Difference between revisions of "Journal Club"
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|24-7 |
|style="padding:.4em;"|SB Baek | |style="padding:.4em;"|SB Baek | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1038/s41587-023-01728-5 A relay velocity model infers cell-dependent RNA velocity] |
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/ | + | |style="padding:.4em;" rowspan=1|2024/05/03 |
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-6 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41587-023-01734-7 Dandelion uses the single-cell adaptive immune receptor repertoire to explore lymphocyte developmental origins] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/26 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-5 | ||
+ | |style="padding:.4em;"|EJ Sung | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-44206-x Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/05 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-4 | ||
+ | |style="padding:.4em;"|IS Choi | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1016/j.cell.2023.11.026 Automatic cell-type harmonization and integration across Human Cell Atlas datasets] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/22 | ||
+ | |style="padding:.4em;" rowspan=1|Single-cell | ||
+ | |style="padding:.4em;"|24-3 | ||
+ | |style="padding:.4em;"|SB Baek | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41592-023-01994-w Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/15 | ||
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|24-2 |
|style="padding:.4em;"|JH Cha | |style="padding:.4em;"|JH Cha | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID] | [https://doi.org/10.1038/s41587-021-00896-6 Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/ | + | |style="padding:.4em;" rowspan=1|2024/03/08 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|24-1 |
|style="padding:.4em;"|EJ Sung | |style="padding:.4em;"|EJ Sung | ||
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
[https://doi.org/10.1016/j.xgen.2023.100383 Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data] | [https://doi.org/10.1016/j.xgen.2023.100383 Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data] | ||
+ | |} | ||
+ | |||
+ | |||
+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2024-1 Microbiome | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/02/06 | + | !scope="col" style="padding:.4em" | Date |
+ | !scope="col" style="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/22 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-18 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.02.02.578701 Metagenomic estimation of dietary intake from human stool] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/22 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-17 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41591-023-02685-x The infant gut virome is associated with preschool asthma risk independently of bacteria] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/08 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-16 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1101/2024.01.08.574624 Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/08 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-15 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-024-44720-6 Defining the biogeographical map and potential bacterial translocation of microbiome in human ‘surface organs’] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/01 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-14 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-42997-7 Gut microbial structural variation associates with immune checkpoint inhibitor response] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/05/01 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-13 | ||
+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1080/19490976.2024.2307586 Fungal signature differentiates alcohol-associated liver disease from nonalcoholic fatty liver disease] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/24 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-12 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1080/19490976.2024.2302076 Incorporating metabolic activity, taxonomy and community structure to improve microbiome based predictive models for host phenotype prediction] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/24 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-11 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-42112-w Disease-specific loss of microbial cross feeding interactions in the human gut] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/03 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-7 | ||
+ | |style="padding:.4em;"|JY Ma | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41592-023-02092-7 Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/04/03 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-9 | ||
+ | |style="padding:.4em;"|SH Ahn | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-40719-7 Microdiversity of the vaginal microbiome is associated with preterm birth] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-8 | ||
+ | |style="padding:.4em;"|HJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41564-023-01584-8 Large language models improve annotation of prokaryotic viral proteins] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/27 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-10 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-42998-6 Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/20 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-6-2 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://academic.oup.com/nargab/article/2/2/lqaa023/5826153 Visualizing ’omic feature rankings and log-ratios using Qurro] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/20 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-6-1 | ||
