tp2c.view.pathways

character vector of pathway or gene names

pathways

environment mapping pathways to genes (default=NULL)

goenv

factor (corresponding to rows of the model matrix) specifying batch assignment of each cell, to perform batch correction (default=NULL).

batch

integer Number of genes to show (default=20)

n.genes

boolean If TRUE, the set of shown genes should be split among highest and lowest loading (default=TRUE). If FALSE, genes with highest absolute loading should be shown.

two.sided

integer vector Number of principal component to show for each listed pathway(default=rep(1, length(pathways)))

n.pc

column color matrix (default=NULL)

colcols

numeric z color limit (default=NULL)

zlim

labRow

positive numbers, used as cex.axis in for the row or column axis labeling(default=1)

cexCol

cexRow

integer Number of random starts to use (default=50)

nstarts

row order (default=NULL). If NULL, uses order from hclust.

row.order

boolean Whether to show cell dendrogram (default=TRUE)

show.Colv

boolean Whether to plot (default=TRUE)

plot

numeric Winsorization trim that should be applied (default=1.1/nrow(p2$counts)). Note that p2 is a 'Pagoda2' object.

trim

showPC

parameters to pass to my.heatmap2. Only if plot is TRUE.

View pathway or gene-weighted PCA
'Pagoda2' version of the function pagoda.show.pathways()
Takes in a list of pathways (or a list of genes), runs weighted PCA, optionally showing the result.

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

tp2c.view.pathways function

View pathway or gene-weighted PCA
'Pagoda2' version of the function pagoda.show.pathways()
Takes in a list of pathways (or a list of genes), runs weighted PCA, optionally showing the result. — tp2c.view.pathways

View pathway or gene-weighted PCA
'Pagoda2' version of the function pagoda.show.pathways()
Takes in a list of pathways (or a list of genes), runs weighted PCA, optionally showing the result.

tp2c.view.pathways: View pathway or gene-weighted PCA 'Pagoda2' version of the function pagoda.show.pathways() Takes in a list of pathways (or a list of genes), runs weighted PCA, optionally showing the result.

Description

Usage

Arguments

Value