README.md
In pouryany/PanomiR: Detection of miRNAs that regulate interacting groups of pathways
Introduction
PanomiR is a package for pathway and microRNA Analysis of gene
expression data. This document provides details about how to install and
utilize various functionality in PanomiR.
For questions, comments, and other queries, contact pouryany@gmail.com
Installation
PanomiR can be accessed via Bioconductor. To install, start R and run
the following code.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("PanomiR")
You can also install the latest development version of PanomiR using
GitHub.
devtools::install_github("pouryany/PanomiR")
Overview
PanomiR is a pipeline to prioritize disease-associated miRNAs based on
activity of disease-associated pathways. The input datasets for PanomiR
are (a) a gene expression disease dataset along with covariates, (b) a
background collection of pathways/genesets, and (c) a collection of
miRNAs containing gene targets.
The general workflow of PanomiR is (a) generation of pathway summary
statistics from gene expression data, (b) detection of differentially
activated pathways, (c) finding coherent groups, or clusters, of
differentially activated pathways, and (d) detecting miRNAs targeting
each group of pathways.
Individual steps of the workflow can be used in isolation to carry out
different analyses. The following sections outline each step and
material needed to execute PanomiR.
1. Pathway summarization
PanomiR can generate pathway activity profiles given a gene expression
dataset and a list of pathways.Pathway summaries are numbers that
represent the overall activity of genes that belong to each pathway.
These numbers are calculated based on a methodology previously described
in part in (Altschuler et al. 2013; Joachim et al. 2018). Briefly, genes
in each sample are ranked by their expression values and then pathway
summaries are calculated as the average rank-squared of genes within a
pathway. The summaries are then center and scaled (zNormalized) across
samples.
The default list of background pathways in PanomiR is formatted into a
table (data("path_gene_table")). The table is based on canonical
pathways collection of Molecular Signatures Database (MSigDB) V6.2 and
it contains annotated pathways from a variety of sources (Liberzon et
al. 2011).
** Users interested in using other pathway/geneset backgrounds, such
as newer versions of MSigDB or KEGG, should refer to the
appendix of this manual.
This section uses a reduced example dataset from The Cancer Genome Atlas
(TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset to generate Pathway
summary statistics (Ally et al. 2017). Note: Make sure that you
select gene representation type that matches the rownames of your
expression data. The type can be modified using the id argument in the
function below. The default value for this argument is ENSEMBL.
library(PanomiR)
# Pathway reference from the PanomiR package
data("path_gene_table")
data("miniTestsPanomiR")
# Generating pathway summary statistics
summaries <- pathwaySummary(miniTestsPanomiR$mini_LIHC_Exp,
path_gene_table, method = "x2",
zNormalize = TRUE, id = "ENSEMBL")
head(summaries)[,1:2]
## TCGA-BC-A10S-01A-22R-A131-07
## BIOCARTA_41BB_PATHWAY -0.1506216
## BIOCARTA_ACE2_PATHWAY -0.5676447
## BIOCARTA_ACH_PATHWAY -0.3211747
## BIOCARTA_ACTINY_PATHWAY 1.4363526
## BIOCARTA_AGPCR_PATHWAY -0.1948523
## BIOCARTA_AGR_PATHWAY 0.6802993
## TCGA-BC-4073-01B-02R-A131-07
## BIOCARTA_41BB_PATHWAY -0.1269436
## BIOCARTA_ACE2_PATHWAY -0.8327436
## BIOCARTA_ACH_PATHWAY -0.4390042
## BIOCARTA_ACTINY_PATHWAY 1.4975456
## BIOCARTA_AGPCR_PATHWAY -0.2499193
## BIOCARTA_AGR_PATHWAY 0.5420588
2. Differential Pathway activation
Once you generate the pathway activity profiles, as discussed in the
last section, there are several analysis that you can perform. We have
bundled some of the most important ones into standalone functions. Here,
we describe differential pathway activation profiling, which is
examining differences in pathway activity profiles in user-determined
conditions.
At this stage you need to provide a pathway-gene association table, an
expression dataset, and a covariates table. You need to specity what
covariates you would like to contrast. You also need to provide a
contrast, as formatted in limma. If the contrast is not provided, the
function assumes the first two levels of the provided contrast
covariate. Note: make sure the contrast covariate is formatted as
factor.
