small style changes annotation

jupyter-book/cellular_structure/annotation.bib

@@ -1,4 +1,4 @@
-@Article{KadurLakshminarasimhaMurthy2022,
+@Article{anno:KadurLakshminarasimhaMurthy2022,
 author={Kadur Lakshminarasimha Murthy, Preetish
 and Sontake, Vishwaraj
 and Tata, Aleksandra
@@ -29,7 +29,7 @@ @Article{KadurLakshminarasimhaMurthy2022
 url={https://doi.org/10.1038/s41586-022-04541-3}
 }
 
-@Article{Wagner2016,
+@Article{anno:Wagner2016,
 author={Wagner, Allon
 and Regev, Aviv
 and Yosef, Nir},
@@ -47,8 +47,7 @@ @Article{Wagner2016
 url={https://doi.org/10.1038/nbt.3711}
 }
 
-@article{
-doi:10.1126/science.abl5197,
+@article{anno:Conde2022,
 author = {C. Domínguez Conde  and C. Xu  and L. B. Jarvis  and D. B. Rainbow  and S. B. Wells  and T. Gomes  and S. K. Howlett  and O. Suchanek  and K. Polanski  and H. W. King  and L. Mamanova  and N. Huang  and P. A. Szabo  and L. Richardson  and L. Bolt  and E. S. Fasouli  and K. T. Mahbubani  and M. Prete  and L. Tuck  and N. Richoz  and Z. K. Tuong  and L. Campos  and H. S. Mousa  and E. J. Needham  and S. Pritchard  and T. Li  and R. Elmentaite  and J. Park  and E. Rahmani  and D. Chen  and D. K. Menon  and O. A. Bayraktar  and L. K. James  and K. B. Meyer  and N. Yosef  and M. R. Clatworthy  and P. A. Sims  and D. L. Farber  and K. Saeb-Parsy  and J. L. Jones  and S. A. Teichmann },
 title = {Cross-tissue immune cell analysis reveals tissue-specific features in humans},
 journal = {Science},
@@ -74,7 +73,7 @@ @article {Pullin2022.05.09.490241
 	journal = {bioRxiv}
 }
 
-@article{PASQUINI2021961,
+@article{anno:PASQUINI2021961,
 title = {Automated methods for cell type annotation on scRNA-seq data},
 journal = {Computational and Structural Biotechnology Journal},
 volume = {19},
@@ -85,10 +84,9 @@ @article{PASQUINI2021961
 url = {https://www.sciencedirect.com/science/article/pii/S2001037021000192},
 author = {Giovanni Pasquini and Jesus Eduardo {Rojo Arias} and Patrick Schäfer and Volker Busskamp},
 keywords = {scRNA-seq, Cell type, Cell state, Automatic annotation},
-abstract = {The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.}
-d}
+}
 
-@Article{Abdelaal2019,
+@Article{anno:Abdelaal2019,
 author={Abdelaal, Tamim
 and Michielsen, Lieke
 and Cats, Davy
@@ -110,7 +108,7 @@ @Article{Abdelaal2019
 url={https://doi.org/10.1186/s13059-019-1795-z}
 }
 
-@article{Fu2020,
+@article{anno:Fu2020,
   doi = {10.12688/f1000research.22969.2},
   url = {https://doi.org/10.12688/f1000research.22969.2},
   year = {2020},
@@ -123,7 +121,7 @@ @article{Fu2020
   journal = {F1000Research}
 }
 
-@article{Huang2021,
+@article{anno:Huang2021,
   doi = {10.1093/bib/bbab035},
   url = {https://doi.org/10.1093/bib/bbab035},
   year = {2021},
@@ -134,7 +132,7 @@ @article{Huang2021
   journal = {Briefings in Bioinformatics}
 }
 
-@article{ZHANG2019383,
+@article{anno:ZHANG2019383,
 title = {Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq},
 journal = {Cell Systems},
 volume = {9},
@@ -150,7 +148,7 @@ @article{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{Sikkema2023,
+@article{anno:Sikkema2023,
   doi = {10.1038/s41591-023-02327-2},
   url = {https://doi.org/10.1038/s41591-023-02327-2},
   year = {2023},
@@ -161,7 +159,7 @@ @article{Sikkema2023
   journal = {Nature Medicine}
 }
 
