Update README.md

README.md

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 # CF/MS Elution Profile PPI Dataset
 
-Includes processed data from several *Homo sapiens* protein co-fractionation mass spectrometry (CF/MS) experiments, as well as positive/negative protein-protein interaction (PPI) labels for each pair.
+Proteins are the functional basis of life, but it is often their interactions with other proteins which gives rise to said functions. Therefore, we are often interested in whether two proteins participate in the same *protein complex*, or if they **'co-complex'**. If they do, both proteins will typically separate out into the same fractions, or **'co-elute'**, during fractionation. As a result, their counts will be *highly correlated* across all the fractions measured. CF/MS leverages this fact to identify new protein complexes by attempting to statistically correlate the elution profiles of groups of proteins. Typically, we use Pearson correlation coefficient to determine correlation between protein pairs. While this often works quite well, Pearson is a linear function. Current research is exploring whether there are non-linear, higher-order signals between these elution profiles that might have better predictive power than Pearson. As deep learning models excel at estimating non-linear relationships in data, the goal of this dataset is to act as training data for such models, especially **Siamese networks**.
+
+Includes processed data from several *Homo sapiens* protein **co-fractionation mass spectrometry (CF/MS)** experiments, as well as positive/negative protein-protein interaction (PPI) labels for each pair.
 
 Collated, maintained by **Drew Lab at University of Illinois at Chicago**
 
 [Drew Lab webpage](https://ksdrew.github.io/)
 
+## File formats
+- **The .elut file**: A **.elut** file is a TSV-like format containing raw count data from a chromatographic fractionation experiment. Each row in a **.elut** file shows the abundance of a single protein across the collected fractions (columns). Generally speaking, these fractions are collected over time. However, different chromatographic columns can separate proteins along different axes. For example, *Size-eclusion chromatography (SEC)* will mostly separate proteins into fractions according to their *size*; *Ion-exchange chromatography (IEX)* will separate them into fractions according to their *charge*. Each file in this dataset comes from one of these two column separation methods and is named accordingly ('...xx_SEC_xx...' / '...xx_IEX_xx...'. We refer to a given protein's (row's) count data across all fractions (columns) as that protein's **elution trace**. To summarize:
+  - A given row contains count data for a specific protein 
+  - A row's *first column* contains its associated **protein ID**
+  - A row's *subsequent columns* contain that protein's count data from the fractionation experiment
+  - **Note: The user may notice that the first row in a .elut file is one column longer than subsequent rows. This is because the first row contains row names (protein IDs), and the first column contains column names (fraction IDs). Therefore, cell 'A0' is empty.**
+
 ## File structure
 - The **.elut** files each contain a collection elution traces for proteins from a given CF/MS experiment. These can be paired to make sample data. A complete list of data sources can be found at the bottom of this README
 - The **.txt** files contain line-wise specification of protein complexes used to generate positive/negative labels. These can be used to direct the pairing of elution traces into data points.