I'm assuming that you're working in R here, but the statistical principles are the same. To look for enrichment of genes, you should apply a hypergeometric test, which is implemented in the phyper function. Additionally, you'll need your universe set, or all the genes that you analysed.

If you want to apply your results to a pathway for visualisation, then there's the gage package in bioconductor which will also provide functions for enrichment tests. The specific package for mapping to pathways and doing visualisations is pathview.

If this is a bit too much code, then there are web implementations for this task which you might consider, namely Reactome, or a more recent way is OpenTarget's batch search (note that the batch search is limited to 200 genes per search at the moment, but I believe they're working on expanding that limit!)

Hi,

If you are working on LINUX or Windows 10, try GeneSCF command line tool. To know what is pathway analysis and what for it is used look into GeneSCF article.

1. Pathway enrichment can be done with many tools. Such as KEGG or Reactome pathways in R goseq (see manual), or with R clusterProfiler (see manual).

2. Visualization of your expression data in pathways can be done with pathview, a package in R.

As Andrew said, Pathway analysis + data visualization can be done in R/Bioconductor using gage and pathview package. The full workflow is described in their package vignettes:
for RNA-Seq data
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf
for microarray data and generic omics analysis
http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/gage.pdf

For people not familiar with R, Pathview Web server provides a user friendly access to Pathview and the full GAGE/Pathview pathway analysis workflow. It can be used for multiple types of omics data and their integrated analysis too.
Pathview Web server:
https://pathview.uncc.edu/
The paper:
https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx372

Pathway Enrichment Analysis is similar to KEGG Orthology or KO Pathway Analysis. The pathways are constructed using many genomes, as such, the orthology part of KO. There exists organism specific pathways such as those only observed in plants. Also, even if your organism of study is not human you could still end up with human pathways like disease related pathways. You can do KO Pathway using Blast2GO.