In BatadaLab/scID: geneset-guided identification of cell types using single cell RNA-seq data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Usage

There are three ways to use scID.

Usage 1: Canonical usage (for finding equivalent groups of cells across data)

Given two datasets of single-cell RNA-seq gene expression for which cell grouping for one the datasets (reference) is known, scID seeks to find transcriptionally equivalent groups of cells for the second dataset (target).

scID_output <- scID::scid_multiclass(target_gem, reference_gem, reference_clusters, ...)

Input

1. target_gem An nxm data frame of n genes (rows) in m cells (columns) of the dataset with unknown grouping, where each entry is library-depth or column normalized gene expression. Cell names are expected to be unique.
2. reference_gem An NxM data frame of N genes (rows) in M cells (columns) of the dataset with known grouping, where each entry is library-depth or column normalized gene expression.
3. reference_clusters A list of cluster labels for the reference cells.

Output

scID_output is a list of two objects

1. scID_output$labels A named list of cluster labels for the target cells

2. scID_output$markers A data frame of signature genes extracted from the reference clusters.

Usage 2: Canonical usage (for finding equivalent groups of cells across data) with multiple targets (T1, T2)

• Step 1: Extract markers from reference clusters

markers_generated_by_scID <- scID::find_markers(reference_gem, reference_clusters, logFC)

This step can be skipped when the user has own method for extracting markers.

• Step 2: Find transctiptionally equivalent cells in target datasets

scID_output_T1 <- scID:scid_multiclass(T1, markers_generated_by_scID, ...)

scID_output_T2 <- scID::scid_multiclass(T2, markers_generated_by_scID, ...)

Usage 3: User-specified cluster gene signatures

A pre-computed set of markers can be given as input by the user alternatively. The markers object has to be a data frame with genes and cluster ID in columns as in this example file.

scID_output <- scID::scid_multiclass(T, markers_generated_by_user, ...)

Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The html_vignette output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The html_vignette format:

• Never uses retina figures
• Has a smaller default figure size
• Uses a custom CSS stylesheet instead of the default Twitter Bootstrap style

Vignette Info

Note the various macros within the vignette section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the title field and the \VignetteIndexEntry to match the title of your vignette.

Styles

The html_vignette template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:

output: 
  rmarkdown::html_vignette:
    css: mystyles.css

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

BatadaLab/scID documentation built on Oct. 21, 2021, 3:06 p.m.