README.md
In martalovino/MoPc: What the Package Does (One Line, Title Case)
MoPc: Multi-omics Partial correlation analysis
MoPc (Multi-omics partial correlation analysis) is software to reveal
the miRNA drivers of a biological process in a multi-omics dataset,
starting from the expression matrices of mRNA, proteomics, and miRNA.
In the presence of such matrices, the canonical approach to identify
miRNA drivers consists in carrying out the mRNA-miRNA correlation (mRNA
and miRNA matrices) and proteomics-miRNA (proteomics and miRNA
matrices).
Although this approach has proved effective, MoPc introduces the partial
correlation measurement between the mRNA and proteomic matrices,
conditioned on the expression of miRNAs. In particular, MoPc selects as
a driver the miRNAs in which the partial correlation p-value is better
(therefore lower) than the bivariate mRNA-proteomic correlation p-value.
The tool also verifies the enrichment of the mRNA-miRNA pairs found with
what is already available in the literature in three well-known
databases: mirDB, TargestScan, mirTarbase.
Installation
You can install the development version of MoPc from
GitHub with:
# install.packages("devtools")
devtools::install_github("martalovino/MoPc")
Why should you use MoPc
MoPc identifies the miRNA drivers starting from a multi-omic dataset
composed of:
- mRNA expression matrix
- proteomic expression matrix
- miRNA expression matrix exploiting the partial correlation between
mRNA and proteomics conditioned on the expression of miRNAs.
Input
To run MoPc you need:
- mRNA expression matrix;
- proteomic expression matrix;
- miRNA expression matrix.
You can find a sample dataset directly inside the package, which you can
easily replace with your data.
Output
The MoPc output consists of:
- A list with all the drivers found, with their enrichment;
- A map with all the miRNA drivers found in the dataset (in red, those
enriched in at least one of mirDB, TargestScan, mirTarbase);
- A report of the enrichment values in the three databases mirDB,
TargestScan, mirTarbase by hypergeometric test.
Please, see the example below.
Example
Below you will find an illustrative example of the input data and the
tool’s output. The tool consists of several parts:
- Setup and parameters setting;
- Data loading and preprocessing;
- Core part of the tool: partial correlation computation, and
validation information;
- Plot correlation;
- Hypergeometric test;
- Plots, validation and hypergeometric test on bivariate correlation
mRNA-mirna and prot-miRNA
Setup and parameters setting
This part loads the package and set proper hyper-parameters.
library(MoPc)
# Parameters
corr_method <- "pearson"
mat1_name <- "rna"
mat2_name <- "prot"
mat3_name <- "mirna"
ncores = 7
fdr_th <- 0.05
improvement_th <- 0
out_folder <- "results"
dir.create(out_folder, showWarnings = FALSE)
Data loading and preprocessing
This part loads some sample data to work with and performs basic
preprocessing.
# load breast cancer dataset
data(rna)
data(prot)
data(mirna)
# minimalist preprocecessing: selecting only common samples and common genes
# between rna and prot
d <- minimalist_preproc(rna, prot, mirna)
#> Total number of samples: 77
#> Total number of genes/proteins: 170
# annotate and sort genes/mirnas by chromosomal order
rna <- annotate_genes(d$mat1)
prot <- annotate_genes(d$mat2)
mirna <- annotate_mirnas(d$mat3)
#-------------------------------------------------------------------------------
# DEFINING ROWS and COLUMNS of partial correlation matrix
# usually genes on the rows and mirnas or CNAs on the columns
#-------------------------------------------------------------------------------
genes_proteins <- rownames(rna$data)
condv <- rownames(mirna$data)
gene_annot <- rna$annot
condv_annot <- mirna$annot
Core part of the tool: partial correlation computation, and validation information
This is the core part of the tool. It contains the partial correlation
computation and the validation information.
#-------------------------------------------------------------------------------
# PARTIAL CORRELATION COMPUTAION
#-------------------------------------------------------------------------------
a <- compute_pc_values(rna$data, prot$data, mirna$data, corr_method, out_folder, ncores)
#> [1] "Computing partial correlation values..................................."
