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Statistical and Computational Methods in Single-cell Analysis Zhicheng Ji Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)

  1. Statistical and Computational Methods in Single-cell Analysis Zhicheng Ji Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 1 / 18

  2. Overview Introductions to Single-cell Sequencing 1 Methods for scRNA-seq 2 Methods for scATAC-seq 3 Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 2 / 18

  3. Introductions Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 3 / 18

  4. Introductions Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 4 / 18

  5. Technologies single-cell DNA/CNV sequencing: DNA sequence information in single cells. single-cell RNA-seq (scRNA-seq): Gene expressions in single cells. single-cell ATAC-seq (scATAC-seq): Open chromatin activities in single cells. single-cell Bisulfite sequencing: DNA methylation in single cells. single-cell VDJ-seq: TCR/Ig sequence information in single T/B cells. CITE-seq: cell surface protein expressions in single cells. Others: scDNase-seq, scChIP-seq, scHi-C Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 5 / 18

  6. Sparsity Jason D. Buenrostro, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. (2015) Nature. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 6 / 18

  7. Analysis Tasks scRNA-seq Imputation Cell Clustering Dimension Reduction Differential Gene Sample alignment/batch effect removal Pseudotime Analysis scATAC-seq Signal Aggregation/Enhancing Cell Clustering Inferring Regulatory Programs Prediction/Aligning with scRNA-seq Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 7 / 18

  8. TSCAN: Pseudotime Reconstruction Cole Trapnell, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. (2014) Nat Biotechnol. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 8 / 18

  9. TSCAN: Pseudotime Reconstruction Group ● 1 2 3 4 5 ● ● ● ● ● 1 ● 5.0 ● ● ● ● 5 PC2 2.5 2 4 3 0.0 −2.5 0 5 10 15 PC1 Zhicheng Ji, et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. (2016) Nucleic Acids Res. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 9 / 18

  10. TSCAN: Pseudotime Reconstruction ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● PCA_dimension_3 ● ● ● ● ● ● 4 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● PCA_dimension_2 ● ● 5 ● ● ● ● ● ● ● ● 10 ● ● 3 ● ● ● ● ● ● ● 2 ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 30 ● ● ● ● ● 0 ● ● ● ● 20 ● ● ● ● ● −30 −20 −10 ● 10 ● 0 −10 −20 −30 −20 −10 0 10 20 PCA_dimension_1 Zhicheng Ji, et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. (2016) Nucleic Acids Res. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 10 / 18

  11. TSCAN: Pseudotime Reconstruction Monocle TSCAN ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 9 ● 9 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 6 6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Gene ● ● ● ● ● ● ● ● Expression ● ● ● ● ● ● ● ● MEF2C 0 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 10 20 30 40 50 0 50 100 150 MYH2 nocluTSCAN Marker ● ● ● ● ● ● ● ● ● ● ● ● ENO3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 9 ● 9 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 6 ● ● ● 6 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 50 100 150 200 0 50 100 150 Pseudotime Zhicheng Ji, et al. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. (2016) Nucleic Acids Res. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 11 / 18

  12. STIP: State Transition Inference Prediction Zeyu Chen*, Zhicheng Ji*, et al. A TCF-1-centered transcriptional network drives an early binary effector versus exhausted CD8+ T cell fate decision . In revision Immunity. Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 12 / 18

  13. Raisin: Differential Gene Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 13 / 18

  14. Raisin: Differential Gene 5 cell 50 cell 1.00 0.75 Reported_FDR 0.50 0.25 0.00 0.00 0.25 0.50 0.75 0.00 0.25 0.50 0.75 Real_FDR BPSC_count limma_count MAST_count scDD_saver wilcoxon_count DESeq2_count limma_saver MAST_saver scde_count wilcoxon_saver Method DEsingle_count limmacell_count raisin_saver t_count edgeR_count limmacell_saver scDD_count t_saver Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 14 / 18

  15. SCRAT: scATAC-seq Signal Aggregation Zhicheng Ji*, Weiqiang Zhou*, et al. Single-cell regulome data analysis by SCRAT. (2017) Zhicheng Ji (Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health) Single-cell Analysis 15 / 18 Bioinformatics.

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