Cancer genome projects have generated massive genome and transcriptome sequencing data, which makes tumor-specific alterations such as somatic mutation and gene expression information easily available. To distinguish cancer drivers from passengers, we implement an R package “Bionexr” for integrative network-based analysis of gene somatic mutation and expression data. Bionexr provides these features:
Bionexr is consisted of four main modules:
prepare_ma()
will take some time to download the dataset used by gene module.library(bionexr) prepare_ma() # prepare data used by Bionexr
res.gene <- perform_gene_ppi(hnsc_mut_part, hnsc_exp_part) res.network <- perform_network_ppi(res.gene[[2]], res.gene[[3]]) g <- network_from_ppi(res.network) plot_ppi(g)
And the result would look like below, note that your running result might have a different layout, that's OK:
res.gene <- perform_gene_pathway(hnsc_mut_part, hnsc_exp_part) res.network <- perform_network_pathway(res.gene[[2]], res.gene[[3]], hnsc_expressed_genes) g <- network_from_significant_branches(res.network) plot_pathway(g)
And the result would look like below, note that your running result might have a different layout, that's OK:
firehose_get
is the main command to download cancer genome data from firehose. Here we use firehose_get
to download HNSC data.
mut_data <- firehose_get("HNSC", "mutation", run_date = "2015_08_21", run_type = "stddata") mut_data <- mut_data[[1]] mut_sample_ids <- unique(mut_data[[7]]) exp_data <- firehose_get("HNSC", "expression", run_date = "2015_08_21", run_type = "stddata") exp_data <- exp_data[[1]] exp_sample_ids <- colnames(exp_data) common_case <- intersect(mut_sample_ids, exp_sample_ids) exp_control <- grepl("-11$", exp_sample_ids) hnsc_mut <- mut_data[mut_data[[7]] %in% common_case, ] hnsc_exp <- exp_data[, (exp_sample_ids %in% common_case) | exp_control]
perform_gene_ppi
and perform_gene_pathway
are the two main commands for performing "Gene Analysis". As you can guess from the function name, perform_gene_ppi
is for PPIN-based approach and perform_gene_pathway
is for pathway-based approach.
See the instructions below, note that hnsc_mut
and hnsc_exp
are from "Data Download" module:
# For PPIN-based approach ppi.gene <- perform_gene_ppi(hnsc_mut, hnsc_exp) # For pathway-based approach pathway.gene <- perform_gene_pathway(hnsc_mut, hnsc_exp)
Note that before performing "Gene Analysis", run command prepare_ma()
first. This module would take a few time to finish, drink some coffee happily.
perform_network_ppi
and perform_network_pathway
are the two main commands for performing "Network Analysis". As the same to "Gene Analysis" module, perform_network_ppi
is for PPIN-based approach and perform_network_pathway
is for pathway-based approach.
See the instructions below, note that hnsc_exp
is from "Data Download" module, and ppi.gene
and pathway.gene
are from "Gene Analysis" module:
# For PPIN-based approach ppi.network <- perform_network_ppi(ppi.gene[[2]], ppi.gene[[3]]) # For pathway-based approach expressed_genes <- identify_expressed_genes(hnsc_exp) pathway.network <- perform_network_pathway(pathway.gene[[2]], pathway.gene[[3]], expressed_genes)
plot_ppi
and plot_pathway
are the two main commands for performing "Visualization" module.plot_ppi
is for PPIN-based approach's result and plot_pathway
is for pathway-based approach's result.
See the instructions below, note that ppi.network
and pathway.network
are from "Network Analysis" module:
# For PPIN-based approach's result ppi.g <- network_from_ppi(ppi.network) plot_ppi(ppi.g) # For pathway-based approach's result pathway.g <- network_from_significant_branches(pathway.network) plot_pathway(pathway.g)
The commands perform_main_ppi
and perform_main_pathway
can perform "Gene Analysis" and "Network Analysis", and the result can be visualized straightforward.
The example instructions are written below, note that hnsc_mut
and hnsc_exp
are from "Data Download" module:
# For PPIN-based approach prepare_ma() ppi.res <- perform_main_ppi(hnsc_mut, hnsc_exp, jobname = "HNSC", use_cache = TRUE) # Visualize PPIN-based result ppi.g <- network_from_ppi(ppi.res) plot_ppi(ppi.g) # For pathway-based approach prepare_ma() pathway.res <- perform_main_pathway(hnsc_mut, hnsc_exp, jobname = "HNSC", test = TRUE) pathway.g <- network_from_significant_branches(pathway.res) plot_pathway(pathway.g)
Please send email to yannis.pku@gmail.com if you have any questions.
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