output0 <- differentialPathwayAnalysis(
geneCounts = miniTestsPanomiR$mini_LIHC_Exp,
pathways = path_gene_table,
covariates = miniTestsPanomiR$mini_LIHC_Cov,
condition = 'shortLetterCode')
de.paths <- output0$DEP
head(de.paths,3)
## logFC AveExpr t
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING -0.9159376 0.3044281 -10.404966
## BIOCARTA_AKT_PATHWAY -0.5744103 0.3123897 -6.770069
## PID_IL5_PATHWAY -0.6219876 0.4240432 -6.255756
## P.Value adj.P.Val B
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING 1.942463e-06 0.002012391 5.240095
## BIOCARTA_AKT_PATHWAY 6.903010e-05 0.035757593 2.126311
## PID_IL5_PATHWAY 1.276971e-04 0.040289104 1.550780
## contrast
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING shortLetterCodeTP-shortLetterCodeNT
## BIOCARTA_AKT_PATHWAY shortLetterCodeTP-shortLetterCodeNT
## PID_IL5_PATHWAY shortLetterCodeTP-shortLetterCodeNT
3. Finding clusters of pathways
PanomiR provides a function to find groups coordinated differentially
activated pathways based on a pathway co-expression network (PCxN)
previously described in (Pita-Juárez et al. 2018). Briefly, PCxN is a
network where nodes are pathways and links are co-expression between the
nodes. It is formatted into a table were rows represent edges. The edges
of PCxN are marked by two numbers, 1- a correlation co-efficient and 2-
a significance adjusted p-value. Cut-offs for both of these numbers can
be manually set using PanomiR functions. See function manuals for more
info.
PCxN and its associated genesets are already released and can be
accessed through following Bioconductor packages:
pcxn and
pcxnData.
Here we have provided a small version of PCxN for tutorial purposes. A
more recent version of PCxN based on MSigDB V6.2 is available through
the data repository accompanying PanomiR manuscript, which can be found
here.
# using an updated version of pcxn
set.seed(2)
pathwayClustsLIHC <- mappingPathwaysClusters(
pcxn = miniTestsPanomiR$miniPCXN,
dePathways = de.paths[1:300,],
topPathways = 200,
outDir=".",
plot = FALSE,
subplot = FALSE,
prefix='',
clusteringFunction = "cluster_louvain",
correlationCutOff = 0.1)
head(pathwayClustsLIHC$Clustering)
## Pathway cluster
## 1 BIOCARTA_NO1_PATHWAY 1
## 2 BIOCARTA_AKT_PATHWAY 1
## 3 BIOCARTA_ALK_PATHWAY 1
## 4 BIOCARTA_RANKL_PATHWAY 1
## 5 BIOCARTA_MCM_PATHWAY 3
## 6 BIOCARTA_CELLCYCLE_PATHWAY 3
4. Prioritizing miRNAs per cluster of pathways.
PanomiR identifies miRNAs that target clusters of pathways, as defined
in the last section. In order to this, you would need a reference table
of miRNA-Pathway association score (enrichment). We recommend using a
customized miRNA-Pathway association table, tailored to your
experimental data. This section provides an overview of prioritization
process. Readers interested in knowing more about the technical details
of PanomiR are refered to accompaniying publication (Work under
preparation).
Enrichment reference
Here, we provide a preprocessed small example table of miRNA-pathway
enrichment in miniTestsPanomiR$miniEnrich object. This table contains
enrichment analysis results using Fisher’s Exact Test between MSigDB
pathways and TargetScan miRNA targets. The individual components are
accessible via data(msigdb_c2) and data(targetScan_03) (Agarwal et
al. 2015; Liberzon et al. 2011). This example table is contains only a
full subset of the full pairwise enrichment. You can refer to section
5 of this manual on how to create full tables and how to
customize them to your specific gene expression data.
Generating targeting scores
PanomiR generates a score for individual miRNAs targeting a group of
pathways. These scores are generated based on the reference enrichment
table. We are interested in knowing to what extent each miRNA targets
pathway clusters identified in the last step (see previous section).
PanomiR constructs a null distribution of this targeting score for each
miRNA. The significance of observed scores from a given group of
pathways (clusters in this case) is contrasted against the null
distribution to generate a targeting p-value. These p-values are used to
rank miRNAs per cluster.
Sampling parameter
The above described process requires repeated sampling to empirically
obtain the null distribution. The argument sampRate denotes the number
of repeats in the process. Note that in the example below, we use a
sampling rate of 50, the recommended rate is between 500-1000. Also, we
set the saveSampling argument to FALSE. This argument, if set TRUE,
ensures that the null distribution is obtain only once. This argument
should be set to TRUE if you wish to save your sampling and check for
different outputs from the clustering algorithms or pathway thresholds.
set.seed(1)
output2 <- prioritizeMicroRNA(enriches0 = miniTestsPanomiR$miniEnrich,
pathClust = miniTestsPanomiR$miniPathClusts$Clustering,
topClust = 1,
sampRate = 50,
method = c("aggInv"),
outDir = "Output/",
dataDir = "outData/",
saveSampling = FALSE,
runJackKnife = FALSE,
numCores = 1,
prefix = "outmiR",
saveCSV = FALSE)
