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Add references, linkcode to docs, remove scanpydoc (#25)
* update references * ref style * no scanpydoc * try return * try return * typehint defaults * add linkcode
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@@ -46,6 +46,8 @@ rst-roles = | |
class, | ||
func, | ||
ref, | ||
cite:p, | ||
cite:t, | ||
rst-directives = | ||
envvar, | ||
exception, | ||
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@article{Wolf2018, | ||
author = {Wolf, F. Alexander | ||
and Angerer, Philipp | ||
and Theis, Fabian J.}, | ||
title = {SCANPY: large-scale single-cell gene expression data analysis}, | ||
journal = {Genome Biology}, | ||
year = {2018}, | ||
month = {Feb}, | ||
day = {06}, | ||
volume = {19}, | ||
number = {1}, | ||
pages = {15}, | ||
abstract = {Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).}, | ||
issn = {1474-760X}, | ||
doi = {10.1186/s13059-017-1382-0}, | ||
url = {https://doi.org/10.1186/s13059-017-1382-0} | ||
@article{luecken2022benchmarking, | ||
title = {Benchmarking atlas-level data integration in single-cell genomics}, | ||
author = {Luecken, Malte D and B{\"u}ttner, Maren and Chaichoompu, Kridsadakorn and Danese, Anna and Interlandi, Marta and M{\"u}ller, Michaela F and Strobl, Daniel C and Zappia, Luke and Dugas, Martin and Colom{\'e}-Tatch{\'e}, Maria and others}, | ||
journal = {Nature methods}, | ||
volume = {19}, | ||
number = {1}, | ||
pages = {41--50}, | ||
year = {2022}, | ||
publisher = {Nature Publishing Group} | ||
} |
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