#> Standard correlation computed in: 2.97763848304749
#> [1] "Saving partial correlation values in: results/partial_estimates_pvalue_pearson.RData"
#> [1] "Data succesfully saved................................................."
mat <- get_relevant_improvement(rna$data, prot$data, a$pval, corr_method, improvement_th)
#> [1] "Computing improvement of partial correlation over standard correlation"
th <- quantile(unlist(mat[!is.na(mat)]), 0.75)
mat <- get_relevant_improvement(rna$data, prot$data, a$pval, corr_method, th)
#> [1] "Computing improvement of partial correlation over standard correlation"
#-------------------------------------------------------------------------------
# GET VALIDATION info
#-------------------------------------------------------------------------------
valid <- validation_info(mat, condv, genes_proteins, 7)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 47"
#> [1] "TargetScan validated: 7"
#> [1] "mirtarbase validated: 29"
Result 1: miRNA driver list
The list with all the drivers found, with their enrichment is available
in the valid object.
head(valid$to_validate)
#> row col value validation_name Validated.mirDB
#> 1: HMGN2 hsa-miR-199a-5p 0.1817172 HMGN2_miR-199a-5p no
#> 2: SOS1 hsa-miR-199a-5p 0.009361374 SOS1_miR-199a-5p no
#> 3: IMMT hsa-miR-199a-5p 0.03892966 IMMT_miR-199a-5p no
#> 4: CAND2 hsa-miR-199a-5p 0.3524636 CAND2_miR-199a-5p no
#> 5: SRGAP3 hsa-miR-199a-5p 0.01547189 SRGAP3_miR-199a-5p yes
#> 6: ANAPC4 hsa-miR-199a-5p 0.01892321 ANAPC4_miR-199a-5p no
#> Validated.TS Validated.mirtarbase
#> 1: no no
#> 2: no no
#> 3: no no
#> 4: no no
#> 5: no no
#> 6: no no
Result 2: Plot correlation figure
Main plot of the package.
#-------------------------------------------------------------------------------
# PARTIAL CORRELATION PLOT
#-------------------------------------------------------------------------------
partial_corr_heatmap_plot(valid$to_validate, genes_proteins, condv, gene_annot, condv_annot,
mat, out_folder)
#> [1] "Plot final heatmap"
#> png
#> 2
Result 3: Hypergeometric test
This section computes the hypergeometric test to ensure the enrichment
in the three databases: mirDB, TargetScan, mirTarBase. The
hypergeometric test results are written in the
hypergeometric_test_results.txt report file.
#-------------------------------------------------------------------------------
# Computing enrichment significance in mirDB, TargetScan, mirtarBase
#-------------------------------------------------------------------------------
hyper_test(valid$to_validate, valid$universe, valid$mirDB, valid$ts, valid$tarbase, file.path(out_folder, "hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 47 1432
#> 204 6793
#> Fisher test p-value: 0.612
#> Hypergeometric test p-value: 0.263
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 7 1472
#> 59 6938
#> Fisher test p-value: 0.191
#> Hypergeometric test p-value: 0.91
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 29 1450
#> 111 6886
#> Fisher test p-value: 0.312
#> Hypergeometric test p-value: 0.129
Plots, validation and hypergeometric test on bivariate correlation mRNA-mirna and prot-miRNA
This part loads the package and set proper hyper-parameters.
#-------------------------------------------------------------------------------
# Get plots and results on mrna-mirna and prot-mirna correlations
#-------------------------------------------------------------------------------
st_corr_mrna_mirna <- heatmap_corr(rna$data, mirna$data, mat1_name, mat3_name,
condv_annot, gene_annot,
file.path(out_folder, "mRNA-miRNA"))
#> [1] "Computing standard correlations ...................."
#> Standard correlation computed in: 1.39073204994202
#> [1] "Saving values in: results/mRNA-miRNA_standard-correlations.RData"
#> [1] "Saving heatmap .................................."
st_corr_prot_mirna <- heatmap_corr(prot$data, mirna$data, mat2_name, mat3_name,
condv_annot, gene_annot,
file.path(out_folder, "prot-miRNA"))
#> [1] "Computing standard correlations ...................."
#> Standard correlation computed in: 0.0121345520019531
#> [1] "Saving values in: results/prot-miRNA_standard-correlations.RData"
#> [1] "Saving heatmap .................................."
