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To-Do List

Task 1: ABcluster

  • [ x ] include utilities/**/* in setup.py
  • Incorporate KR normalization and ICE normalization functions into the single-cell pipeline.
  • Make command-line callable.
  • Create a function to find differentially expressed regions for KR normalized runs.
  • Add the clustering cells pipeline code.
  • Write the SNPS code

Task 2: differentiate_chromosomes

  • Process the interchromosomal contacts separately with a flag, so the pipeline does not have to run on the genome-wide tensor.

Task 3: Noise Reduction

  • Implement noise reduction before loop calling with Hiccups.

Task 4: Finish Bulk Pipeline

  • Complete the bulk pipeline.

Task 5: Finish Synthetic Pipeline and Upload

  • Create a file to test the cumulant pipeline on synthetic data.
  • Create a file to demonstrate how there are, in fact, two compartments, and that the Hi-C data cannot be explained by arbitrary even-numbered compartments.

Task 6: Parallelization

  • Make everything run in parallel where possible.

Task 7: Comparison with scHiCluster

  • Compare Hypermatrix with scHiCluster.

Task 8: Comparison with Higashi

  • Compare Hypermatrix with Higashi.

Task 9: Review Dixon et al.

  • Go through Dixon et al. again and review their omics data.

Task 10: Genomic Factors Analysis

  • Perform analysis on genomic factors.

Guided Factorization

  • Divide the genome into bins. Create the following category vectors based on data:
    • Isochore: Classify bins into categories L1, L2, H1, H2, and H3 based on GC content:
      • L1: <38%
      • L2: 38%-42%
      • H1: 42%-47%
      • H2: 47%-52%
      • H3: >52%
    • Replication Timing: Classify bins into categories E0, E1, L1, L2, and L3 based on replication timing.
  • Create a matrix with entry (i,j) equal to 1 if i and j are in the same category, and 0 if they are in different categories.

Annotated Bibliography

  • Yao, Z., Van Velthoven, C. T., Nguyen, T. N., et al. (2021).
    A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell, 184(12), 3222-3241.

  • Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., et al. (2015).
    Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347(6226), 1138-1142.

  • Luo, C., Keown, C. L., Kurihara, L., et al. (2017).
    Single-cell methylomes identify neuronal subtypes and regulatory elements in the mammalian cortex. Science, 357(6351), 600-604.

  • Luo, C., Liu, H., Xie, F., et al. (2022).
    Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genomics, 2(3).

  • Luo, C., Rivkin, A., Zhou, J., et al. (2018).
    Robust single-cell DNA methylome profiling with snmC-seq2. Nature Communications, 9(1), 3824.

  • Liu, H., Zeng, Q., Zhou, J., et al. (2023).
    Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain. Nature, 624(7991), 366-377.

  • Lee, D. S., Luo, C., Zhou, J., et al. (2019).
    Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nature Methods, 16(10), 999-1006.

  • Zhang, R., Zhou, T., & Ma, J. (2022).
    Ultrafast and interpretable single-cell 3D genome analysis with Fast-Higashi. Cell Systems, 13(10), 798-807.

  • Tan, L., Xing, D., Chang, C. H., et al. (2018).
    Three-dimensional genome structures of single diploid human cells. Science, 361(6405), 924-928.

    • Method for calling single-cell compartments and separating homologous chromosomes.
    • Single-cell compartment value of bin i is the weighted average CpG density in the reference genome of all the bins that bin i contacts in the single cell contact map.

  • Nagano, T., Lubling, Y., Várnai, C., et al. (2017).
    Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature, 547(7661), 61-67.

    • Single-cell compartment value of bin i is the weighted average A/B compartment value of bulk Hi-C of all the bins that bin i contacts in the single cell contact map.

  • Zhou, J., Ma, J., Chen, Y., et al. (2019).
    Robust single-cell Hi-C clustering by convolution-and random-walk–based imputation. Proceedings of the National Academy of Sciences, 116(28), 14011-14018.

    • Single-cell compartment value of bin i is the dot product of imputed Hi-C contact maps with CpG density vector.

  • Xie, W. J., Meng, L., Liu, S., et al. (2017).
    Structural modeling of chromatin integrates genome features and reveals chromosome folding principle. Scientific Reports, 7(1), 2818.

    • The authors show that average CpG frequency from the reference genome can serve as a good proxy for A/B compartments in bulk Hi-C data. The large majority of partially methylated domains as called by scanning the reference genome are in the B compartment as called by principal component analysis.

  • Benjamini, Y., & Speed, T. P. (2012).
    Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Research, 40(10), e72-e72.

  • Welch, J. D., Kozareva, V., Ferreira, A., et al. (2019).
    Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell, 177(7), 1873-1887.

    • 3-fold tensor method.

  • Stevens, T. J., Lando, D., Basu, S., et al. (2017).
    3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature, 544(7648), 59-64.

  • Kind, J., Pagie, L., Ortabozkoyun, H., et al. (2013).
    Single-cell dynamics of genome-nuclear lamina interactions. Cell, 153(1), 178-192.

    Incorporate the other commands in readme

    Bulk Command

  1. As a first demonstration, derive A/B compartments from integrated bulk epigenetic data and synthetic data to illustrate the non-negative tensor decomposition method. Navigate to the utilities directory:

    cd hypermatrix/utilities

  2. Make the runPipeline_bulkdata.sh script executable:

    chmod +x runPipeline_bulkdata.sh

  3. Run the runPipeline_bulkdata.sh script to get the necessary data:

    ./runPipeline_bulkdata.sh

differentiate_chromosomes Command

To differentiate between the homologous chromosomes and determine if B compartments are lamina-associated:

python differentiate_chromosomes.py --input <path_to_input_file> --output <output_directory>

Arguments

  • --input: Path to the input file containing chromosome data.
  • --output: Directory where the output files will be saved.

Output

The output directory will contain:

  • homologous_chromosomes.txt: Differentiated homologous chromosomes.
  • lamina_associated_B_compartments.txt: B compartments that are lamina-associated.

Example Command

python differentiate_chromosomes.py --input data/chromosome_data.csv --output results/