## Working on Cluster1.
## Performing aggInv function.
## aggInv Method Done
head(output2$Cluster1)
## x cluster_hits aggInv_cover aggInv_pval
## 1 hsa-miR-101-3p.2 6 -1.9566603 0.0001216703
## 2 hsa-miR-101-3p.1 4 -0.3395771 0.0006214715
## 3 hsa-miR-124-3p.2/hsa-miR-506-3p 7 -0.2357761 0.0008599272
## 4 hsa-miR-1247-5p 4 -1.6599230 0.0021625662
## 5 hsa-miR-1249-3p 1 -2.4578993 0.0042061415
## 6 hsa-miR-1252-5p 4 -0.7572036 0.0050836835
## aggInv_fdr
## 1 0.002433406
## 2 0.005732848
## 3 0.005732848
## 4 0.010812831
## 5 0.016824566
## 6 0.016945612
5. miRNA-Pathway enrichment tables
PanomiR best performs on tissue/experiment-customized datasets. In order
to do this, you need to create a customized enrichment table. You can
simply do so by using the pathway and miRNA list that we have provided
as a part of the package. simply, plug in the name of the genes present
(expressed) in your experiment in the following code
# using an updated version of pcxn
data("msigdb_c2")
data("targetScan_03")
customeTableEnrich <- miRNAPathwayEnrichment(mirSets = targetScan_03,
pathwaySets = msigdb_c2,
geneSelection = yourGenes,
mirSelection = yourMicroRNAs,
fromID = "ENSEMBL",
toID = "ENTREZID",
minPathSize = 9,
numCores = 1,
outDir = ".",
saveOutName = NULL)
In the above section, the field fromID denotes the gene representation
format of your input list. Here is a quick example that runs fast. Note
that the miRNAPathwayEnrichment() function creates a detailed report
with parameters that are used internally. To get a smaller table that is
suitable for publication purposes, use reportEnrichment() function.
# using an updated version of pcxn
data("msigdb_c2")
data("targetScan_03")
tempEnrich <-miRNAPathwayEnrichment(targetScan_03[1:30],msigdb_c2[1:30])
head(reportEnrichment(tempEnrich))
## miRNA Pathway pval pAdjust
## 14 hsa-miR-1252-5p BIOCARTA_CSK_PATHWAY 0.00179 0.259
## 55 hsa-miR-1271-5p/hsa-miR-96-5p BIOCARTA_AKT_PATHWAY 0.01100 1.000
## 122 hsa-miR-124-3p.1 BIOCARTA_RANKL_PATHWAY 0.01550 1.000
## 53 hsa-miR-124-3p.2/hsa-miR-506-3p BIOCARTA_AKT_PATHWAY 0.03740 1.000
## 99 hsa-miR-1252-5p BIOCARTA_AGPCR_PATHWAY 0.03940 1.000
## 112 hsa-miR-124-3p.1 BIOCARTA_BCR_PATHWAY 0.05360 1.000
6. Customized genesets and recommendations
PanomiR can integrate genesets and pathways from external sources
including those annotated in MSigDB. In order to do so, you need to
provide a GeneSetCollection object as defined in the GSEABase
package.
The example below illustrates how to use external sources to create your
own customized pathway-gene association table. This customized can then
replaced path_gene_table input in functions described in sections 1,2,
and 5 of this manual.
data("gscExample")
newPathGeneTable <-tableFromGSC(gscExample)
##
##
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
head(newPathGeneTable)
## # A tibble: 6 × 3
## Pathway ENTREZID ENSEMBL
## <chr> <chr> <chr>
## 1 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 124245 ENSG00000158545
## 2 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 1800 ENSG00000015413
## 3 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 404550 ENSG00000154102
## 4 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 6687 ENSG00000197912
## 5 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 750 ENSG00000221819
## 6 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 2588 ENSG00000141012
The the pathway correlation network in section 3 is build upon an MSigDB
V6.2, canonical pathways (cp) collection dataset that includes KEGG
Pathways. KEGG prohibits distribution of its pathways by third parties.
However, use can access desired versions of MSigDB in gmt format via
this link
(Subramanian et al. 2005).
The library msigdb provides an programmatic interface to download
different geneset collections. Including how to add KEGG pathways or
download mouse genesets. Use the this MSigDB
tutorial
to create your desired gene sets.
You can also use the following code chunk to create pathway-gene
association tables from gmt files.