#-------------------------------------------------------------------------------
# Validating mrna-mirna and prot-mirna correlations
#-------------------------------------------------------------------------------
to_validate_mrna <- st_corr_mrna_mirna$table[st_corr_mrna_mirna$table$pvaluefdrcorr < fdr_th,]
valid_mrna <- validation_info(to_validate_mrna, condv, genes_proteins, 7, from_cor_mat = FALSE)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 3"
#> [1] "TargetScan validated: 0"
#> [1] "mirtarbase validated: 3"
hyper_test(valid_mrna$to_validate, valid_mrna$universe, valid_mrna$mirDB, valid_mrna$ts, valid_mrna$tarbase, file.path(out_folder, "mRNA_hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 3 176
#> 248 8049
#> Fisher test p-value: 0.498
#> Hypergeometric test p-value: 0.782
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 0 179
#> 66 8231
#> Fisher test p-value: 0.406
#> Hypergeometric test p-value: 0.757
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 3 176
#> 137 8160
#> Fisher test p-value: 0.771
#> Hypergeometric test p-value: 0.343
to_validate_prot <- st_corr_prot_mirna$table[st_corr_prot_mirna$table$pvaluefdrcorr < fdr_th,]
valid_prot <- validation_info(to_validate_prot, condv, genes_proteins, 7, from_cor_mat = FALSE)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 2"
#> [1] "TargetScan validated: 0"
#> [1] "mirtarbase validated: 1"
hyper_test(valid_prot$to_validate, valid_prot$universe, valid_prot$mirDB, valid_prot$ts, valid_prot$tarbase, file.path(out_folder, "prot_hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 2 46
#> 249 8179
#> Fisher test p-value: 0.653
#> Hypergeometric test p-value: 0.169
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 0 48
#> 66 8362
#> Fisher test p-value: 1
#> Hypergeometric test p-value: 0.314
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 1 47
#> 139 8289
#> Fisher test p-value: 0.551
#> Hypergeometric test p-value: 0.188
martalovino/MoPc documentation built on May 30, 2022, 4:11 p.m.
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MoPc: Multi-omics Partial correlation analysis
MoPc (Multi-omics partial correlation analysis) is software to reveal the miRNA drivers of a biological process in a multi-omics dataset, starting from the expression matrices of mRNA, proteomics, and miRNA.
In the presence of such matrices, the canonical approach to identify miRNA drivers consists in carrying out the mRNA-miRNA correlation (mRNA and miRNA matrices) and proteomics-miRNA (proteomics and miRNA matrices).
Although this approach has proved effective, MoPc introduces the partial correlation measurement between the mRNA and proteomic matrices, conditioned on the expression of miRNAs. In particular, MoPc selects as a driver the miRNAs in which the partial correlation p-value is better (therefore lower) than the bivariate mRNA-proteomic correlation p-value. The tool also verifies the enrichment of the mRNA-miRNA pairs found with what is already available in the literature in three well-known databases: mirDB, TargestScan, mirTarbase.
Installation
You can install the development version of MoPc from GitHub with:
# install.packages("devtools")
devtools::install_github("martalovino/MoPc")
Why should you use MoPc
MoPc identifies the miRNA drivers starting from a multi-omic dataset composed of:
- mRNA expression matrix
- proteomic expression matrix
- miRNA expression matrix exploiting the partial correlation between mRNA and proteomics conditioned on the expression of miRNAs.
Input
To run MoPc you need:
- mRNA expression matrix;
- proteomic expression matrix;
- miRNA expression matrix.
You can find a sample dataset directly inside the package, which you can easily replace with your data.
Output
The MoPc output consists of:
- A list with all the drivers found, with their enrichment;
- A map with all the miRNA drivers found in the dataset (in red, those enriched in at least one of mirDB, TargestScan, mirTarbase);
- A report of the enrichment values in the three databases mirDB, TargestScan, mirTarbase by hypergeometric test.
Please, see the example below.
Example
Below you will find an illustrative example of the input data and the tool’s output. The tool consists of several parts:
- Setup and parameters setting;
- Data loading and preprocessing;
- Core part of the tool: partial correlation computation, and validation information;
- Plot correlation;
- Hypergeometric test;
- Plots, validation and hypergeometric test on bivariate correlation mRNA-mirna and prot-miRNA
Setup and parameters setting
This part loads the package and set proper hyper-parameters.
library(MoPc)
# Parameters
corr_method <- "pearson"
mat1_name <- "rna"
mat2_name <- "prot"
mat3_name <- "mirna"
ncores = 7
fdr_th <- 0.05
improvement_th <- 0
out_folder <- "results"
dir.create(out_folder, showWarnings = FALSE)
Data loading and preprocessing
This part loads some sample data to work with and performs basic preprocessing.
