In MoseleyBioinformaticsLab/categoryCompare2: Meta-Analysis of High-Throughput Experiments Using Feature Annotations

knitr::opts_chunk$set(echo = TRUE)
root_loc <- rprojroot::find_root("DESCRIPTION")

tmp_loc <- tempdir()
Sys.setenv(file_loc = root_loc)
Sys.setenv(exec_loc = file.path(root_loc, "inst/executables"))
Sys.setenv(test_loc = file.path(root_loc, "inst/extdata/test_data"))
Sys.setenv(results_loc = tmp_loc)

Sys.chmod(dir(file.path(root_loc, "inst", "executables"), pattern = "*.R", full.names = TRUE), "0750")

Scripts??

This version of categoryCompare2 now includes a set of executable scripts that can be used to perform an analysis from the command line. This document attempts to document what these scripts do. The scripts are found in the folder exec from the package directory.

You can run the install_executables function to move the scripts somewhere more useful, or executable_path to provide the locations of the scripts so that you can make aliases and change their permissions yourself so that they are accessible from the command line directly.

Workflow

Feature Lists

categoryCompare2 assumes you have one or more feature lists (normally genes) that you want to run enrichment on and compare the enrichments from the feature lists. For enrichment, one also needs the full list or universe of features that were measured in the experiment.

The easiest way to generate the input file in the expected format is to provide newline separated feature lists, each one in a separate file, where the file name indicates what condition the features came from.

In this example, we have the gene symbols for the differential genes at two different timepoints, 10 and 48 hours. These were calculated using the estrogen dataset from Bioconductor.

```{sh show_symbols_10} head $test_loc/10_symbol.txt

```{sh show_symbols_48}
head $test_loc/48_symbol.txt

There is also a file with the full set of features measured on the Affymetrix chip for this data set.

To generate the feature file, we need to combine these individual files into a single JSON file. This uses feature_files_2_json.R

```{sh show_help} $exec_loc/feature_files_2_json.R --help

Let's run it!

```{sh run_features}
$exec_loc/feature_files_2_json.R --json="$results_loc/features.json" \
  --file1="$test_loc/10_symbol.txt" \
  --file2="$test_loc/48_symbol.txt" \
  --universe="$test_loc/universe_symbol.txt"

Now we can see that the features.json file has the three sets of gene symbols in it.

```{sh show_features_json} head $results_loc/features.json

### Annotations

```{sh annotation_help}
$exec_loc/create_annotations.R --help

Organism DB

Often times you will want to generate annotations from an included organism database that generated and maintained by Bioconductor. These are normally the org.*.eg.db, also known as organism entrez gene databases. There are other databases, including the various measurement platform databases.

See ?get_db_annotation for all the various feature types and annotation types available.

We will create an annotation object using the cellular component sub-ontology of the Gene Ontology mapped to gene symbols.

```{sh create_annotation} $exec_loc/create_annotations.R --orgdb="org.Hs.eg.db" \ --feature-type="SYMBOL" \ --annotation-type="CC" \ --json="$results_loc/annotations.json"

#### User Provided Annotations

If you have annotations you want to use, they should be in a JSON file where
each JSON object is the feature, with the annotations that it has.

An example would be:

```{sh show_custom_annotation}
head $test_loc/create_annotations_example_input.json

Using this file, you would run:

$exec_loc/create_annotations.R --input="create_annotations_example_input.json" \
  --feature-type="SYMBOL" \
  --annotation-type="CC" \
  --json="$results_loc/annotations.json"

In this case the --feature-type and --annotation-type should reflect what the original sources that were used to generate these annotations.

Comparisons!

Finally, with the features and annotations in hand, we can do the enrichments and comparisons between them using run_enrichments.R.

```{sh run_enrichments_help} $exec_loc/run_enrichments.R --help

So, with our generated files, we would now do:

```{sh run_enrichments}
$exec_loc/categoryCompare2.R --features="$results_loc/features.json" \
  --annotations="$results_loc/annotations.json" \
  --p-cutoff=0.001 \
  --output-directory="$results_loc" 

Which provides the output in r file.path(tmp_loc, "full_table.txt"). Here is a preview of the results:

in_results <- read.table(file.path(tmp_loc, "full_table.txt"), sep = "\t", header = TRUE)
knitr::kable(head(in_results), digits = 2)

MoseleyBioinformaticsLab/categoryCompare2 documentation built on Nov. 3, 2024, 11:06 a.m.