+ | |style="padding:.4em;"|JH Cha | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-019-10656-5 Establishing microbial composition measurement standards with reference frames] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/20 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-5 | ||
+ | |style="padding:.4em;"|NY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1186/s13059-024-03166-1 AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-4 | ||
+ | |style="padding:.4em;"|YR Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-44289-6 Differential responses of the gut microbiome and resistome to antibiotic exposures in infants and adults] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/13 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-3 | ||
+ | |style="padding:.4em;"|JY Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41467-023-44290-z Effective binning of metagenomic contigs using contrastive multi-view representation learning] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/06 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-2 | ||
+ | |style="padding:.4em;"|WJ Kim | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1038/s41559-020-01353-4 Polarization of microbial communities between competitive and cooperative metabolism] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/06 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-1-2 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202303925 Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome] | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/03/06 | ||
+ | |style="padding:.4em;" rowspan=1|Microbiome | ||
+ | |style="padding:.4em;"|24-1-1 | ||
+ | |style="padding:.4em;"|G Koh | ||
+ | |style="padding:.4em;text-align:left"| | ||
+ | [https://doi.org/10.1073/pnas.2008731118 Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes] | ||
+ | |} | ||
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+ | {|class=wikitable style="text-align:center;" | ||
+ | |+style="text-align:left;font-size:12pt" | 2023-2 scOmics | ||
+ | |- | ||
+ | !scope="col" style="padding:.4em" | Date | ||
+ | !scope="col" style="padding:.4em" | Team | ||
+ | !scope="col" style="padding:.4em" | Paper<br/>index | ||
+ | !scope="col" style="padding:.4em" | Presenter | ||
+ | !scope="col" style="padding:.4em" | Paper title | ||
+ | |- | ||
+ | |style="padding:.4em;" rowspan=1|2024/02/20 | ||
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|23-40 | |style="padding:.4em;"|23-40 | ||
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[https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples] | [https://doi.org/10.1038/s41592-023-02035-2 Population-level integration of single-cell datasets enables multi-scale analysis across samples] | ||
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− | |style="padding:.4em;" rowspan=1|2024/ | + | |style="padding:.4em;" rowspan=1|2024/02/06 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
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[https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication] | [https://doi.org/10.1038/s43587-023-00514-x scDiffCom: a tool for differential analysis of cell–cell interactions provides a mouse atlas of aging changes in intercellular communication] | ||
|- | |- | ||
− | |style="padding:.4em;" rowspan=1|2024/01/ | + | |style="padding:.4em;" rowspan=1|2024/01/30 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
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[https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells] | [https://doi.org/10.1038/s41587-022-01467-z Modeling intercellular communication in tissues using spatial graphs of cells] | ||
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− | |style="padding:.4em;" rowspan=1|2024/01/ | + | |style="padding:.4em;" rowspan=1|2024/01/16 |
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[https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references] | [https://doi.org/10.1038/s41588-023-01523-7 Precise identification of cell states altered in disease using healthy single-cell references] | ||
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− | |style="padding:.4em;" rowspan=1| | + | |style="padding:.4em;" rowspan=1|2024/01/09 |
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[https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data] | [https://aacrjournals.org/clincancerres/article/29/19/3924/729105/Learning-Individual-Survival-Models-from-PanCancer Learning Individual Survival Models from PanCancer Whole Transcriptome Data] | ||
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!scope="col" style="padding:.4em" | Presenter | !scope="col" style="padding:.4em" | Presenter | ||
!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
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|style="padding:.4em;" rowspan=1|2024/02/21 | |style="padding:.4em;" rowspan=1|2024/02/21 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-66 | |style="padding:.4em;"|23-66 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|HJ Kim |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1186/s40168-023-01607-w Phages are unrecognized players in the ecology of the oral pathogen Porphyromonas gingivalis] |
