cluster_LSI: Cluster LSI

In scfurl/m3addon: This package adds to the popular "monocle3"

Description

This function extracts clustering from the last iteration of LSI (see iterativeLSI) cell type differences in a single cell experiment. This function uses the leiden clustering as implemented in monocle3, then finds less granular clusters in the data using partitions (monocle3) using the reduced dimension LSI input from the last iteration of LSI used.

Usage

cluster_LSI(
  cds,
  k = 20,
  weight = F,
  num_iter = 1,
  resolution_parameter = NULL,
  random_seed = 2020,
  verbose = T,
  partition_q_value = 0.05
)

Arguments

cds	the cell_data_set upon which to perform this operation.
k	Nnteger number of nearest neighbors to use when creating the k nearest neighbor graph for Leiden clustering. k is related to the resolution of the clustering result, a bigger k will result in lower resolution and vice versa. Default is 20.
weight	A logical argument to determine whether or not to use Jaccard coefficients for two nearest neighbors (based on the overlapping of their kNN) as the weight used for Louvain clustering. Default is FALSE
num_iter	Integer number of iterations used for Leiden clustering. The clustering result giving the largest modularity score will be used as the final clustering result. Default is 1. Note that if num_iter is greater than 1, the random_seed argument will be ignored for the louvain method.
random_seed	The seed used by the random number generator in louvain-igraph package. This argument will be ignored if num_iter is larger than 1.
verbose	A logic flag to determine whether or not we should print the run details.
binarize	boolean whether to binarize data prior to TFIDF transformation
resolution	Parameter that controls the resolution of clustering. If NULL (Default), the parameter is determined automatically.
partition_qval	Numeric, the q-value cutoff to determine when to partition. Default is 0.05.

References

Granja, J. M.et al. (2019). Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nature Biotechnology, 37(12), 1458–1465.

Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., et al. (2018). The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature, 555(7697), 538–542.

Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, Etienne Lefebvre: Fast unfolding of communities in large networks. J. Stat. Mech. (2008) P10008

V. A. Traag and L. Waltman and N. J. van Eck: From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports, 9(1) (2019). doi: 10.1038/s41598-019-41695-z.

Jacob H. Levine and et. al. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 2015.

scfurl/m3addon documentation built on Aug. 9, 2021, 5:30 p.m.