# load breast cancer dataset
data(rna)
data(prot)
data(mirna)
# minimalist preprocecessing: selecting only common samples and common genes
# between rna and prot
d <- minimalist_preproc(rna, prot, mirna)
#> Total number of samples: 77
#> Total number of genes/proteins: 170
# annotate and sort genes/mirnas by chromosomal order
rna <- annotate_genes(d$mat1)
prot <- annotate_genes(d$mat2)
mirna <- annotate_mirnas(d$mat3)
#-------------------------------------------------------------------------------
# DEFINING ROWS and COLUMNS of partial correlation matrix
# usually genes on the rows and mirnas or CNAs on the columns
#-------------------------------------------------------------------------------
genes_proteins <- rownames(rna$data)
condv <- rownames(mirna$data)
gene_annot <- rna$annot
condv_annot <- mirna$annot
Core part of the tool: partial correlation computation, and validation information
This is the core part of the tool. It contains the partial correlation computation and the validation information.
#-------------------------------------------------------------------------------
# PARTIAL CORRELATION COMPUTAION
#-------------------------------------------------------------------------------
a <- compute_pc_values(rna$data, prot$data, mirna$data, corr_method, out_folder, ncores)
#> [1] "Computing partial correlation values..................................."
#> Standard correlation computed in: 2.97763848304749
#> [1] "Saving partial correlation values in: results/partial_estimates_pvalue_pearson.RData"
#> [1] "Data succesfully saved................................................."
mat <- get_relevant_improvement(rna$data, prot$data, a$pval, corr_method, improvement_th)
#> [1] "Computing improvement of partial correlation over standard correlation"
th <- quantile(unlist(mat[!is.na(mat)]), 0.75)
mat <- get_relevant_improvement(rna$data, prot$data, a$pval, corr_method, th)
#> [1] "Computing improvement of partial correlation over standard correlation"
#-------------------------------------------------------------------------------
# GET VALIDATION info
#-------------------------------------------------------------------------------
valid <- validation_info(mat, condv, genes_proteins, 7)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 47"
#> [1] "TargetScan validated: 7"
#> [1] "mirtarbase validated: 29"
Result 1: miRNA driver list
The list with all the drivers found, with their enrichment is available in the valid object.
head(valid$to_validate)
#> row col value validation_name Validated.mirDB
#> 1: HMGN2 hsa-miR-199a-5p 0.1817172 HMGN2_miR-199a-5p no
#> 2: SOS1 hsa-miR-199a-5p 0.009361374 SOS1_miR-199a-5p no
#> 3: IMMT hsa-miR-199a-5p 0.03892966 IMMT_miR-199a-5p no
#> 4: CAND2 hsa-miR-199a-5p 0.3524636 CAND2_miR-199a-5p no
#> 5: SRGAP3 hsa-miR-199a-5p 0.01547189 SRGAP3_miR-199a-5p yes
#> 6: ANAPC4 hsa-miR-199a-5p 0.01892321 ANAPC4_miR-199a-5p no
#> Validated.TS Validated.mirtarbase
#> 1: no no
#> 2: no no
#> 3: no no
#> 4: no no
#> 5: no no
#> 6: no no
Result 2: Plot correlation figure
Main plot of the package.
#-------------------------------------------------------------------------------
# PARTIAL CORRELATION PLOT
#-------------------------------------------------------------------------------
partial_corr_heatmap_plot(valid$to_validate, genes_proteins, condv, gene_annot, condv_annot,
mat, out_folder)
#> [1] "Plot final heatmap"
#> png
#> 2
Result 3: Hypergeometric test
This section computes the hypergeometric test to ensure the enrichment in the three databases: mirDB, TargetScan, mirTarBase. The hypergeometric test results are written in the hypergeometric_test_results.txt report file.
