[AlexsLemonade#229] comparative-rnaseq-analysis: README cleanup

analyses/comparative-RNASeq-analysis/README.md

@@ -7,23 +7,23 @@
 - [Limitations and Requirements](#limitations-and-requirements)
 - [Future Updates](#future-updates)
 
-# Purpose
+## Purpose
 The comparative-RNAseq-analysis module implements the outlier analysis workflow published in [Vaske et al. Jama Open Network. 2019](https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2753519), which highlights genes within each sample whose expression is an outlier compared to the expression distribution of the dataset as a whole. This workflow:
   - Creates correlation matrices for polyA samples and ribodeplete (stranded) samples using gene expression data.
   - Generates gene outlier threshold values for ribodeplete samples.
   - Lists outlier genes for each ribodeplete sample.
 
-# Usage
+## Usage
 The input file must be an RNA-Seq TPM gene expression matrix in the [RDS](https://stat.ethz.ch/R-manual/R-devel/library/base/html/readRDS.html) file format. Currently available matrices that meet this format are `pbta-gene-expression-rsem-tpm.polya.rds` and `pbta-gene-expression-rsem-tpm.stranded.rds`.
 
-Example command lines follow, using the stranded (ribodeplete) dataset.
+Command lines follow, using as example the stranded (ribodeplete) dataset.
 Available flags:
   - `--verbose` toggles verbose output
   - `--output-prefix MyDataset` prepends MyDataset to output filenames to identify runs. Subsequent steps in the same run must use the same prefix.
   - `--scratch ../../scratch` provides path to scratch dir where intermediate files shared between steps can be read and written.
   - `--results ./results` provides path to final results dir.
 
-## 01 - Correlation Matrix
+### 01 - Correlation Matrix
 Generates the correlation matrix and filtered gene list.
 
 ```
@@ -35,15 +35,17 @@ Generates the correlation matrix and filtered gene list.
 ```
 
 Input file:
-`data/pbta-gene-expression-rsem-tpm.stranded.rds`
+```
+data/pbta-gene-expression-rsem-tpm.stranded.rds
+```
 
 Output files:
 ```
 scratch/rsem-tpm-stranded-all_by_all_correlations.rds
 scratch/rsem-tpm-stranded-filtered_genes_to_keep.rds
 ```
 
-## 02 -  Thresholds and Outliers
+### 02 -  Thresholds and Outliers
 Generates outlier thresholds and matrix of outlier genes.
 
 ```
@@ -70,25 +72,24 @@ scratch/rsem-tpm-stranded-log2-normalized.rds
 scratch/rsem-tpm-stranded-threshold-expression-values.rds
 ```
 
-# Limitations and requirements
+## Limitations and requirements
 Because the per-sample results of this analysis are dependent on the entire dataset, all samples in the dataset must meet certain standards for the outliers to be meaningful. *(Currently, these standards are not being enforced.)*
   - All samples must pass a quality control check.
   - Dataset must contain only tumor samples; no normal, cell line, etc data.
   - All samples in the dataset must have the same library preparation for their gene expression to be comparable. (Eg, polyA selection, ribodepletion, or hybrid capture).
 
-## Software dependencies
+### Software dependencies
 The analysis uses python 3 and requires the following libraries. Version numbers
-represent version in use and earlier or later versions may also be acceptable.
+are those currently in use and earlier or later versions may also be acceptable but have not been tested.
 ```
 numpy (1.17.3)
 pandas (0.25.3)
 scipy (1.3.2)
 scikit-learn (0.19.1)
 pyreadr (0.2.1)
-
 ```
 In addition, a `utils` module is included for certain shared functions.
 
-# Future updates
-- The next iteration of this of this module will use the `pbta-histologies.tsv` file to filter the input datasets to only tumor samples. It will also filter samples to those which meet a to-be-described quality control standard.
+## Future updates
+- The next iteration of this module will use the `pbta-histologies.tsv` file to filter the input datasets to only tumor samples. It will also filter samples to those which meet a to-be-described quality control standard.