date generated: 2025-05-21
Mitch performs unidimensional and multidimensional gene set enrichment analysis. The concept behind this dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). This implementation is suited to R based workflows of multi-omics datasets. This software was developed by Antony Kaspi and Mark Ziemann. Learn more about Mitch at the website: https://github.com/markziemann/Mitch
Here is the first few lines of the input profile.
x | |
---|---|
0610007P14Rik | -0.0392645 |
0610009B22Rik | -0.0028689 |
0610009E02Rik | -0.2454686 |
0610009L18Rik | 0.9803795 |
0610009O20Rik | -0.3821166 |
0610010F05Rik | 0.5840653 |
Here are some metrics about the input data profile:
Profile metrics | |
---|---|
num_genesets | 1 |
num_genes_in_profile | 15245 |
duplicated_genes_present | 0 |
num_profile_genes_in_sets | 16 |
num_profile_genes_not_in_sets | 15229 |
Here is a plot of the input profiles. Note the dynamic ranges.
Here is the contour plot of the profile including all detected genes.
Here are some metrics about the gene sets used:
GMT file of genesets:Gene sets metrics | |
---|---|
num_genesets | 1 |
num_genesets_excluded | 0 |
num_genesets_included | 1 |
set | setSize | pANOVA | s.dist | p.adjustANOVA |
---|---|---|---|---|
mtgenes | 16 | 0.458 | 0.107 | 0.458 |
set | setSize | pANOVA | s.dist | p.adjustANOVA |
---|---|---|---|---|
mtgenes | 16 | 0.458 | 0.107 | 0.458 |
set | mtgenes |
setSize | 16 |
pANOVA | 0.458 |
s.dist | 0.107 |
p.adjustANOVA | 0.458 |
GeneID | Gene Rank |
---|---|
mt-Nd4 | 6285.5 |
mt-Rnr2 | 6271.5 |
mt-Rnr1 | 5815.5 |
mt-Nd5 | 4498.5 |
mt-Nd2 | 4362.5 |
mt-Cytb | 4254.5 |
mt-Nd6 | 3913.5 |
mt-Nd1 | 3766.5 |
mt-Co1 | 3401.5 |
mt-Ti | -990.5 |
mt-Tw | -1568.5 |
mt-Tm | -2162.5 |
mt-Tp | -4045.5 |
mt-Tq | -5813.5 |
mt-Tt | -6611.5 |
mt-Te | -7124.5 |
GeneID | Gene Rank |
---|---|
mt-Nd4 | 6285.5 |
mt-Rnr2 | 6271.5 |
mt-Rnr1 | 5815.5 |
mt-Nd5 | 4498.5 |
mt-Nd2 | 4362.5 |
mt-Cytb | 4254.5 |
mt-Nd6 | 3913.5 |
mt-Nd1 | 3766.5 |
mt-Co1 | 3401.5 |
mt-Ti | -990.5 |
mt-Tw | -1568.5 |
mt-Tm | -2162.5 |
mt-Tp | -4045.5 |
mt-Tq | -5813.5 |
mt-Tt | -6611.5 |
mt-Te | -7124.5 |
Only used for one-dimensional analysis.
Here, the network diagram is used to depict the similarity between some of the top ranked gene sets. It makes separate charts for up and downregulated sets. It works best when prioritisation is done by effect size during the mitch_calc()
step. By default, we only show the top 20 genes, but you can use the networkplot()
command yourself with other options. See ?networkplot
for more detail. There is an element of stochasticity with regard to the network projection, so if you see a lot of overlapping labels or labels getting cut off, you could repeat the chart generation until you get a nice layout. See ?networkplot
for more detail.
Below the network diagrams, you will see lists of genes that make up the up and downregulated sets respectively. For upregulated genes the score needs to be >2 and for downregulated genes it needs to be < -2. This is to remove genes that have uninteresting differential expression and do not contribute enrichment.
if (d==1) {
networkplot(eres=res,FDR=0.05,n_sets=20)
network_genes(eres=res,FDR=0.05,n_sets=20)
} else {
message("Network charts only generated in one-dimensional analysis.")
}
## Can't plot upregulated sets. Fewer than 5 found.
## Can't plot downregulated sets. Fewer than 5 found.
## No significant upregulated sets to show.
## No significant upregulated sets to show.
## [[1]]
## NULL
Here is the session info with all the versions of packages used.
sessionInfo()
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gtools_3.9.5 mitch_1.20.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 beeswarm_0.4.0 xfun_0.52
## [4] bslib_0.9.0 ggplot2_3.5.2 htmlwidgets_1.6.4
## [7] caTools_1.18.3 GGally_2.2.1 lattice_0.22-7
## [10] vctrs_0.6.5 tools_4.5.0 bitops_1.0-9
## [13] generics_0.1.3 parallel_4.5.0 tibble_3.2.1
## [16] pkgconfig_2.0.3 KernSmooth_2.23-26 RColorBrewer_1.1-3
## [19] lifecycle_1.0.4 compiler_4.5.0 farver_2.1.2
## [22] stringr_1.5.1 gplots_3.2.0 httpuv_1.6.16
## [25] htmltools_0.5.8.1 sass_0.4.10 yaml_2.3.10
## [28] later_1.4.2 pillar_1.10.2 jquerylib_0.1.4
## [31] tidyr_1.3.1 MASS_7.3-65 cachem_1.1.0
## [34] mime_0.13 ggstats_0.9.0 network_1.19.0
## [37] tidyselect_1.2.1 digest_0.6.37 stringi_1.8.7
## [40] dplyr_1.1.4 reshape2_1.4.4 purrr_1.0.4
## [43] fastmap_1.2.0 grid_4.5.0 cli_3.6.5
## [46] magrittr_2.0.3 dichromat_2.0-0.1 withr_3.0.2
## [49] scales_1.4.0 promises_1.3.2 rmarkdown_2.29
## [52] gridExtra_2.3 kableExtra_1.4.0 coda_0.19-4.1
## [55] shiny_1.10.0 evaluate_1.0.3 knitr_1.50
## [58] viridisLite_0.4.2 rlang_1.1.6 Rcpp_1.0.14
## [61] xtable_1.8-4 glue_1.8.0 echarts4r_0.4.5
## [64] xml2_1.3.8 jsonlite_2.0.0 svglite_2.1.3
## [67] rstudioapi_0.17.1 R6_2.6.1 plyr_1.8.9
## [70] statnet.common_4.11.0 systemfonts_1.2.2
END of report