library(GSEABase)
yourGeneSetCollection <- getGmt("YOUR GMT FILE")
newPathGeneTable <- tableFromGSC(yourGeneSetCollection)
Session info
sessionInfo()
## R Under development (unstable) (2021-12-03 r81290)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] PanomiR_0.99.4
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.21.0 colorspace_2.0-2 ggtree_3.3.1
## [4] ellipsis_0.3.2 qvalue_2.27.0 XVector_0.35.0
## [7] aplot_0.1.1 rstudioapi_0.13 farver_2.1.0
## [10] graphlayouts_0.8.0 ggrepel_0.9.1 bit64_4.0.5
## [13] scatterpie_0.1.7 AnnotationDbi_1.57.1 fansi_0.5.0
## [16] splines_4.2.0 cachem_1.0.6 GOSemSim_2.21.0
## [19] knitr_1.37 polyclip_1.10-0 jsonlite_1.7.2
## [22] annotate_1.73.0 GO.db_3.14.0 png_0.1-7
## [25] graph_1.73.0 ggforce_0.3.3 compiler_4.2.0
## [28] httr_1.4.2 assertthat_0.2.1 Matrix_1.4-0
## [31] fastmap_1.1.0 lazyeval_0.2.2 cli_3.1.0
## [34] limma_3.51.2 tweenr_1.0.2 htmltools_0.5.2
## [37] tools_4.2.0 igraph_1.2.11 gtable_0.3.0
## [40] glue_1.6.0 GenomeInfoDbData_1.2.7 reshape2_1.4.4
## [43] DO.db_2.9 dplyr_1.0.7 fastmatch_1.1-3
## [46] Rcpp_1.0.7 enrichplot_1.15.2 Biobase_2.55.0
## [49] vctrs_0.3.8 Biostrings_2.63.1 ape_5.6-1
## [52] nlme_3.1-153 ggraph_2.0.5 xfun_0.29
## [55] stringr_1.4.0 lifecycle_1.0.1 clusterProfiler_4.3.1
## [58] XML_3.99-0.8 DOSE_3.21.2 org.Hs.eg.db_3.14.0
## [61] zlibbioc_1.41.0 MASS_7.3-54 scales_1.1.1
## [64] tidygraph_1.2.0 parallel_4.2.0 RColorBrewer_1.1-2
## [67] yaml_2.2.1 memoise_2.0.1 gridExtra_2.3
## [70] ggplot2_3.3.5 downloader_0.4 ggfun_0.0.4
## [73] yulab.utils_0.0.4 stringi_1.7.6 RSQLite_2.2.9
## [76] S4Vectors_0.33.9 tidytree_0.3.6 BiocGenerics_0.41.2
## [79] BiocParallel_1.29.10 GenomeInfoDb_1.31.1 rlang_0.4.12
## [82] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
## [85] lattice_0.20-45 purrr_0.3.4 treeio_1.19.1
## [88] patchwork_1.1.1 shadowtext_0.1.0 bit_4.0.4
## [91] tidyselect_1.1.1 GSEABase_1.57.0 plyr_1.8.6
## [94] magrittr_2.0.1 R6_2.5.1 IRanges_2.29.1
## [97] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
## [100] withr_2.4.3 KEGGREST_1.35.0 RCurl_1.98-1.5
## [103] tibble_3.1.6 crayon_1.4.2 utf8_1.2.2
## [106] rmarkdown_2.11 viridis_0.6.2 grid_4.2.0
## [109] data.table_1.14.2 blob_1.2.2 forcats_0.5.1
## [112] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
## [115] gridGraphics_0.5-1 stats4_4.2.0 munsell_0.5.0
## [118] viridisLite_0.4.0 ggplotify_0.1.0
References
Agarwal, Vikram, George W Bell, Jin-Wu Nam, and David P Bartel. 2015.
“Predicting Effective microRNA Target Sites in Mammalian mRNAs.” *Elife*
4: e05005.
Ally, Adrian, Miruna Balasundaram, Rebecca Carlsen, Eric Chuah, Amanda
Clarke, Noreen Dhalla, Robert A Holt, et al. 2017. “Comprehensive and
Integrative Genomic Characterization of Hepatocellular Carcinoma.”
*Cell* 169 (7): 1327–41.
Altschuler, Gabriel M, Oliver Hofmann, Irina Kalatskaya, Rebecca Payne,
Shannan J Ho Sui, Uma Saxena, Andrei V Krivtsov, et al. 2013.
“Pathprinting: An Integrative Approach to Understand the Functional
Basis of Disease.” *Genome Medicine* 5 (7): 1–13.
Joachim, Rose B, Gabriel M Altschuler, John N Hutchinson, Hector R Wong,
Winston A Hide, and Lester Kobzik. 2018. “The Relative Resistance of
Children to Sepsis Mortality: From Pathways to Drug Candidates.”
*Molecular Systems Biology* 14 (5): e7998.
Liberzon, Arthur, Aravind Subramanian, Reid Pinchback, Helga
Thorvaldsdóttir, Pablo Tamayo, and Jill P Mesirov. 2011. “Molecular
Signatures Database (MSigDB) 3.0.” *Bioinformatics* 27 (12): 1739–40.
Pita-Juárez, Yered, Gabriel Altschuler, Sokratis Kariotis, Wenbin Wei,
Katjuša Koler, Claire Green, Rudolph E Tanzi, and Winston Hide. 2018.
“The Pathway Coexpression Network: Revealing Pathway Relationships.”
*PLoS Computational Biology* 14 (3): e1006042.
Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee,
Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, et al. 2005.
“Gene Set Enrichment Analysis: A Knowledge-Based Approach for
Interpreting Genome-Wide Expression Profiles.” *Proceedings of the
National Academy of Sciences* 102 (43): 15545–50.
