Gene Set Analysis (GSA) via self-contained test for group
effect on signaling (directed) pathways based on SEM. The core of the
methodology is implemented in the RICF algorithm of SEMrun(),
recovering from RICF output node-specific group effect p-values, and
Brown’s combined permutation p-values of node activation and inhibition.
Usage
SEMgsa(g = list(), data, group, method = "BH", alpha = 0.05, n_rep = 1000, ...)Arguments
- g
A list of pathways to be tested.
- data
A matrix or data.frame. Rows correspond to subjects, and columns to graph nodes (variables).
- group
A binary vector. This vector must be as long as the number of subjects. Each vector element must be 1 for cases and 0 for control subjects.
- method
Multiple testing correction method. One of the values available in
p.adjust. By default, method is set to "BH" (i.e., Benjamini-Hochberg correction).- alpha
Gene set test significance level (default = 0.05).
- n_rep
Number of randomization replicates (default = 1000).
- ...
Currently ignored.
Value
A list of 2 objects:
"gsa", A data.frame reporting the following information for each pathway in the input list:
"No.nodes", pathway size (number of nodes);
"No.DEGs", number of differential espression genes (DEGs) within the pathway, after multiple test correction with one of the methods available in
p.adjust;"pert", pathway perturbation status (see details);
"pNA", Brown's combined P-value of pathway node activation;
"pNI", Brown's combined P-value of pathway node inhibition;
"PVAL", Bonferroni combined P-value of pNA, and pNI; i.e., 2* min(pNA, PNI);
"ADJP", Adjusted Bonferroni P-value of pathway perturbation; i.e., min(No.pathways * PVAL; 1).
"DEG", a list with DEGs names per pathways.
Details
For gaining more biological insights into the functional roles of pre-defined subsets of genes, node perturbation obtained from RICF fitting has been combined with up- or down-regulation of genes from a reference interactome to obtain overall pathway perturbation as follows:
The node perturbation is defined as activated when the minimum among the p-values is positive; if negative, the status is inhibited.
Up- or down- regulation of genes is computed from the weighted adjacency matrix of each pathway as column sum of weights(-1,0,1) over each source node. If the overall sum of node weights is below 1, the pathway is flagged as down-regulated, otherwise as up-regulated.
The combination between these two quantities allows to define the direction (up or down) of gene perturbation. Up- or down regulated gene status, associated with node inhibition, indicates a decrease in activation (or increase in inhibition) in cases with respect to control group. Conversely, up- or down regulated gene status, associated with node activation, indicates an increase in activation (or decrease in inhibition) in cases with respect to control group.
References
Grassi, M., Tarantino, B. (2022). SEMgsa: topology-based pathway enrichment analysis with structural equation models. BMC Bioinformatics, 17 Aug, 23, 344. <https://doi.org/10.1186/s12859-022-04884-8>
Author
Mario Grassi mario.grassi@unipv.it
Examples
# \dontrun{
# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.
# Selection of FTD-ALS pathways from KEGG pathways
paths.name <- c("MAPK signaling pathway",
"Protein processing in endoplasmic reticulum",
"Endocytosis",
"Wnt signaling pathway",
"Neurotrophin signaling pathway",
"Amyotrophic lateral sclerosis")
j <- which(names(kegg.pathways) %in% paths.name)
GSA <- SEMgsa(kegg.pathways[j], als.npn, alsData$group,
method = "bonferroni", alpha = 0.05,
n_rep = 1000)
#> k = 1 MAPK signaling pathway
#> k = 2 Protein processing in endoplasmic reticulum
#> k = 3 Endocytosis
#> k = 4 Wnt signaling pathway