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|style="padding:.4em;" rowspan=1|2024/02/21 | |style="padding:.4em;" rowspan=1|2024/02/21 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-65 | |style="padding:.4em;"|23-65 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JH Cha |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10. | + | [https://doi.org/10.1038/s41564-023-01439-2 A predicted CRISPR-mediated symbiosis between uncultivated archaea] |
|- | |- | ||
|style="padding:.4em;" rowspan=1|2024/02/14 | |style="padding:.4em;" rowspan=1|2024/02/14 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-64 | |style="padding:.4em;"|23-64 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|SH Ahn |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https:// | + | [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01692-x Integrating compositional and functional content to describe vaginal microbiomes in health and disease] |
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|style="padding:.4em;" rowspan=1|2024/02/14 | |style="padding:.4em;" rowspan=1|2024/02/14 | ||
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|23-63 | |style="padding:.4em;"|23-63 | ||
− | |style="padding:.4em;"| | + | |style="padding:.4em;"|JY Ma |
|style="padding:.4em;text-align:left"| | |style="padding:.4em;text-align:left"| | ||
− | [https://doi.org/10.1038/ | + | [https://doi.org/10.1038/s41587-023-01696-w Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data] |
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Revision as of 09:16, 27 March 2024
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2024/05/10 | Single-cell | 24-7 | SB Baek | |
2024/05/03 | Single-cell | 24-6 | JH Cha | |
2024/04/26 | Single-cell | 24-5 | EJ Sung |
Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity |
2024/04/05 | Single-cell | 24-4 | IS Choi |
Automatic cell-type harmonization and integration across Human Cell Atlas datasets |
2024/03/22 | Single-cell | 24-3 | SB Baek |
Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells |
2024/03/15 | Single-cell | 24-2 | JH Cha |
Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID |
2024/03/08 | Single-cell | 24-1 | EJ Sung |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2023/08/30 | Single-cell | 23-24 | JW Yu | |
2023/08/09 | Single-cell | 23-23 | IS Choi |
Major data analysis errors invalidate cancer microbiome findings |
2023/08/02 | Single-cell | 23-22 | EJ Sung | |
2023/07/26 | Single-cell | 23-21 | G Koh | |
2023/07/19 | Single-cell | 23-20 | JW Yu |
Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression |
2023/07/12 | Single-cell | 23-19 | JH Cha |
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data |
2023/07/05 | Single-cell | 23-18 | SB Baek |
Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance |
2023/06/28 | Single-cell | 23-17 | EJ Sung | |
2023/06/21 | Single-cell | 23-16 | IS Choi | |
2023/06/14 | Single-cell | 23-15 | G Koh | |
2023/05/31 | Single-cell | 23-14 | JW Yu |
Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma |
2023/05/24 | Single-cell | 23-13 | JH Cha | |
2023/05/17 | Single-cell | 23-12 | SB Baek | |
2023/05/10 | Single-cell | 23-11 | EJ Sung |
Supervised discovery of interpretable gene programs from single-cell data |
2023/05/03 | Single-cell | 23-10 | IS Choi |
Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer |
2023/04/26 | Single-cell | 23-9 | G Koh | |
2023/03/22 | Single-cell | 23-8 | JW Yu |
MetaTiME: Meta-components of the Tumor Immune Microenvironment |
2023/03/08 | Single-cell | 23-7 | JH Cha | |
2023/02/21 | Single-cell | 23-6 | SB Baek | |
2023/02/14 | Single-cell | 23-5 | EJ Sung |
A T cell resilience model associated with response to immunotherapy in multiple tumor types |
2022/01/31 | Single-cell | 23-4 | IS Choi | |
2023/01/25 | Single-cell | 23-3 | G Koh | |
2023/01/17 | Single-cell | 23-2 | JW Yu |
Pan-cancer integrative histology-genomic analysis via multimodal deep learning |
2023/01/11 | Single-cell | 23-1 | JH Cha |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/11/23 | Single-cell | 21-39 | IS Choi | |
2021/11/16 | Single-cell | 21-38 | SB Back | |
2021/11/09 | Single-cell | 21-37 | JH Cha | |
2021/11/02 | Single-cell | 21-36 | SB Baek |
Functional Inference of Gene Regulation using Single-Cell Multi-Omics |
2021/10/26 | Single-cell | 21-35 | IS Choi | |
2021/10/19 | Single-cell | 21-34 | JH Cha | |
2021/10/05 | Single-cell | 21-33 | JH Cha |
Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma |
2021/09/28 | Single-cell | 21-32 | SB Baek | |
2021/09/14 | Single-cell | 21-31 | IS Choi | |
2021/09/07 | Single-cell | 21-30 | JH Cha |
A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer |
2021/08/31 | Single-cell | 21-29 | IS Choi |
Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma |
2021/08/24 | Single-cell | 21-28 | SB Baek |
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2021/02/22 | Single-cell | 21-8 | IS Choi |
Functional CRISPR dissection of gene networks controlling human regulatory T cell identity |
21-7 | JH Cha |
Molecular Pathways of Colon Inflammation Induced by Cancer Immunotherapy | ||
2021/02/15 | Single-cell | 21-6 | SB Baek | |
21-5 | IS Choi |
Trajectory-based differential expression analysis for single-cell sequencing data | ||
2021/02/08 | Single-cell | 21-4 | SB Baek |
Genetic determinants of co-accessible chromatin regions in activated T cells across humans |
21-3 | JH Cha |
Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer | ||
2021/02/01 | Single-cell | 21-2 | JW Cho | |
21-1 | JW Cho |