-@article{ZENG20222739,
+@article{anno:ZENG20222739,
 title = {What is a cell type and how to define it?},
 journal = {Cell},
 volume = {185},
@@ -176,7 +174,7 @@ @article{ZENG20222739
 Cell types are the basic functional units of an organism. Cell types exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize, and understand. This review provides an overview of the basic principles of cell types rooted in evolution and development and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism’s function, using the mammalian brain as a primary example. I propose a roadmap toward a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.}
 }
 
-@Article{Traag2019,
+@Article{anno:Traag2019,
 author={Traag, V. A.
 and Waltman, L.
 and van Eck, N. J.},
@@ -193,7 +191,7 @@ @Article{Traag2019
 doi={10.1038/s41598-019-41695-z},
 url={https://doi.org/10.1038/s41598-019-41695-z}
 }
-@article{SHI20222234,
+@article{anno:SHI20222234,
 title = {Bone marrow hematopoiesis drives multiple sclerosis progression},
 journal = {Cell},
 volume = {185},
@@ -205,39 +203,14 @@ @article{SHI20222234
 url = {https://www.sciencedirect.com/science/article/pii/S0092867422006511},
 author = {Kaibin Shi and Handong Li and Ting Chang and Wenyan He and Ying Kong and Caiyun Qi and Ran Li and Huachen Huang and Zhibao Zhu and Pei Zheng and Zhe Ruan and Jie Zhou and Fu-Dong Shi and Qiang Liu},
 keywords = {multiple sclerosis, autoreactive T cells, bone marrow, myelopoiesis, neuroinflammation},
-abstract = {Summary
-Multiple sclerosis (MS) is a T cell-mediated autoimmune disease of the central nervous system (CNS). Bone marrow hematopoietic stem and progenitor cells (HSPCs) rapidly sense immune activation, yet their potential interplay with autoreactive T cells in MS is unknown. Here, we report that bone marrow HSPCs are skewed toward myeloid lineage concomitant with the clonal expansion of T cells in MS patients. Lineage tracing in experimental autoimmune encephalomyelitis, a mouse model of MS, reveals remarkable bone marrow myelopoiesis with an augmented output of neutrophils and Ly6Chigh monocytes that invade the CNS. We found that myelin-reactive T cells preferentially migrate into the bone marrow compartment in a CXCR4-dependent manner. This aberrant bone marrow myelopoiesis involves the CCL5-CCR5 axis and augments CNS inflammation and demyelination. Our study suggests that targeting the bone marrow niche presents an avenue to treat MS and other autoimmune disorders.}
 }
+
 @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
                Visvader, J and Kuo, F and Alt, F W and Gilliland, D G and
                Golub, T R and Orkin, S H",
-  abstract  = "The TEL (translocation-Ets-leukemia or ETV6) locus, which
-               encodes an Ets family transcription factor, is frequently
-               rearranged in human leukemias of myeloid or lymphoid origins. By
-               gene targeting in mice, we previously showed that TEL-/- mice
-               are embryonic lethal because of a yolk sac angiogenic defect.
-               TEL also appears essential for the survival of selected neural
-               and mesenchymal populations within the embryo proper. Here, we
-               have generated mouse chimeras with TEL-/- ES cells to examine a
-               possible requirement in adult hematopoiesis. Although not
-               required for the intrinsic proliferation and/or differentiation
-               of adult-type hematopoietic lineages in the yolk sac and fetal
-               liver, TEL function is essential for the establishment of
-               hematopoiesis of all lineages in the bone marrow. This defect is
-               manifest within the first week of postnatal life. Our data
-               pinpoint a critical role for TEL in the normal transition of
-               hematopoietic activity from fetal liver to bone marrow. This
-               might reflect an inability of TEL-/- hematopoietic stem cells or
-               progenitors to migrate or home to the bone marrow or, more
-               likely, the failure of these cells to respond appropriately
-               and/or survive within the bone marrow microenvironment. These
-               data establish TEL as the first transcription factor required
-               specifically for hematopoiesis within the bone marrow, as
-               opposed to other sites of hematopoietic activity during
-               development.",
   journal   = "Genes Dev.",
   publisher = "Cold Spring Harbor Laboratory",
   volume    =  12,
@@ -247,21 +220,8 @@ @ARTICLE{Wang1998-rx
   year      =  1998,
   language  = "en"
 }
-@article{
-doi:10.1126/science.abl5197,
-author = {C. Domínguez Conde  and C. Xu  and L. B. Jarvis  and D. B. Rainbow  and S. B. Wells  and T. Gomes  and S. K. Howlett  and O. Suchanek  and K. Polanski  and H. W. King  and L. Mamanova  and N. Huang  and P. A. Szabo  and L. Richardson  and L. Bolt  and E. S. Fasouli  and K. T. Mahbubani  and M. Prete  and L. Tuck  and N. Richoz  and Z. K. Tuong  and L. Campos  and H. S. Mousa  and E. J. Needham  and S. Pritchard  and T. Li  and R. Elmentaite  and J. Park  and E. Rahmani  and D. Chen  and D. K. Menon  and O. A. Bayraktar  and L. K. James  and K. B. Meyer  and N. Yosef  and M. R. Clatworthy  and P. A. Sims  and D. L. Farber  and K. Saeb-Parsy  and J. L. Jones  and S. A. Teichmann },
-title = {Cross-tissue immune cell analysis reveals tissue-specific features in humans},
-journal = {Science},
-volume = {376},
-number = {6594},
-pages = {eabl5197},
-year = {2022},
-doi = {10.1126/science.abl5197},
-URL = {https://www.science.org/doi/abs/10.1126/science.abl5197},
-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{Lotfollahi2022,
+@Article{anno:Lotfollahi2022,
 author={Lotfollahi, Mohammad
 and Naghipourfar, Mohsen
 and Luecken, Malte D.
@@ -289,7 +249,7 @@ @Article{Lotfollahi2022
 url={https://doi.org/10.1038/s41587-021-01001-7}
 }
 
