterSeq = "data/TAIR10_upstream_1000_20101104.txt",
file.geneSeq = "data/TAIR10_seq_20110103_representative_gene_model_updated.txt",
th.pre_tss = 1000,
th.post_tss = 200,
genome_nucleotide_distribution = c(A = 0.3253439, C = 0.1746561, G = 0.1746561, T = 0.3253439 ),
th.pval.known_motifs = 0.05,
th.diffexp = 0.05,
th.pval.treatment = 0.05,
th.pval.tissue = 0.05,
th.min.samples = 1,
s.multipleTestCorrection = "none",
th.min_number_targets = 2,
th.min_number_MR_targets = 2,
th.pval_masterRegulator = 0.05,
foldername.tmp = "tmp/",
foldername.results = "results/")
Next evaluate and store the results
# Results
# Step 1 - Gene regulatory network inference using ensemble regression with Monte Carlo based threshold selection
l.res.grn = l.results$l.res.grn
# Step 2 - Transcription factor direct target promoter binding based filtering of gene regulatory link predictions
l.res.grn_tfbs = l.results$l.res.grn_tfbs
# Step3 - Context specific annotation and filtering of gene regulatory link predictions
l.res.link_annotation = l.results$l.res.link_annotation
# Step 4 - Master regulator hierarchy inference
l.res.MR_hierarchy = l.results$l.res.MR_hierarchy
format_results(l.grn_subnetworks = l.res.link_annotation$l.grn_subnetworks,
tb.condition_tissue_differentialExpression = l.res.link_annotation$tb.condition_tissue_differentialExpression,
l.Hierarchy=l.res.MR_hierarchy$l.Hierarchy,
l.Hierarchy_tfs_per_tier=l.res.MR_hierarchy$l.Hierarchy_tfs_per_tier,
l.Hierarchy_nb_tfs_per_tier=l.res.MR_hierarchy$l.Hierarchy_nb_tfs_per_tier,
l.df.masterRegulatorHierarchy=l.res.MR_hierarchy$l.df.masterRegulatorHierarchy,
v.number_tiers=l.res.MR_hierarchy$v.number_tiers,
m.MR_vs_conditions = l.res.MR_hierarchy$m.MR_vs_conditions,
l.MR_vs_geneGroups_given_condition = l.res.MR_hierarchy$l.MR_vs_geneGroups_given_condition,
number_of_conditions_per_master_regulator=l.res.MR_hierarchy$number_of_conditions_per_master_regulator,
tb.condition_treatments=l.data$tb.condition_treatments,
tb.condition_tissues=l.data$tb.condition_tissues,
df.transcriptionFactorAnnotation=l.data$df.transcriptionFactorAnnotation,
df.geneGroups=l.data$df.geneGroups,
tb.geneGroups=l.data$tb.geneGroups,
v.geneGroups=l.data$v.geneGroups,
l.geneGroups=l.data$l.geneGroups,
th.pval = 0.05,
foldername.results = "results/",
file.subGeneGroups = "data/aracyc_pathways.20180327")
A) Overview of the MERIT framework.
Notes
Installation of devtools dependencies under Ubuntu (prior to installing devtools): sudo apt-get install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev
Subsequently, install devtools in R: install.packages("devtools")
If you have difficulaties installing TFBSTOOLS sudo apt-get install gsl-bin libgsl0-dev
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.