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Introduction
PanomiR is a package for pathway and microRNA Analysis of gene expression data. This document provides details about how to install and utilize various functionality in PanomiR.
For questions, comments, and other queries, contact pouryany@gmail.com
Installation
PanomiR can be accessed via Bioconductor. To install, start R and run the following code.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("PanomiR")
You can also install the latest development version of PanomiR using GitHub.
devtools::install_github("pouryany/PanomiR")
Overview
PanomiR is a pipeline to prioritize disease-associated miRNAs based on activity of disease-associated pathways. The input datasets for PanomiR are (a) a gene expression disease dataset along with covariates, (b) a background collection of pathways/genesets, and (c) a collection of miRNAs containing gene targets.
The general workflow of PanomiR is (a) generation of pathway summary statistics from gene expression data, (b) detection of differentially activated pathways, (c) finding coherent groups, or clusters, of differentially activated pathways, and (d) detecting miRNAs targeting each group of pathways.
Individual steps of the workflow can be used in isolation to carry out different analyses. The following sections outline each step and material needed to execute PanomiR.
1. Pathway summarization
PanomiR can generate pathway activity profiles given a gene expression dataset and a list of pathways.Pathway summaries are numbers that represent the overall activity of genes that belong to each pathway. These numbers are calculated based on a methodology previously described in part in (Altschuler et al. 2013; Joachim et al. 2018). Briefly, genes in each sample are ranked by their expression values and then pathway summaries are calculated as the average rank-squared of genes within a pathway. The summaries are then center and scaled (zNormalized) across samples.
The default list of background pathways in PanomiR is formatted into a
table (data("path_gene_table")). The table is based on canonical
pathways collection of Molecular Signatures Database (MSigDB) V6.2 and
it contains annotated pathways from a variety of sources (Liberzon et
al. 2011).
** Users interested in using other pathway/geneset backgrounds, such as newer versions of MSigDB or KEGG, should refer to the appendix of this manual.
This section uses a reduced example dataset from The Cancer Genome Atlas
(TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset to generate Pathway
summary statistics (Ally et al. 2017). Note: Make sure that you
select gene representation type that matches the rownames of your
expression data. The type can be modified using the id argument in the
function below. The default value for this argument is ENSEMBL.
library(PanomiR)
# Pathway reference from the PanomiR package
data("path_gene_table")
data("miniTestsPanomiR")
# Generating pathway summary statistics
summaries <- pathwaySummary(miniTestsPanomiR$mini_LIHC_Exp,
path_gene_table, method = "x2",
zNormalize = TRUE, id = "ENSEMBL")
head(summaries)[,1:2]
## TCGA-BC-A10S-01A-22R-A131-07
## BIOCARTA_41BB_PATHWAY -0.1506216
## BIOCARTA_ACE2_PATHWAY -0.5676447
## BIOCARTA_ACH_PATHWAY -0.3211747
## BIOCARTA_ACTINY_PATHWAY 1.4363526
## BIOCARTA_AGPCR_PATHWAY -0.1948523
## BIOCARTA_AGR_PATHWAY 0.6802993
## TCGA-BC-4073-01B-02R-A131-07
## BIOCARTA_41BB_PATHWAY -0.1269436
## BIOCARTA_ACE2_PATHWAY -0.8327436
## BIOCARTA_ACH_PATHWAY -0.4390042
## BIOCARTA_ACTINY_PATHWAY 1.4975456
## BIOCARTA_AGPCR_PATHWAY -0.2499193
## BIOCARTA_AGR_PATHWAY 0.5420588
2. Differential Pathway activation
Once you generate the pathway activity profiles, as discussed in the last section, there are several analysis that you can perform. We have bundled some of the most important ones into standalone functions. Here, we describe differential pathway activation profiling, which is examining differences in pathway activity profiles in user-determined conditions.
At this stage you need to provide a pathway-gene association table, an expression dataset, and a covariates table. You need to specity what covariates you would like to contrast. You also need to provide a contrast, as formatted in limma. If the contrast is not provided, the function assumes the first two levels of the provided contrast covariate. Note: make sure the contrast covariate is formatted as factor.
