run_MERIT: run algorithm
In mbanf/MERIT: MastEr Regulator Inference Tool
Description
Usage
Arguments
Value
View source: R/functions.R
View source: R/MERIT_functions.R
Description
run algorithm
Usage
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run_MERIT(b.load_grn_inference = "yes", b.load_TFBS_inference = "yes",
b.load_treatment_tissue_inference = "yes",
m.foldChange_differentialExpression = m.foldChange_differentialExpression,
m.pvalue_differentialExpression = m.pvalue_differentialExpression,
df.experiment_condition_annotation = df.experiment_condition_annotation,
tb.condition_treatments = tb.condition_treatments,
tb.condition_tissues = tb.condition_tissues,
df.transcriptionFactorAnnotation = df.transcriptionFactorAnnotation,
df.geneGroups = df.geneGroups, tb.geneGroups = tb.geneGroups,
v.geneGroups = v.geneGroups, l.geneGroups = l.geneGroups,
n.cpus = 3, 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/")
Arguments
b.load_grn_inference
("yes","no")
b.load_TFBS_inference
"yes","no")
b.load_treatment_tissue_inference
("yes","no")
df.transcriptionFactorAnnotation
seed
(default = 1234)
importance.measure
(default = "impurity")
n.trees
(default = 1000)
n.lead_method_expression_shuffling
(default = 1)
th.lead_grn_method
(default = 0.95)
th.support_grn_methods
(default = 0.95)
n.grnSupport
(default = 1)
file.TF_to_Motif_IDs
(default = "data/TF_to_Motif_IDs.txt")
file.TFBS_motifs
(default = "data/Transcription_factor_weight_matrix_Arabidopsis_thaliana.txt")
file.promoterSeq
(default = "data/TAIR10_upstream_1000_20101104.txt")
file.geneSeq
(default = "data/TAIR10_seq_20110103_representative_gene_model_updated.txt")
th.pre_tss
(default= 1000)
th.post_tss
(default = 200)
genome_nucleotide_distribution
ACGT distribution (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
(default = "tmp/")
foldername.results
= (default = "results/")
nbootstrap
(default = =100)
nstepsLARS
(default = 5)
Value
a list of results from all 4 steps
setwd(...) # set to dataset
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")
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 = 3,
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/")
# 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, # A) TFs versus Conditions (Matrix plot) P(TF,C)
l.MR_vs_geneGroups_given_condition = l.res.MR_hierarchy$l.MR_vs_geneGroups_given_condition, # B) per condition - TFs versus Domains (P(TF,D|C)) => also cumulative plot
number_of_conditions_per_master_regulator=l.res.MR_hierarchy$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")
mbanf/MERIT documentation built on June 16, 2021, 1:07 p.m.
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Description Usage Arguments Value
View source: R/functions.R View source: R/MERIT_functions.R
Description
run algorithm
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | run_MERIT(b.load_grn_inference = "yes", b.load_TFBS_inference = "yes",
b.load_treatment_tissue_inference = "yes",
m.foldChange_differentialExpression = m.foldChange_differentialExpression,
m.pvalue_differentialExpression = m.pvalue_differentialExpression,
df.experiment_condition_annotation = df.experiment_condition_annotation,
tb.condition_treatments = tb.condition_treatments,
tb.condition_tissues = tb.condition_tissues,
df.transcriptionFactorAnnotation = df.transcriptionFactorAnnotation,
df.geneGroups = df.geneGroups, tb.geneGroups = tb.geneGroups,
v.geneGroups = v.geneGroups, l.geneGroups = l.geneGroups,
n.cpus = 3, 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/")
|
Arguments
b.load_grn_inference |
("yes","no") |
b.load_TFBS_inference |
"yes","no") |
b.load_treatment_tissue_inference |
("yes","no") |
df.transcriptionFactorAnnotation |
|
seed |
(default = 1234) |
importance.measure |
(default = "impurity") |
n.trees |
(default = 1000) |
n.lead_method_expression_shuffling |
(default = 1) |
th.lead_grn_method |
(default = 0.95) |
th.support_grn_methods |
(default = 0.95) |
n.grnSupport |
(default = 1) |
file.TF_to_Motif_IDs |
(default = "data/TF_to_Motif_IDs.txt") |
file.TFBS_motifs |
(default = "data/Transcription_factor_weight_matrix_Arabidopsis_thaliana.txt") |
file.promoterSeq |
(default = "data/TAIR10_upstream_1000_20101104.txt") |
file.geneSeq |
(default = "data/TAIR10_seq_20110103_representative_gene_model_updated.txt") |
th.pre_tss |
(default= 1000) |
th.post_tss |
(default = 200) |
genome_nucleotide_distribution |
ACGT distribution (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 |
(default = "tmp/") |
foldername.results |
= (default = "results/") |
nbootstrap |
(default = =100) |
nstepsLARS |
(default = 5) |
Value
a list of results from all 4 steps setwd(...) # set to dataset
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")
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$l.geneGroups, n.cpus = 3, seed=1234, importance.measure="impurity", n.trees=1000, n.lead_method_expression_shuffling = 1, n.bootstrap=100, n.stepsLARS=5, th.lead_grn_method = 0.95, th.support_grn_methods = 0.95, n.grnSupport = 1, file.TF_to_Motif_IDs = "data/TF_to_Motif_IDs.txt", file.TFBS_motifs = "data/Transcription_factor_weight_matrix_Arabidopsis_thaliana.txt", file.promoterSeq = "data/TAIR10_upstream_1000_20101104.txt", file.geneSeq = "data/TAIR10_seq_20110103_representative_gene_model_updated.txt", th.pre_tss = 1000, th.post_tss = 200, genome_nucleotide_distribution = c(A = 0.3253439, C = 0.1746561, G = 0.1746561, T = 0.3253439 ), th.pval.known_motifs = 0.05, th.diffexp = 0.05, th.pval.treatment = 0.05, th.pval.tissue = 0.05, th.min.samples = 1, s.multipleTestCorrection = "none", th.min_number_targets = 2, th.min_number_MR_targets = 2, th.pval_masterRegulator = 0.05, foldername.tmp = "tmp/", foldername.results = "results/")
# Step 1 - Gene regulatory network inference using ensemble regression with Monte Carlo based threshold selection l.res.grn = l.results$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$m.MR_vs_conditions, # A) TFs versus Conditions (Matrix plot) P(TF,C) l.MR_vs_geneGroups_given_condition = l.res.MR_hierarchy$l.MR_vs_geneGroups_given_condition, # B) per condition - TFs versus Domains (P(TF,D|C)) => also cumulative plot 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")
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