In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

all_times <- list()  # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      now <<- Sys.time()
    } else {
      res <- difftime(Sys.time(), now, units = "secs")
      all_times[[options$label]] <<- res
    }
  }
}))
knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  time_it = TRUE
)

The following workflows are outlined in this section:

1. Prepare data for custom downstream analysis
2. Determine cluster-Specific Genes
3. Annotate cell types
4. Evaluate gene set expression(s) and associations
5. Identify gene modules

1. Prepare data for custom downstream analysis

1. M01: Pre-process data
   • Generates Seurat object with normalized and scaled single-cell data.
2. M02: Integrate data (optional)
   • Only necessary if batch correction and integration is required for multiple data sets.
3. M18: Identify optimal cluster resolution (optional)
   • Evaluate cell population clusters at several resolutions to identify optimal resolution.

2. Determine cluster-specific genes

1. M01: Pre-process data
   • Generates Seurat object with normalized and scaled single-cell data.
2. M02: Integrate data (optional)
   • Only necessary if batch correction and integration is required for multiple data sets.
3. M18: Identify optimal cluster resolution
   • Evaluate cell population clusters at several resolutions to identify optimal resolution.
   • Differential genes are computed for each evaluated cluster using Wilcoxon and CDI DE methods.

3. Annotate cell types

1. M01: Pre-process data
   • Generates Seurat object with normalized and scaled single-cell data.
2. M02: Integrate data (optional)
   • Only necessary if batch correction and integration is required for multiple data sets.
3. M18: Identify optimal cluster resolution (optional)
   • Evaluate cell population clusters at several resolutions to identify optimal resolution.
4. M05: Annotate cell types
   • Annotate clusters at resolution identified in M18.

4. Evaluate gene expression(s) and associations

1. M01: Pre-process data
   • Generates Seurat object with normalized and scaled single-cell data.
2. M02: Integrate data (optional)
   • Only necessary if batch correction and integration is required for multiple data sets.
3. M18: Identify optimal cluster resolution (optional)
   • Evaluate cell population clusters at several resolutions to identify optimal resolution.
4. M09: Evaluate gene expression and association profiles

5. Identify gene modules

1. M01: Pre-process data
   • Generates Seurat object with normalized and scaled single-cell data.
2. M02: Integrate data (optional)
   • Only necessary if batch correction and integration is required for multiple data sets.
3. M18: Identify optimal cluster resolution (optional)
   • Evaluate cell population clusters at several resolutions to identify optimal resolution.
4. M24: Identify gene modules.

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.