Last updated: 2017-10-19

Code version: 861184a54d0981caa0791fd3a31ddee6995fa00b

Here’s a rough outline of the project aims. Will be updated as the project goes…

Aim 1: Establish similarities and differences in practical considerations for the analysis of single-cell and bulk RNA-seq expression count.

  1. Overdispersion (variance-mean dependency)

  2. Sparsity: technical artifact or biological reality?

Aim 2: Evaluate performances of DE methods.

  1. Is it a coverage issue? Investigate bulk method’s performance on single-cell data versus thinned data.

  2. Lowly versus highly expressed genes?

  3. Pseudo-count and filtering

  4. Low versus high variation?

  5. Sample size

Aim 3: Tailor normalization/DE for single-cell RNA-seq data?

  1. UMI versus non-UMI

  2. Sparsity/levels of expression

This R Markdown site was created with workflowr