Merge branch 'master' of https://github.com/theislab/single-cell-best…

…-practices

jupyter-book/cellular_structure/annotation.bib

@@ -60,7 +60,7 @@ @article{anno:Conde2022
 eprint = {https://www.science.org/doi/pdf/10.1126/science.abl5197},
 abstract = {Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference dataset, tissue-integrated expression analysis, and antigen receptor sequencing. The human immune system is composed of many different cell types spread across the entire body, but little is currently known about the fine-grained variations in these cell types across organs. Using single-cell genomics, Domínguez Conde et al. examined the gene expression profile of more than 300,000 individual immune cells extracted from 16 different tissues in 12 deceased adult organ donors (see the Perspective by Liu and Zhang). Cell identity was assigned using CellTypist, an automated cell classification tool designed by the authors. In-depth data analysis revealed insights into how the immune system adapts to function effectively in different organ contexts. —LZ and DJ An immune cell atlas of human innate and adaptive immune cells across lymphoid, mucosal, and exocrine sites reveals tissue-specific compositions and features.}}
 
-@article {anno:Pullin2022.05.09.490241,
+@article {Pullin2022.05.09.490241,
 	author = {Pullin, Jeffrey M. and McCarthy, Davis J.},
 	title = {A comparison of marker gene selection methods for single-cell RNA sequencing data},
 	elocation-id = {2022.05.09.490241},
@@ -148,16 +148,15 @@ @article{anno:ZHANG2019383
 Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering “forces” separation, reusing the same dataset generates artificially low p values and hence false discoveries. We introduce a valid post-clustering differential analysis framework, which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.}
 }
 
-@article {anno:Sikkema2022.03.10.483747,
-	author = {Sikkema, L and Strobl, D and Zappia, L and Madissoon, E and Markov, NS and Zaragosi, L and Ansari, M and Arguel, M and Apperloo, L and B{\'e}cavin, C and Berg, M and Chichelnitskiy, E and Chung, M and Collin, A and Gay, ACA and Hooshiar Kashani, B and Jain, M and Kapellos, T and Kole, TM and Mayr, C and von Papen, M and Peter, L and Ram{\'\i}rez-Su{\'a}stegui, C and Schniering, J and Taylor, C and Walzthoeni, T and Xu, C and Bui, LT and de Donno, C and Dony, L and Guo, M and Gutierrez, AJ and Heumos, L and Huang, N and Ibarra, I and Jackson, N and Kadur Lakshminarasimha Murthy, P and Lotfollahi, M and Tabib, T and Talavera-Lopez, C and Travaglini, K and Wilbrey-Clark, A and Worlock, KB and Yoshida, M and , and Desai, T and Eickelberg, O and Falk, C and Kaminski, N and Krasnow, M and Lafyatis, R and Nikol{\'\i}c, M and Powell, J and Rajagopal, J and Rozenblatt-Rosen, O and Seibold, MA and Sheppard, D and Shepherd, D and Teichmann, SA and Tsankov, A and Whitsett, J and Xu, Y and Banovich, NE and Barbry, P and Duong, TE and Meyer, KB and Kropski, JA and Pe{\textquoteright}er, D and Schiller, HB and Tata, PR and Schultze, JL and Misharin, AV and Nawijn, MC and Luecken, MD and Theis, F},
-	title = {An integrated cell atlas of the human lung in health and disease},
-	elocation-id = {2022.03.10.483747},
-	year = {2022},
-	doi = {10.1101/2022.03.10.483747},
-	publisher = {Cold Spring Harbor Laboratory},
-	URL = {https://www.biorxiv.org/content/early/2022/03/11/2022.03.10.483747},
-	eprint = {https://www.biorxiv.org/content/early/2022/03/11/2022.03.10.483747.full.pdf},
-	journal = {bioRxiv}
+@article{anno:Sikkema2023,
+  doi = {10.1038/s41591-023-02327-2},
+  url = {https://doi.org/10.1038/s41591-023-02327-2},
+  year = {2023},
+  month = jun,
+  publisher = {Springer Science and Business Media {LLC}},
+  author = {Lisa Sikkema and Ciro Ram{\'{\i}}rez-Su{\'{a}}stegui and Daniel C. Strobl and Tessa E. Gillett and Luke Zappia and Elo Madissoon and Nikolay S. Markov and Laure-Emmanuelle Zaragosi and Yuge Ji and Meshal Ansari and Marie-Jeanne Arguel and Leonie Apperloo and Martin Banchero and Christophe B{\'{e}}cavin and Marijn Berg and Evgeny Chichelnitskiy and Mei-i Chung and Antoine Collin and Aurore C. A. Gay and Janine Gote-Schniering and Baharak Hooshiar Kashani and Kemal Inecik and Manu Jain and Theodore S. Kapellos and Tessa M. Kole and Sylvie Leroy and Christoph H. Mayr and Amanda J. Oliver and Michael von Papen and Lance Peter and Chase J. Taylor and Thomas Walzthoeni and Chuan Xu and Linh T. Bui and Carlo De Donno and Leander Dony and Alen Faiz and Minzhe Guo and Austin J. Gutierrez and Lukas Heumos and Ni Huang and Ignacio L. Ibarra and Nathan D. Jackson and Preetish Kadur Lakshminarasimha Murthy and Mohammad Lotfollahi and Tracy Tabib and Carlos Talavera-L{\'{o}}pez and Kyle J. Travaglini and Anna Wilbrey-Clark and Kaylee B. Worlock and Masahiro Yoshida and Yuexin Chen and James S. Hagood and Ahmed Agami and Peter Horvath and Joakim Lundeberg and Charles-Hugo Marquette and Gloria Pryhuber and Chistos Samakovlis and Xin Sun and Lorraine B. Ware and Kun Zhang and Maarten van den Berge and Yohan Boss{\'{e}} and Tushar J. Desai and Oliver Eickelberg and Naftali Kaminski and Mark A. Krasnow and Robert Lafyatis and Marko Z. Nikolic and Joseph E. Powell and Jayaraj Rajagopal and Mauricio Rojas and Orit Rozenblatt-Rosen and Max A. Seibold and Dean Sheppard and Douglas P. Shepherd and Don D. Sin and Wim Timens and Alexander M. Tsankov and Jeffrey Whitsett and Yan Xu and Nicholas E. Banovich and Pascal Barbry and Thu Elizabeth Duong and Christine S. Falk and Kerstin B. Meyer and Jonathan A. Kropski and Dana Pe'er and Herbert B. Schiller and Purushothama Rao Tata and Joachim L. Schultze and Sara A. Teichmann and Alexander V. Misharin and Martijn C. Nawijn and Malte D. Luecken and Fabian J. Theis and},
+  title = {An integrated cell atlas of the lung in health and disease},
+  journal = {Nature Medicine}
 }
 