output0 <- differentialPathwayAnalysis(
geneCounts = miniTestsPanomiR$mini_LIHC_Exp,
pathways = path_gene_table,
covariates = miniTestsPanomiR$mini_LIHC_Cov,
condition = 'shortLetterCode')
de.paths <- output0$DEP
head(de.paths,3)
## logFC AveExpr t
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING -0.9159376 0.3044281 -10.404966
## BIOCARTA_AKT_PATHWAY -0.5744103 0.3123897 -6.770069
## PID_IL5_PATHWAY -0.6219876 0.4240432 -6.255756
## P.Value adj.P.Val B
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING 1.942463e-06 0.002012391 5.240095
## BIOCARTA_AKT_PATHWAY 6.903010e-05 0.035757593 2.126311
## PID_IL5_PATHWAY 1.276971e-04 0.040289104 1.550780
## contrast
## REACTOME_GROWTH_HORMONE_RECEPTOR_SIGNALING shortLetterCodeTP-shortLetterCodeNT
## BIOCARTA_AKT_PATHWAY shortLetterCodeTP-shortLetterCodeNT
## PID_IL5_PATHWAY shortLetterCodeTP-shortLetterCodeNT
3. Finding clusters of pathways
PanomiR provides a function to find groups coordinated differentially activated pathways based on a pathway co-expression network (PCxN) previously described in (Pita-Juárez et al. 2018). Briefly, PCxN is a network where nodes are pathways and links are co-expression between the nodes. It is formatted into a table were rows represent edges. The edges of PCxN are marked by two numbers, 1- a correlation co-efficient and 2- a significance adjusted p-value. Cut-offs for both of these numbers can be manually set using PanomiR functions. See function manuals for more info.
PCxN and its associated genesets are already released and can be accessed through following Bioconductor packages: pcxn and pcxnData.
Here we have provided a small version of PCxN for tutorial purposes. A more recent version of PCxN based on MSigDB V6.2 is available through the data repository accompanying PanomiR manuscript, which can be found here.
# using an updated version of pcxn
set.seed(2)
pathwayClustsLIHC <- mappingPathwaysClusters(
pcxn = miniTestsPanomiR$miniPCXN,
dePathways = de.paths[1:300,],
topPathways = 200,
outDir=".",
plot = FALSE,
subplot = FALSE,
prefix='',
clusteringFunction = "cluster_louvain",
correlationCutOff = 0.1)
head(pathwayClustsLIHC$Clustering)
## Pathway cluster
## 1 BIOCARTA_NO1_PATHWAY 1
## 2 BIOCARTA_AKT_PATHWAY 1
## 3 BIOCARTA_ALK_PATHWAY 1
## 4 BIOCARTA_RANKL_PATHWAY 1
## 5 BIOCARTA_MCM_PATHWAY 3
## 6 BIOCARTA_CELLCYCLE_PATHWAY 3
4. Prioritizing miRNAs per cluster of pathways.
PanomiR identifies miRNAs that target clusters of pathways, as defined in the last section. In order to this, you would need a reference table of miRNA-Pathway association score (enrichment). We recommend using a customized miRNA-Pathway association table, tailored to your experimental data. This section provides an overview of prioritization process. Readers interested in knowing more about the technical details of PanomiR are refered to accompaniying publication (Work under preparation).
Enrichment reference
Here, we provide a preprocessed small example table of miRNA-pathway
enrichment in miniTestsPanomiR$miniEnrich object. This table contains
enrichment analysis results using Fisher’s Exact Test between MSigDB
pathways and TargetScan miRNA targets. The individual components are
accessible via data(msigdb_c2) and data(targetScan_03) (Agarwal et
al. 2015; Liberzon et al. 2011). This example table is contains only a
full subset of the full pairwise enrichment. You can refer to section
5 of this manual on how to create full tables and how to
customize them to your specific gene expression data.
Generating targeting scores
PanomiR generates a score for individual miRNAs targeting a group of pathways. These scores are generated based on the reference enrichment table. We are interested in knowing to what extent each miRNA targets pathway clusters identified in the last step (see previous section). PanomiR constructs a null distribution of this targeting score for each miRNA. The significance of observed scores from a given group of pathways (clusters in this case) is contrasted against the null distribution to generate a targeting p-value. These p-values are used to rank miRNAs per cluster.
Sampling parameter
The above described process requires repeated sampling to empirically
obtain the null distribution. The argument sampRate denotes the number
of repeats in the process. Note that in the example below, we use a
sampling rate of 50, the recommended rate is between 500-1000. Also, we
set the saveSampling argument to FALSE. This argument, if set TRUE,
ensures that the null distribution is obtain only once. This argument
should be set to TRUE if you wish to save your sampling and check for
different outputs from the clustering algorithms or pathway thresholds.
set.seed(1)
output2 <- prioritizeMicroRNA(enriches0 = miniTestsPanomiR$miniEnrich,
pathClust = miniTestsPanomiR$miniPathClusts$Clustering,
topClust = 1,
sampRate = 50,
method = c("aggInv"),
outDir = "Output/",
dataDir = "outData/",
saveSampling = FALSE,
runJackKnife = FALSE,
numCores = 1,
prefix = "outmiR",
saveCSV = FALSE)
