Difference between revisions of "Journal Club"
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
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[https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells] | [https://doi.org/10.1101/2022.02.05.479217 Biologically informed deep learning to infer gene program activity in single cells] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
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[https://doi.org/10.1101/2021.10.17.464750 SIMBA: SIngle-cell eMBedding Along with features] | [https://doi.org/10.1101/2021.10.17.464750 SIMBA: SIngle-cell eMBedding Along with features] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
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[https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning] | [https://pubmed.ncbi.nlm.nih.gov/34462589/ Mapping single-cell data to reference atlases by transfer learning] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-41 | |style="padding:.4em;"|22-41 | ||
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[https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity] | [https://doi.org/10.1101/2021.10.31.466532 Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-40 | |style="padding:.4em;"|22-40 | ||
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[https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics] | [https://pubmed.ncbi.nlm.nih.gov/34845454/ Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-39 | |style="padding:.4em;"|22-39 | ||
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[https://doi.org/10.1016/j.patter.2022.100443 EMBEDR: Distinguishing signal from noise in single-cell omics data] | [https://doi.org/10.1016/j.patter.2022.100443 EMBEDR: Distinguishing signal from noise in single-cell omics data] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-38 | |style="padding:.4em;"|22-38 | ||
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[https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics] | [https://pubmed.ncbi.nlm.nih.gov/34675423/ Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-37 | |style="padding:.4em;"|22-37 | ||
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[https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)] | [https://pubmed.ncbi.nlm.nih.gov/34426704/ Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA)] | ||
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|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-36 | |style="padding:.4em;"|22-36 | ||
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[https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial] | [https://pubmed.ncbi.nlm.nih.gov/34986867/ Hepatocellular carcinoma patients with high circulating cytotoxic T cells and intra-tumoral immune signature benefit from pembrolizumab: results from a single-arm phase 2 trial] | ||
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− | |style="padding:.4em;" rowspan=1|2022/ | + | |style="padding:.4em;" rowspan=1|2022/05/19 |
|style="padding:.4em;" rowspan=1|Single-cell | |style="padding:.4em;" rowspan=1|Single-cell | ||
|style="padding:.4em;"|22-35 | |style="padding:.4em;"|22-35 | ||
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[https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data] | [https://pubmed.ncbi.nlm.nih.gov/35199064/ Effect of imputation on gene network reconstruction from single-cell RNA-seq data] | ||
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!scope="col" style="padding:.4em" | Paper title | !scope="col" style="padding:.4em" | Paper title | ||
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− | |style="padding:.4em;" rowspan=1|2022/ | + | |style="padding:.4em;" rowspan=1|2022/07/01 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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[https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data] | [https://www.biorxiv.org/content/10.1101/2021.09.13.460160v3 Inference of disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data] | ||
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− | |style="padding:.4em;" rowspan=1|2022/ | + | |style="padding:.4em;" rowspan=1|2022/06/24 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-28 | |style="padding:.4em;"|22-28 | ||
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disease] | disease] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-27 | |style="padding:.4em;"|22-27 | ||
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[https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia] | [https://www.sciencedirect.com/science/article/pii/S2666379121002561 Identification of Faecalibacterium prausnitzii strains for gut microbiome-based intervention in Alzheimer’s-type dementia] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-26 | |style="padding:.4em;"|22-26 | ||
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[https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum] | [https://www.nature.com/articles/s41591-022-01688-4 Microbiome and metabolome features of the cardiometabolic disease spectrum] | ||
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− | |style="padding:.4em;" rowspan=1|2022 | + | |style="padding:.4em;" rowspan=1|2022/06/03 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-25 | |style="padding:.4em;"|22-25 | ||