#-------------------------------------------------------------------------------
# Computing enrichment significance in mirDB, TargetScan, mirtarBase
#-------------------------------------------------------------------------------
hyper_test(valid$to_validate, valid$universe, valid$mirDB, valid$ts, valid$tarbase, file.path(out_folder, "hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 47 1432
#> 204 6793
#> Fisher test p-value: 0.612
#> Hypergeometric test p-value: 0.263
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 7 1472
#> 59 6938
#> Fisher test p-value: 0.191
#> Hypergeometric test p-value: 0.91
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 29 1450
#> 111 6886
#> Fisher test p-value: 0.312
#> Hypergeometric test p-value: 0.129
Plots, validation and hypergeometric test on bivariate correlation mRNA-mirna and prot-miRNA
This part loads the package and set proper hyper-parameters.
#-------------------------------------------------------------------------------
# Get plots and results on mrna-mirna and prot-mirna correlations
#-------------------------------------------------------------------------------
st_corr_mrna_mirna <- heatmap_corr(rna$data, mirna$data, mat1_name, mat3_name,
condv_annot, gene_annot,
file.path(out_folder, "mRNA-miRNA"))
#> [1] "Computing standard correlations ...................."
#> Standard correlation computed in: 1.39073204994202
#> [1] "Saving values in: results/mRNA-miRNA_standard-correlations.RData"
#> [1] "Saving heatmap .................................."
st_corr_prot_mirna <- heatmap_corr(prot$data, mirna$data, mat2_name, mat3_name,
condv_annot, gene_annot,
file.path(out_folder, "prot-miRNA"))
#> [1] "Computing standard correlations ...................."
#> Standard correlation computed in: 0.0121345520019531
#> [1] "Saving values in: results/prot-miRNA_standard-correlations.RData"
#> [1] "Saving heatmap .................................."
#-------------------------------------------------------------------------------
# Validating mrna-mirna and prot-mirna correlations
#-------------------------------------------------------------------------------
to_validate_mrna <- st_corr_mrna_mirna$table[st_corr_mrna_mirna$table$pvaluefdrcorr < fdr_th,]
valid_mrna <- validation_info(to_validate_mrna, condv, genes_proteins, 7, from_cor_mat = FALSE)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 3"
#> [1] "TargetScan validated: 0"
#> [1] "mirtarbase validated: 3"
hyper_test(valid_mrna$to_validate, valid_mrna$universe, valid_mrna$mirDB, valid_mrna$ts, valid_mrna$tarbase, file.path(out_folder, "mRNA_hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 3 176
#> 248 8049
#> Fisher test p-value: 0.498
#> Hypergeometric test p-value: 0.782
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 0 179
#> 66 8231
#> Fisher test p-value: 0.406
#> Hypergeometric test p-value: 0.757
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 3 176
#> 137 8160
#> Fisher test p-value: 0.771
#> Hypergeometric test p-value: 0.343
to_validate_prot <- st_corr_prot_mirna$table[st_corr_prot_mirna$table$pvaluefdrcorr < fdr_th,]
valid_prot <- validation_info(to_validate_prot, condv, genes_proteins, 7, from_cor_mat = FALSE)
#> [1] "Validating obtained miRNAs"
#> [1] "Validation process ongoing"
#> [1] "mirDB validated: 2"
#> [1] "TargetScan validated: 0"
#> [1] "mirtarbase validated: 1"
hyper_test(valid_prot$to_validate, valid_prot$universe, valid_prot$mirDB, valid_prot$ts, valid_prot$tarbase, file.path(out_folder, "prot_hypergeometric_test_results.txt"))
#> [1] "Computing Fisher and hypergeometric test .............................."
#> Matrix, Fisher and hypergeometric test on mirDB
#> M1 matrix:
#> 2 46
#> 249 8179
#> Fisher test p-value: 0.653
#> Hypergeometric test p-value: 0.169
#>
#>
#> Matrix, Fisher and hypergeometric test on TargetScan
#> M1 matrix:
#> 0 48
#> 66 8362
#> Fisher test p-value: 1
#> Hypergeometric test p-value: 0.314
#>
#>
#> Matrix, Fisher and hypergeometric test on mirtarbase
#> M1 matrix:
#> 1 47
#> 139 8289
#> Fisher test p-value: 0.551
#> Hypergeometric test p-value: 0.188
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