## Working on Cluster1.
## Performing aggInv function.
## aggInv Method Done
head(output2$Cluster1)
## x cluster_hits aggInv_cover aggInv_pval
## 1 hsa-miR-101-3p.2 6 -1.9566603 0.0001216703
## 2 hsa-miR-101-3p.1 4 -0.3395771 0.0006214715
## 3 hsa-miR-124-3p.2/hsa-miR-506-3p 7 -0.2357761 0.0008599272
## 4 hsa-miR-1247-5p 4 -1.6599230 0.0021625662
## 5 hsa-miR-1249-3p 1 -2.4578993 0.0042061415
## 6 hsa-miR-1252-5p 4 -0.7572036 0.0050836835
## aggInv_fdr
## 1 0.002433406
## 2 0.005732848
## 3 0.005732848
## 4 0.010812831
## 5 0.016824566
## 6 0.016945612
5. miRNA-Pathway enrichment tables
PanomiR best performs on tissue/experiment-customized datasets. In order to do this, you need to create a customized enrichment table. You can simply do so by using the pathway and miRNA list that we have provided as a part of the package. simply, plug in the name of the genes present (expressed) in your experiment in the following code
# using an updated version of pcxn
data("msigdb_c2")
data("targetScan_03")
customeTableEnrich <- miRNAPathwayEnrichment(mirSets = targetScan_03,
pathwaySets = msigdb_c2,
geneSelection = yourGenes,
mirSelection = yourMicroRNAs,
fromID = "ENSEMBL",
toID = "ENTREZID",
minPathSize = 9,
numCores = 1,
outDir = ".",
saveOutName = NULL)
In the above section, the field fromID denotes the gene representation
format of your input list. Here is a quick example that runs fast. Note
that the miRNAPathwayEnrichment() function creates a detailed report
with parameters that are used internally. To get a smaller table that is
suitable for publication purposes, use reportEnrichment() function.
# using an updated version of pcxn
data("msigdb_c2")
data("targetScan_03")
tempEnrich <-miRNAPathwayEnrichment(targetScan_03[1:30],msigdb_c2[1:30])
head(reportEnrichment(tempEnrich))
## miRNA Pathway pval pAdjust
## 14 hsa-miR-1252-5p BIOCARTA_CSK_PATHWAY 0.00179 0.259
## 55 hsa-miR-1271-5p/hsa-miR-96-5p BIOCARTA_AKT_PATHWAY 0.01100 1.000
## 122 hsa-miR-124-3p.1 BIOCARTA_RANKL_PATHWAY 0.01550 1.000
## 53 hsa-miR-124-3p.2/hsa-miR-506-3p BIOCARTA_AKT_PATHWAY 0.03740 1.000
## 99 hsa-miR-1252-5p BIOCARTA_AGPCR_PATHWAY 0.03940 1.000
## 112 hsa-miR-124-3p.1 BIOCARTA_BCR_PATHWAY 0.05360 1.000
6. Customized genesets and recommendations
PanomiR can integrate genesets and pathways from external sources
including those annotated in MSigDB. In order to do so, you need to
provide a GeneSetCollection object as defined in the GSEABase
package.
The example below illustrates how to use external sources to create your
own customized pathway-gene association table. This customized can then
replaced path_gene_table input in functions described in sections 1,2,
and 5 of this manual.
data("gscExample")
newPathGeneTable <-tableFromGSC(gscExample)
##
##
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
head(newPathGeneTable)
## # A tibble: 6 × 3
## Pathway ENTREZID ENSEMBL
## <chr> <chr> <chr>
## 1 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 124245 ENSG00000158545
## 2 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 1800 ENSG00000015413
## 3 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 404550 ENSG00000154102
## 4 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 6687 ENSG00000197912
## 5 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 750 ENSG00000221819
## 6 NIKOLSKY_BREAST_CANCER_16Q24_AMPLICON 2588 ENSG00000141012
The the pathway correlation network in section 3 is build upon an MSigDB V6.2, canonical pathways (cp) collection dataset that includes KEGG Pathways. KEGG prohibits distribution of its pathways by third parties. However, use can access desired versions of MSigDB in gmt format via this link (Subramanian et al. 2005).
The library msigdb provides an programmatic interface to download
different geneset collections. Including how to add KEGG pathways or
download mouse genesets. Use the this MSigDB
tutorial
to create your desired gene sets.
You can also use the following code chunk to create pathway-gene association tables from gmt files.