#> k = 5 Neurotrophin signaling pathway
#> k = 6 Amyotrophic lateral sclerosis
GSA$gsa
#> No.nodes No.DEGs pert
#> Neurotrophin signaling pathway 119 33 down act
#> Amyotrophic lateral sclerosis 364 66 up inh
#> Endocytosis 252 59 <NA>
#> MAPK signaling pathway 294 47 up act
#> Protein processing in endoplasmic reticulum 171 51 up act
#> Wnt signaling pathway 166 34 <NA>
#> pNa pNi
#> Neurotrophin signaling pathway 4.840572e-14 2.720046e-14
#> Amyotrophic lateral sclerosis 2.647549e-12 1.273426e-13
#> Endocytosis 3.013589e-11 2.664535e-12
#> MAPK signaling pathway 6.145084e-12 4.988220e-09
#> Protein processing in endoplasmic reticulum 7.039591e-12 6.101954e-10
#> Wnt signaling pathway 1.427281e-08 2.227794e-07
#> PVAL ADJP
#> Neurotrophin signaling pathway 5.440093e-14 3.264056e-13
#> Amyotrophic lateral sclerosis 2.546852e-13 1.528111e-12
#> Endocytosis 5.329071e-12 3.197442e-11
#> MAPK signaling pathway 1.229017e-11 7.374101e-11
#> Protein processing in endoplasmic reticulum 1.407918e-11 8.447509e-11
#> Wnt signaling pathway 2.854561e-08 1.712737e-07
GSA$DEG
#> $`Neurotrophin signaling pathway`
#> [1] "207" "10000" "91860" "814" "1399" "2309" "10818" "11213" "3667"
#> [10] "3845" "5604" "4215" "4217" "5602" "5600" "5599" "5601" "4793"
#> [19] "4893" "4916" "5296" "5335" "5336" "5664" "5781" "5879" "5906"
#> [28] "387" "8767" "9252" "6272" "6654" "7531"
#>
#> $`Amyotrophic lateral sclerosis`
#> [1] "55860" "10189" "55626" "9973" "25978" "1329" "1337" "1340"
#> [9] "1346" "10540" "51164" "25981" "146754" "27019" "83544" "10126"
#> [17] "2081" "2733" "2882" "10013" "3178" "3181" "220988" "3309"
#> [25] "3710" "3799" "3800" "64837" "89953" "84557" "81631" "5608"
#> [33] "5600" "2475" "55706" "56901" "4747" "4741" "9542" "23511"
#> [41] "23225" "79902" "4928" "5532" "5534" "5688" "5689" "5718"
#> [49] "5879" "5903" "6390" "6391" "6392" "6396" "81929" "23064"
#> [57] "10280" "140775" "23435" "84790" "10382" "84617" "29979" "29978"
#> [65] "29796" "55255"
#>
#> $Endocytosis
#> [1] "116983" "10097" "10096" "57180" "163" "64411" "55738" "84364"
#> [9] "10565" "10092" "829" "867" "25978" "128866" "51510" "1212"
#> [17] "27128" "26052" "2060" "8729" "9815" "9146" "3133" "3134"
#> [25] "3561" "83737" "3799" "10015" "8394" "5338" "23550" "10890"
#> [33] "9230" "22841" "57403" "5867" "5869" "7879" "9135" "387"
#> [41] "56904" "4087" "6643" "58533" "23111" "51324" "10254" "7251"
#> [49] "9559" "51160" "55737" "51534" "23325" "9897" "8976" "147179"
#> [57] "644150" "11059" "118813"
#>
#> $`MAPK signaling pathway`
#> [1] "207" "208" "774" "775" "776" "9254"
#> [7] "836" "1398" "1436" "1946" "8822" "2885"
#> [13] "3480" "8517" "8681" "4254" "4296" "7786"
#> [19] "4215" "9064" "8491" "1432" "5599" "23162"
#> [25] "5601" "2122" "8569" "4763" "4772" "4775"
#> [31] "56034" "5228" "100137049" "5494" "5532" "5534"
#> [37] "5567" "84867" "5879" "5881" "5906" "5921"
#> [43] "5922" "9252" "6654" "23118" "57551"
#>
#> $`Protein processing in endoplasmic reticulum`
#> [1] "4287" "9532" "578" "1410" "8454" "51009" "3301" "54788"
#> [9] "5611" "285126" "55741" "80267" "2081" "30001" "26270" "3320"
#> [17] "3326" "7184" "3309" "10960" "11253" "5602" "5599" "5601"
#> [25] "8720" "51360" "7841" "4780" "23645" "9978" "6185" "51128"
#> [33] "6396" "25956" "29927" "7095" "11231" "6400" "64374" "6500"
#> [41] "6745" "6747" "7321" "7322" "7326" "29979" "29978" "165324"
#> [49] "23190" "7466" "55432"
#>
#> $`Wnt signaling pathway`
#> [1] "10297" "816" "894" "896" "57680" "1452" "80319" "23002" "1856"
#> [10] "23291" "7976" "8549" "6885" "4609" "4772" "4776" "64840" "5532"
#> [19] "5534" "5567" "5879" "5881" "9978" "387" "9475" "84870" "8607"
#> [28] "59343" "6477" "6907" "7089" "7091" "57216" "80326"
#>
# }