Prepare miRNA-augmented pathways for integrative miRNA-mRNA pathway analyses

This function takes influential miRNA-mRNA interactions, identified by the mirnaIntegration() function, and adds them to biological pathways retrieved from a pathway database such as KEGG, WikiPathways and Reactome. The pathways returned from this function are needed to perform a topologically-aware integrative pathway analysis (TAIPA) through the topologicalAnalysis() function.

Usage

preparePathways(
  mirnaObj,
  database = "KEGG",
  organism = "Homo sapiens",
  minPc = 10,
  size = NULL,
  BPPARAM = bpparam()
)

Arguments

mirnaObj: A MirnaExperiment object containing miRNA and gene data
database: The name of the database to use. It must be one of: KEGG, Reactome, and WikiPathways. Default is KEGG
organism: The name of the organism under consideration. The different databases have different supported organisms. To see the list of supported organisms for a given database, use the supportedOrganisms() function. Default specie is Homo sapiens
minPc: The minimum percentage of measured features that a pathway must have for being considered in the analysis. Default is 10. See the details section for additional information
size: An optional numeric vector of length 2, containing the minimum and maximum number of nodes for each pathway. For example, if size = c(10, 150), all the pathways with less than 10 nodes and those with more than 150 nodes will be discarded. Default is NULL to keep all pathways
BPPARAM: The desired parallel computing behavior. This parameter defaults to BiocParallel::bpparam(), but this can be edited. See BiocParallel::bpparam() for information on parallel computing in R

Value

A list object containing the miRNA-augmented pathways as graphNEL

objects.

Details

To create augmented pathways, this function uses the graphite R package to download biological networks from the above mentioned databases. Then, each pathway is converted to a graph object, and significant miRNA-mRNA interactions are added to the network. Further, edge weights are added according to interaction type.

At this point, biological pathways with few nodes measured are excluded from this analysis. This is required because, during differential expression analysis, lowly expressed features are removed. Therefore, some pathways might result significantly affected even if only 1% of nodes is perturbed. The default behavior is to exclude pathways with less than 10% of representation (minPc = 10).

Finally, this function performs a breadth-first search (BFS) algorithm to topologically sort pathway nodes so that each individual node occurs after all its upstream nodes. Nodes within cycles are considered leaf nodes.

Information about pathway coverage, i.e. the percentage of nodes with expression measurments, edge weights, topological sorting order, and the parameters used to create the networks are all stored in the graphData slot of each graphNEL object.

References

Sales, G., Calura, E., Cavalieri, D. et al. graphite - a Bioconductor package to convert pathway topology to gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it

Examples


# \donttest{
# load example MirnaExperiment object
obj <- loadExamples()

# perform integration analysis with default settings
obj <- mirnaIntegration(obj)
#> Since data derive from paired samples, a correlation test will be used.
#> Performing Spearman's correlation analysis...
#> A statistically significant correlation between 215 miRNA-target pairs was found!

# retrieve pathways from KEGG and augment them with miRNA-gene interactions
paths <- preparePathways(obj)
#> Reading KEGG pathways from cache...
#> Adding miRNA-gene interactions to biological pathways...
#> Warning: 152 pathways have been ignored because they contain too few nodes with gene expression measurement.
#> Performing topological sorting of pathway nodes...

# perform the integrative pathway analysis with 1000 permutations
ipa <- topologicalAnalysis(obj, paths, nPerm = 1000)
#> Calculating pathway scores...
#> Generating random permutations...
#> Calculating p-values with 1000 permutations...
#> Correcting p-values through the max-T procedure...
#> The topologically-aware integrative pathway analysis reported 2 significantly altered pathways!

# access the results of pathway analysis
integratedPathways(ipa)
#>                                             pathway  coverage    score
#> Thyroid hormone synthesis Thyroid hormone synthesis 0.3469388 12.12941
#> Thyroid cancer                       Thyroid cancer 0.2820513 11.56291
#>                           normalized.score       P.Val adj.P.Val
#> Thyroid hormone synthesis         8.493830 0.000999001     0.014
#> Thyroid cancer                    7.203265 0.000999001     0.042

# create a dotplot of integrated pathways
integrationDotplot(ipa)


# explore a specific biological network
visualizeNetwork(ipa, "Thyroid hormone synthesis")

# }