Pagoda2: Pagoda2 R6 class
In pagoda2: Single Cell Analysis and Differential Expression

Pagoda2

R Documentation

Pagoda2 R6 class

Description

The class encompasses gene count matrices, providing methods for normalization, calculating embeddings, and differential expression.

Public fields

counts: Gene count matrix, normalized on total counts (default=NULL)
modelType: string Model used to normalize count matrices. Only supported values are 'raw', 'plain', and 'linearObs'. – 'plain': Normalize by regressing out on the non-zero observations of each gene (default). – 'raw': Use the raw count matrices, without normalization. The expression matrix taken "as is" without normalization, although log.scale still applies. – 'linearObs': Fit a linear model of pooled counts across all genes against depth. This approach isn't recommened, as the depth dependency is not completely normalized out.
clusters: Results of clustering (default=list())
graphs: Graph representations of the dataset (default=list())
reductions: Results of reductions, e.g. PCA (default=list())
embeddings: Results of visualization algorithms, t-SNE or largeVis (default=list())
diffgenes: Lists of differentially expressed genes (default=list())
n.cores: number of cores (default=1)
misc: list with additional info (default=list())
batch: Batch factor for the dataset (default=NULL)
genegraphs: Slot to store graphical representations in gene space (i.e. gene kNN graphs) (default=list())
depth: Number of molecules measured per cell (default=NULL)

Methods

Public methods

Pagoda2$new()
Pagoda2$setCountMatrix()
Pagoda2$adjustVariance()
Pagoda2$makeKnnGraph()
Pagoda2$getKnnClusters()
Pagoda2$geneKnnbyPCA()
Pagoda2$getHierarchicalDiffExpressionAspects()
Pagoda2$makeGeneKnnGraph()
Pagoda2$getDensityClusters()
Pagoda2$getDifferentialGenes()
Pagoda2$plotDiffGeneHeatmap()
Pagoda2$getRefinedLibSizes()
Pagoda2$plotGeneHeatmap()
Pagoda2$plotEmbedding()
Pagoda2$getOdGenes()
Pagoda2$getNormalizedExpressionMatrix()
Pagoda2$calculatePcaReduction()
Pagoda2$expandOdGenes()
Pagoda2$localPcaKnn()
Pagoda2$testPathwayOverdispersion()
Pagoda2$getEmbedding()
Pagoda2$clone()

Method `new()`

Initialize Pagoda2 class

Usage

Pagoda2$new(
  x,
  modelType = "plain",
  n.cores = parallel::detectCores(logical = FALSE),
  verbose = TRUE,
  min.cells.per.gene = 0,
  trim = round(min.cells.per.gene/2),
  min.transcripts.per.cell = 10,
  batch = NULL,
  lib.sizes = NULL,
  log.scale = TRUE,
  keep.genes = NULL
)

Arguments

x: input count matrix
modelType: Model used to normalize count matrices (default='plain'). Only supported values are 'raw', 'plain', and 'linearObs'.
n.cores: numeric Number of cores to use (default=1)
verbose: boolean Whether to give verbose output (default=TRUE)
min.cells.per.gene: integer Minimum number of cells per gene, used to subset counts for coverage (default=0)
trim: numeric Parameter used for winsorizing count data (default=round(min.cells.per.gene/2)). If value>0, will winsorize counts in normalized space in the hopes of getting a more stable depth estimates. If value<=0, ignored.
min.transcripts.per.cell: integer Minimum number of transcripts per cells, used to subset counts for coverage (default=10)
batch: fctor Batch factor for the dataset (default=NULL)
lib.sizes: character vector of library sizes (default=NULL)
log.scale: boolean If TRUE, scale counts by log() (default=TRUE)
keep.genes: list of genes to keep in count matrix after filtering out by coverage but before normalization (default=NULL)

Returns

new Pagoda2 object

Examples

\donttest{ 
## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=500)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object 
p2_object <- Pagoda2$new(counts, log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 
}

Method `setCountMatrix()`

Provide the initial count matrix, and estimate deviance residual matrix (correcting for depth and batch)

Usage

Pagoda2$setCountMatrix(
  countMatrix,
  depthScale = 1000,
  min.cells.per.gene = 0,
  trim = round(min.cells.per.gene/2),
  min.transcripts.per.cell = 10,
  lib.sizes = NULL,
  log.scale = FALSE,
  keep.genes = NULL,
  verbose = TRUE
)

