working on testthat

README.md

@@ -7,9 +7,10 @@ Using TCR sequences for graph embedding
 [![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.borch.dev/uploads/screpertoire/articles/trex)
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+## Introduction
+
 <img align="right" src="https://github.com/ncborcherding/Trex/blob/main/www/trex_hex.png" width="305" height="352">
 
-## Introduction
 Single-cell sequencing is now a integral tool in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, 
 like immune cell receptor profiling at the level of an individual cell. Towards this end, we developed the [scRepertoire](https://github.com/ncborcherding/scRepertoire) 
 R package to assist in the interaction of immune receptor and gene expression sequencing. However, utilization of clonal indices for more complex analyses are still lacking, specifically in using clonality in embedding of single-cells. To this end, we developed an R package that uses deep learning to vectorize TCR sequences using order or translating the sequence into amino acid properties.