Paper Presentations

Overview

Each student has been assigned one journal publication to present in class. The expected structure of the paper presentations is outlined in this Slideshow Template. Students can use any slide show presentation software they wish, but should follow the presentation structure of the template. A detailed presentation schedule is available in the internal Course Planning Sheet sheet (see Columns Q-R of Projects Tab). The grading of both the paper and project presentations includes anonymous feedback from all students as well as the instructor, where understanding of the material, clarity of the oral presentations and critical thinking are the main grading criteria. The grading forms will be provided in the Presentation Schedule (internal google sheet) shortly before the presentations start on May 14th and May 19th. The following lists the assigned papers organized by course project topics.

Publications organized by course projects

All references in Paperpile

RNA-Seq Aligners

  1. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12: 357–360. PubMed

  2. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34: 525–527. Pubmed

DEG Analysis Methods

  1. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550. PubMed

  2. Guo Y, Li C-I, Ye F, Shyr Y (2013) Evaluation of read count based RNAseq analysis methods. BMC Genomics 14 Suppl 8: S2. PubMed

  3. Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42: e91. PubMed

Functional Interpretation

  1. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550. PubMed

  2. Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J (2018) Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat Commun 9: 20. PubMed

  3. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14: 7. PubMed

AI/ML Classification

  1. Díaz-Uriarte R, Alvarez de Andrés S (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7: 3. PubMed

  2. Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8: 25. PubMed

  3. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S-I (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2: 56–67. PubMed

  4. Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. arXiv [csLG]. doi: 10.48550/arXiv.1603.0275. arXiv

Single-cell Genomics: Clustering Significance & Dimensionality Reduction

  1. Grabski IN, Street K, Irizarry RA (2023) Significance analysis for clustering with single-cell RNA-sequencing data. Nat Methods 20: 1196–1202. PubMed

  2. Huang H, Wang Y, Rudin C, Browne EP (2022) Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization. Commun Biol 5: 719. PubMed

Single-cell Genomics: Cell-type Annotation & Trajectory Inference

  1. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, Purdom E, Dudoit S (2018) Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19: 477. PubMed

  2. Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, et al (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20: 163–172. PubMed

  3. Crowell HL, Soneson C, Germain P-L, Calini D, Collin L, Raposo C, Malhotra D, Robinson MD (2020) Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat Commun 11: 6077. PubMed

Multi-omics: Integration & Pathways

  1. Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC, Stegle O (2020) MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol 21: 111. PubMed

  2. Odom GJ, Ban Y, Colaprico A, Liu L, Silva TC, Sun X, Pico AR, Zhang B, Wang L, Chen X (2020) PathwayPCA: An R/Bioconductor package for pathway based integrative analysis of multi-omics data. Proteomics 20: e1900409. PubMed

Multi-omics: Classification & Network analysis

  1. Pai S, Hui S, Isserlin R, Shah MA, Kaka H, Bader GD (2019) netDx: interpretable patient classification using integrated patient similarity networks. Mol Syst Biol 15: e8497. PubMed

  2. Singh A, Shannon CP, Gautier B, Rohart F, Vacher M, Tebbutt SJ, Lê Cao K-A (2019) DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35: 3055–3062. PubMed

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