library(GSEABase)
yourGeneSetCollection <- getGmt("YOUR GMT FILE")
newPathGeneTable <- tableFromGSC(yourGeneSetCollection)
Session info
sessionInfo()
## R Under development (unstable) (2021-12-03 r81290)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] PanomiR_0.99.4
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.21.0 colorspace_2.0-2 ggtree_3.3.1
## [4] ellipsis_0.3.2 qvalue_2.27.0 XVector_0.35.0
## [7] aplot_0.1.1 rstudioapi_0.13 farver_2.1.0
## [10] graphlayouts_0.8.0 ggrepel_0.9.1 bit64_4.0.5
## [13] scatterpie_0.1.7 AnnotationDbi_1.57.1 fansi_0.5.0
## [16] splines_4.2.0 cachem_1.0.6 GOSemSim_2.21.0
## [19] knitr_1.37 polyclip_1.10-0 jsonlite_1.7.2
## [22] annotate_1.73.0 GO.db_3.14.0 png_0.1-7
## [25] graph_1.73.0 ggforce_0.3.3 compiler_4.2.0
## [28] httr_1.4.2 assertthat_0.2.1 Matrix_1.4-0
## [31] fastmap_1.1.0 lazyeval_0.2.2 cli_3.1.0
## [34] limma_3.51.2 tweenr_1.0.2 htmltools_0.5.2
## [37] tools_4.2.0 igraph_1.2.11 gtable_0.3.0
## [40] glue_1.6.0 GenomeInfoDbData_1.2.7 reshape2_1.4.4
## [43] DO.db_2.9 dplyr_1.0.7 fastmatch_1.1-3
## [46] Rcpp_1.0.7 enrichplot_1.15.2 Biobase_2.55.0
## [49] vctrs_0.3.8 Biostrings_2.63.1 ape_5.6-1
## [52] nlme_3.1-153 ggraph_2.0.5 xfun_0.29
## [55] stringr_1.4.0 lifecycle_1.0.1 clusterProfiler_4.3.1
## [58] XML_3.99-0.8 DOSE_3.21.2 org.Hs.eg.db_3.14.0
## [61] zlibbioc_1.41.0 MASS_7.3-54 scales_1.1.1
## [64] tidygraph_1.2.0 parallel_4.2.0 RColorBrewer_1.1-2
## [67] yaml_2.2.1 memoise_2.0.1 gridExtra_2.3
## [70] ggplot2_3.3.5 downloader_0.4 ggfun_0.0.4
## [73] yulab.utils_0.0.4 stringi_1.7.6 RSQLite_2.2.9
## [76] S4Vectors_0.33.9 tidytree_0.3.6 BiocGenerics_0.41.2
## [79] BiocParallel_1.29.10 GenomeInfoDb_1.31.1 rlang_0.4.12
## [82] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
## [85] lattice_0.20-45 purrr_0.3.4 treeio_1.19.1
## [88] patchwork_1.1.1 shadowtext_0.1.0 bit_4.0.4
## [91] tidyselect_1.1.1 GSEABase_1.57.0 plyr_1.8.6
## [94] magrittr_2.0.1 R6_2.5.1 IRanges_2.29.1
## [97] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
## [100] withr_2.4.3 KEGGREST_1.35.0 RCurl_1.98-1.5
## [103] tibble_3.1.6 crayon_1.4.2 utf8_1.2.2
## [106] rmarkdown_2.11 viridis_0.6.2 grid_4.2.0
## [109] data.table_1.14.2 blob_1.2.2 forcats_0.5.1
## [112] digest_0.6.29 xtable_1.8-4 tidyr_1.1.4
## [115] gridGraphics_0.5-1 stats4_4.2.0 munsell_0.5.0
## [118] viridisLite_0.4.0 ggplotify_0.1.0
References
Agarwal, Vikram, George W Bell, Jin-Wu Nam, and David P Bartel. 2015.
“Predicting Effective microRNA Target Sites in Mammalian mRNAs.” *Elife*
4: e05005.
Ally, Adrian, Miruna Balasundaram, Rebecca Carlsen, Eric Chuah, Amanda
Clarke, Noreen Dhalla, Robert A Holt, et al. 2017. “Comprehensive and
Integrative Genomic Characterization of Hepatocellular Carcinoma.”
*Cell* 169 (7): 1327–41.
Altschuler, Gabriel M, Oliver Hofmann, Irina Kalatskaya, Rebecca Payne,
Shannan J Ho Sui, Uma Saxena, Andrei V Krivtsov, et al. 2013.
“Pathprinting: An Integrative Approach to Understand the Functional
Basis of Disease.” *Genome Medicine* 5 (7): 1–13.
Joachim, Rose B, Gabriel M Altschuler, John N Hutchinson, Hector R Wong,
Winston A Hide, and Lester Kobzik. 2018. “The Relative Resistance of
Children to Sepsis Mortality: From Pathways to Drug Candidates.”
*Molecular Systems Biology* 14 (5): e7998.
Liberzon, Arthur, Aravind Subramanian, Reid Pinchback, Helga
Thorvaldsdóttir, Pablo Tamayo, and Jill P Mesirov. 2011. “Molecular
Signatures Database (MSigDB) 3.0.” *Bioinformatics* 27 (12): 1739–40.
Pita-Juárez, Yered, Gabriel Altschuler, Sokratis Kariotis, Wenbin Wei,
Katjuša Koler, Claire Green, Rudolph E Tanzi, and Winston Hide. 2018.
“The Pathway Coexpression Network: Revealing Pathway Relationships.”
*PLoS Computational Biology* 14 (3): e1006042.
Subramanian, Aravind, Pablo Tamayo, Vamsi K Mootha, Sayan Mukherjee,
Benjamin L Ebert, Michael A Gillette, Amanda Paulovich, et al. 2005.
“Gene Set Enrichment Analysis: A Knowledge-Based Approach for
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