Update README.rst

README.rst

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-|PyPI| |PyPIDownloads| |Docs|
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-scHPL: Hierarchical progressive learning of cell identities in single-cell data
+ <img src="https://github.com/lcmmichielsen/scHPL/blob/master/docs/scHPL.png" width="820px" align="center">
 
+|PyPI| |PyPIDownloads| |Docs|
 
-.. raw:: html
 
- <img src="https://github.com/lcmmichielsen/scHPL/blob/master/docs/scHPL.png" width="820px" align="center">
+scHPL: Hierarchical progressive learning of cell identities in single-cell data
 
 We present a hierarchical progressive learning method which automatically finds relationships between cell populations across multiple datasets and uses this to construct a hierarchical classification tree. For each node in the tree either a linear SVM, kNN, or one-class SVM, which enables the detection of unknown populations, is trained. The trained classification tree can be used to predict the labels of a new unlabeled dataset.