typo fixes

README.md

@@ -64,5 +64,5 @@ If you are new to DNA methylation analysis, we recommend reading through this [i
 - [load processed methylation data](docs/loading-data.ipynb)
 - [filter unreliable probes from your data](docs/filtering-probes.ipynb)
 - [run array-level quality control reports](docs/quality-control-example.ipynb)
-- [detect outlier samples]() (docs/mds-example.ipynb)
+- [detect outlier samples](docs/mds-example.ipynb)
 - [predict the sex of human samples](docs/quality-control-example.ipynb#predicting-sex)

docs/mds-example.ipynb

@@ -16,7 +16,15 @@
     "\n",
     "```methylcheck``` allows users the option of filtering their data based on the results of MDS. The standard cut off is 1.5 standard deviations from the mean of the data, however users have the ability to adjust the cut off value if they are not satisfied by the filtering. After examining the MDS plot, press 'enter' to accept the cut-off as it is, or enter a new value and rerun the plot. \n",
     "\n",
-    "We will walk through an example of how to use MDS with this dataset from GEO: [GSE111629](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111629). This is a large dataset (n=571) of patients with Parkinson's Disease (n=335) and a group of controls (n=237). These blood samples were run on Illumina's 450k arrays. "
+    "We will walk through an example of how to use MDS with this dataset from GEO: [GSE111629](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111629). This is a large dataset (n=571) of patients with Parkinson's Disease (n=335) and a group of controls (n=237). These blood samples were run on Illumina's 450k arrays. \n",
+    "\n",
+    "We downloaded the data from GEO and processed it with `methylprep` using the following command:\n",
+    "\n",
+    "```shell\n",
+    ">>> python -m methylprep process -d <filepath> --all\n",
+    "```\n",
+    "\n",
+    "**WARNING:** This is a huge dataset and `methylprep` will take +8 hours to process it. It will also eat up a lot of storage on your machine. We chose this data because it demonstrates the utility of the MDS function. We recommend users pick a different dataset to follow along this example!"
    ],
    "metadata": {}
   },