 @article{anno:ZENG20222739,
@@ -206,7 +205,7 @@ @article{anno:SHI20222234
 keywords = {multiple sclerosis, autoreactive T cells, bone marrow, myelopoiesis, neuroinflammation},
 }
 
-@ARTICLE{anno:Wang1998-rx,
+@ARTICLE{Wang1998-rx,
   title     = "The {TEL/ETV6} gene is required specifically for hematopoiesis
                in the bone marrow",
   author    = "Wang, L C and Swat, W and Fujiwara, Y and Davidson, L and
@@ -345,7 +344,7 @@ @Article{anno:Zhang2019
 url={https://doi.org/10.1038/s41592-019-0529-1}
 }
 
-@ARTICLE{anno:Lopez2018-zc,
+@ARTICLE{Lopez2018-zc,
   title     = "Deep generative modeling for single-cell transcriptomics",
   author    = "Lopez, Romain and Regier, Jeffrey and Cole, Michael B and
                Jordan, Michael I and Yosef, Nir",
@@ -360,12 +359,19 @@ @ARTICLE{anno:Lopez2018-zc
 }
 @misc{anno:Engelmann2019,
   doi = {10.48550/ARXIV.2211.03793},
+  
   url = {https://arxiv.org/abs/2211.03793},
+  
   author = {Engelmann, Jan and Hetzel, Leon and Palla, Giovanni and Sikkema, Lisa and Luecken, Malte and Theis, Fabian},
+  
   keywords = {Genomics (q-bio.GN), Machine Learning (cs.LG), Quantitative Methods (q-bio.QM), Applications (stat.AP), FOS: Biological sciences, FOS: Biological sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
   title = {Uncertainty Quantification for Atlas-Level Cell Type Transfer},
+  
   publisher = {arXiv},
+  
   year = {2022},
+  
   copyright = {arXiv.org perpetual, non-exclusive license}
 }