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome] | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02195-w Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome] | ||
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− | |style="padding:.4em;" rowspan=1|2022/ | + | |style="padding:.4em;" rowspan=1|2022/05/27 |
|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-24 | |style="padding:.4em;"|22-24 | ||
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[https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies] | [https://www.biorxiv.org/content/10.1101/2022.02.21.480893v1 Integrating phylogenetic and functional data in microbiome studies] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
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[https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs] | [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02473-1 Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs] | ||
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|style="padding:.4em;" rowspan=1|Microbiome | |style="padding:.4em;" rowspan=1|Microbiome | ||
|style="padding:.4em;"|22-22 | |style="padding:.4em;"|22-22 |
Revision as of 08:06, 13 April 2022
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2022/07/21 | Single-cell | 22-44 | IS Choi |
Biologically informed deep learning to infer gene program activity in single cells |
2022/07/14 | Single-cell | 22-43 | SB Baek | |
2022/07/07 | Single-cell | 22-42 | JH Cha |
Mapping single-cell data to reference atlases by transfer learning |
2022/06/30 | Single-cell | 22-41 | JW Yu | |
2022/06/23 | Single-cell | 22-40 | IS Choi | |
2022/06/16 | Single-cell | 22-39 | EJ Sung |
EMBEDR: Distinguishing signal from noise in single-cell omics data |
2022/06/09 | Single-cell | 22-38 | SB Baek | |
2022/06/02 | Single-cell | 22-37 | JH Cha | |
2022/05/26 | Single-cell | 22-36 | JW Yu | |
2022/05/19 | Single-cell | 22-35 | EJ Sung |
Effect of imputation on gene network reconstruction from single-cell RNA-seq data |
2022/05/12 | Single-cell | 22-34 | IS Choi | |
2022/04/07 | Single-cell | 22-33 | SB Baek | |
2022/03/25 | Single-cell | 22-32 | JH Cha | |
2022/03/18 | Single-cell | 22-31 | JW Yu |
Systematic investigation of cytokine signaling activity at the tissue and single-cell levels |
2022/03/04 | Single-cell | 22-30 | EJ Sung |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2022/07/01 | Microbiome | 22-29 | JY Ma | |
2022/06/24 | Microbiome | 22-28 | SH Lee |
[https://www.nature.com/articles/s41591-022-01686-6 Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease] |
2022/06/17 | Microbiome | 22-27 | SH Ann | |
2022/06/10 | Microbiome | 22-26 | HJ Kim |
Microbiome and metabolome features of the cardiometabolic disease spectrum |
2022/06/03 | Microbiome | 22-25 | JH Cha | |
2022/05/27 | Microbiome | 22-24 | NY Kim |
Integrating phylogenetic and functional data in microbiome studies |
2022/05/20 | Microbiome | 22-23 | MY Ma |
Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs |
2022/05/13 | Microbiome | 22-22 | SH Lee |
Multivariable association discovery in population-scale meta-omics studies |
2022/04/08 | Microbiome | 22-21 | SH Ahn | |
2022/04/01 | Microbiome | 22-20 | HJ Kim |
AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite |
2022/03/18 | Microbiome | 22-19 | JH Cha | |
2022/03/04 | Microbiome | 22-18 | JY Ma |
Microbiota of the prostate tumor environment investigated by whole-transcriptome profiling |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2022/02/25 | Single-cell | 22-17 | SB Baek |
MultiMAP: dimensionality reduction and integration of multimodal data |
2022/02/18 | Single-cell | 22-16 | IS Choi | |
2022/02/11 | Single-cell | 22-15 | JH Cha | |
2022/02/04 | Single-cell | 22-14 | IS Choi | |
2022/01/28 | Single-cell | 22-13 | EJ Sung | |
2022/01/28 | Single-cell | 22-12 | JH Cha |
Pan-cancer single-cell landscape of tumor-infiltrating T cells |
2022/01/14 | Single-cell | 22-11 | JW Yu |
Atlas of clinically distinct cell states and ecosystems across human solid tumors |
2022/01/07 | Single-cell | 22-10 | SB Baek |
Date | Team | Paper index |
Presenter | Paper title |
---|---|---|---|---|
2022/02/25 | Microbiome | 22-9 | NY Kim |
Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M |
2022/02/18 | Microbiome | 22-8 | SH Lee | |
2022/02/11 | Microbiome | 22-7 | SH Ahn |
Gut microbial determinants of clinically important improvement in patients with rheumatoid arthritis |
2022/02/04 | Microbiome | 22-6 | JH Cha |
Gut microbiota modulates weight gain in mice after discontinued smoke exposure |
2022/01/28 | Microbiome | 22-5 | JY Ma |
The human microbiome encodes resistance to the antidiabetic drug acarbose |
2022/01/28 | Microbiome | 22-4 | SH Lee |
Commensal bacteria promote endocrine resistance in prostate cancer through androgen biosynthesis |
2022/01/14 | Microbiome | 22-3 | HJ Kim |
The influence of the gut microbiome on BCG-induced trained immunity |
2022/01/07 | Microbiome | 22-2 | JY Ma | |
2022/01/07 | Microbiome | 22-1 | NY Kim |
ReprDB and panDB: minimalist databases with maximal microbial representation |
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 |