Merge pull request #88 from blab/cut-text

Reduce verbosity of the results

README.md

@@ -17,8 +17,9 @@ In this work, we tested whether dimensionality reduction methods could capture k
 We applied principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to sequences with well-defined phylogenetic clades and either reassortment (H3N2) or recombination (SARS-CoV-2).
 For each low-dimensional embedding of sequences, we calculated the correlation between pairwise genetic and Euclidean distances in the embedding and applied a hierarchical clustering method to identify clusters in the embedding.
 We measured the accuracy of clusters compared to previously defined phylogenetic clades, reassortment clusters, or recombinant lineages.
-We found that MDS maintained the strongest correlation between pairwise genetic and Euclidean distances between sequences, best captured the intermediate placement of recombinant lineages between parental lineages, and most accurately identified reassortment groups.
+We found that MDS maintained the strongest correlation between pairwise genetic and Euclidean distances between sequences and best captured the intermediate placement of recombinant lineages between parental lineages
 Clusters from t-SNE most accurately recapitulated known phylogenetic clades and recombinant lineages.
+Both MDS and t-SNE accurately identified reassortment groups.
 We show that simple statistical methods without a biological model can accurately represent known genetic relationships for relevant human pathogenic viruses.
 Our open source implementation of these methods for analysis of viral genome sequences can be easily applied when phylogenetic methods are either unnecessary or inappropriate.