run_clustering_workflow: Completes the clustering workflow

In NWhitener/benchmarking: Benchmarking Tools For Single Cell RNA Sequencing Datasets

Completes the clustering workflow

Description

This function completes the entire clustering workflow, on different data transformation types, and with different clustering methods. The log data transformation pipeline includes filter_data, annotating, applying the log transformation, selecting the Highly Variable Genes, and finally scaling the data before dimensionality reduction, and clustering. The TFIDF data transformation completes the TFIDF data transformation, annotates the data, and selects the Highly Variable Genes before applying dimensionality reduction and finally clustering. The clustering parameters are set to default, except for the number of clusters in kmeans clustering, which is set to 10

Usage

run_clustering_workflow(
  dataList,
  method = "kmeans",
  transformationType = "log",
  seed = 1,
  numberClusters = 10
)

Arguments

dataList	A list of the data sets
method	The method of clustering to use, defaults to Kmeans
transformationType	The data transformation to be used, defaults to the log transformation
seed	The seed to be set for reproducibility, defaults to 1
numberClusters	The number of clusters to use for Kmeans clustering, defaults to 10

Value

the Data list with all components of the workflow completed

NWhitener/benchmarking documentation built on Oct. 23, 2023, 12:20 a.m.