generateClassificationAnnotation

a factor that provides the clustering

clustering

a p2 selection object that provided by the web interfact user

selections

Given a cell clustering (partitioning) and a set of user provided selections
generate a cleaned up annotation of cluster groups that can be used for classification

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for Conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Evan Biederstedt

pagoda2

Single Cell Analysis and Differential Expression

Nikolas Barkas

Viktor Petukhov

Peter Kharchenko

Simon Steiger

generateClassificationAnnotation function

Given a cell clustering (partitioning) and a set of user provided selections
generate a cleaned up annotation of cluster groups that can be used for classification — generateClassificationAnnotation

Given a cell clustering (partitioning) and a set of user provided selections
generate a cleaned up annotation of cluster groups that can be used for classification

generateClassificationAnnotation: Given a cell clustering (partitioning) and a set of user provided selections generate a cleaned up annotation of cluster groups that can be used for classification

Description

Usage

Arguments

Value