-@Article{Kang2021,
+@Article{anno:Kang2021,
 author={Kang, Joyce B.
 and Nathan, Aparna
 and Weinand, Kathryn
@@ -313,7 +273,7 @@ @Article{Kang2021
 url={https://doi.org/10.1038/s41467-021-25957-x}
 }
 
-@article{HAO20213573,
+@article{anno:HAO20213573,
 title = {Integrated analysis of multimodal single-cell data},
 journal = {Cell},
 volume = {184},
@@ -329,7 +289,7 @@ @article{HAO20213573
 The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.}
 }
 
-@Article{Pliner2019,
+@Article{anno:Pliner2019,
 author={Pliner, Hannah A.
 and Shendure, Jay
 and Trapnell, Cole},
@@ -347,7 +307,7 @@ @Article{Pliner2019
 url={https://doi.org/10.1038/s41592-019-0535-3}
 }
 
-@Article{Zhang2019,
+@Article{anno:Zhang2019,
 author={Zhang, Allen W.
 and O'Flanagan, Ciara
 and Chavez, Elizabeth A.
@@ -388,21 +348,6 @@ @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",
-  abstract  = "Single-cell transcriptome measurements can reveal unexplored
-               biological diversity, but they suffer from technical noise and
-               bias that must be modeled to account for the resulting
-               uncertainty in downstream analyses. Here we introduce
-               single-cell variational inference (scVI), a ready-to-use
-               scalable framework for the probabilistic representation and
-               analysis of gene expression in single cells (
-               https://github.com/YosefLab/scVI ). scVI uses stochastic
-               optimization and deep neural networks to aggregate information
-               across similar cells and genes and to approximate the
-               distributions that underlie observed expression values, while
-               accounting for batch effects and limited sensitivity. We used
-               scVI for a range of fundamental analysis tasks including batch
-               correction, visualization, clustering, and differential
-               expression, and achieved high accuracy for each task.",
   journal   = "Nat. Methods",
   publisher = "Springer Science and Business Media LLC",
   volume    =  15,
@@ -412,7 +357,7 @@ @ARTICLE{Lopez2018-zc
   year      =  2018,
   language  = "en"
 }
-@misc{https://doi.org/10.48550/arxiv.2211.03793,
+@misc{anno:Engelmann2019,
   doi = {10.48550/ARXIV.2211.03793},
   
   url = {https://arxiv.org/abs/2211.03793},