New Analysis Example: Simpler example of gene clustering -- k-means for microarray

### What are the goals of this new example analysis?

Currently ORA in microarray uses differential expression results and the RNA-seq one (not yet developed #344) will end up using a gene module from WGCNA.

However, we should give users a more basic way to find gene clusters from data. Some users may find WGCNA a bit daunting (it also requires some computing power). And it may be more than what a user needs for their particular question, so an example that shows something like k-means.

### What kind of dataset will this need?

Something with enough samples that a cluster would make some kind of sense.
I think `GSE37382` which is medulloblastoma with subgroups and is used for dimension reduction seems like a reasonable dataset to use for this too.

### What steps should be included in this analysis?

These are the roughest ideas of steps I have right now that will need to be made more specific and further polished when we dig into this example more.

1) Import data and metadata
2) Use [k-means function](https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/kmeans)
3) Do some exploration into how "well" k-means ran -- unclear to me without doing a bit more digging what this looks like. It may be as simple as printing out some kind of summary stats.
4) May want to run more iterations and see if you get the same-ish results?
5) Get some kind of annotation for the genes that you can use as a test for seeing if your gene clustering seems sensible. This could be something like GO terms (But maybe not GO terms since they overlap so much).
6) Probably plot gene-wise PCA and label the k-means clusters as colors and another form of gene annotation as shapes and see if it makes sense.

### What packages/methods do you recommend using or looking into for this analysis?

May not need extra packages besides `magrittr`, and tidyverse ones (which are assumed everywhere). Both `k-means` and `prcomp` are in base R.

**Note** if/when this issue is completed, the ORA example should be updated to use this output (this should be its own issue and PR).

Start by reviewing the existing dimension-reduction work that uses the proposed GSE37382 dataset, then clarify the six suggested analysis steps and how k-means results should be evaluated. The example is done when it imports data and metadata, runs and assesses k-means, explores repeatability, adds gene annotation, and plots PCA with cluster and annotation labels.

Written by the indexing model from the issue text.