Arguments

countMatrix: input count matrix
depthScale: numeric Scaling factor for normalizing counts (defaul=1e3). If 'plain', counts are scaled by counts = counts/as.numeric(depth/depthScale).
min.cells.per.gene: integer Minimum number of cells per gene, used to subset counts for coverage (default=0)
trim: numeric Parameter used for winsorizing count data (default=round(min.cells.per.gene/2)). If value>0, will winsorize counts in normalized space in the hopes of getting a more stable depth estimates. If value<=0, ignored.
min.transcripts.per.cell: integer Minimum number of transcripts per cells, used to subset counts for coverage (default=10)
lib.sizes: character vector of library sizes (default=NULL)
log.scale: boolean If TRUE, scale counts by log() (default=TRUE)
keep.genes: list of genes to keep in count matrix after filtering out by coverage but before normalization (default=NULL)
verbose: boolean Whether to give verbose output (default=TRUE)

Returns

normalized count matrix (or if modelTye='raw', the unnormalized count matrix)

Method `adjustVariance()`

Adjust variance of the residual matrix, determine overdispersed sites This is done to normalize the extent to which genes with (very) different expression magnitudes will contribute to the downstream anlaysis.

Usage

Pagoda2$adjustVariance(
  gam.k = 5,
  alpha = 0.05,
  plot = FALSE,
  use.raw.variance = FALSE,
  use.unadjusted.pvals = FALSE,
  do.par = TRUE,
  max.adjusted.variance = 1000,
  min.adjusted.variance = 0.001,
  cells = NULL,
  verbose = TRUE,
  min.gene.cells = 0,
  persist = is.null(cells),
  n.cores = self$n.cores
)

Arguments

gam.k: integer The k used for the generalized additive model 'v ~ s(m, k =gam.k)' (default=5). If gam.k<2, linear regression is used 'lm(v ~ m)'.
alpha: numeric The Type I error probability or the significance level (default=5e-2). This is the criterion used to measure statistical significance, i.e. if the p-value < alpha, then it is statistically significant.
plot: boolean Whether to output the plot (default=FALSE)
use.raw.variance: (default=FALSE). If modelType='raw', then this conditional will be used as TRUE.
use.unadjusted.pvals: boolean Whether to use Benjamini-Hochberg adjusted p-values (default=FALSE).
do.par: boolean Whether to put multiple graphs into a signle plot with par() (default=TRUE)
max.adjusted.variance: numeric Maximum adjusted variance (default=1e3). The gene scale factor is defined as sqrt(pmax(min.adjusted.variance,pmin(max.adjusted.variance,df$qv))/exp(df$v))
min.adjusted.variance: numeric Minimum adjusted variance (default=1e-3). The gene scale factor is defined as sqrt(pmax(min.adjusted.variance,pmin(max.adjusted.variance,df$qv))/exp(df$v))
cells: character vector Subset of cells upon which to perform variance normalization with adjustVariance() (default=NULL)
verbose: boolean Whether to give verbose output (default=TRUE)
min.gene.cells: integer Minimum number of genes per cells (default=0). This parameter is used to filter counts.
persist: boolean Whether to save results (default=TRUE, i.e. is.null(cells)).
n.cores: numeric Number of cores to use (default=1)

Returns

residual matrix with adjusted variance

Examples

\donttest{
## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=500)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object 
p2_object <- Pagoda2$new(counts,log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 
## Normalize gene expression variance
p2_object$adjustVariance(plot=TRUE, gam.k=10)
}

Method `makeKnnGraph()`

Create k-nearest neighbor graph

Usage

Pagoda2$makeKnnGraph(
  k = 30,
  nrand = 1000,
  type = "counts",
  weight.type = "1m",
  odgenes = NULL,
  n.cores = self$n.cores,
  distance = "cosine",
  center = TRUE,
  x = NULL,
  p = NULL,
  var.scale = (type == "counts"),
  verbose = TRUE
)

Arguments

k: integer Number of k clusters for k-NN (default=30)
nrand: numeric Number of randomizations i.e. the gene sets (of the same size) to be evaluated in parallel with each gene set (default=1e3)
type: string Data type of the reduction (default='counts'). If type='counts', this will access the raw counts. Otherwise, 'type' must be name of the reductions.
weight.type: string 'cauchy', 'normal', 'constant', '1m' (default='1m')
odgenes: character vector Overdispersed genes to retrieve (default=NULL)
n.cores: numeric Number of cores to use (default=1)
distance: string Distance metric used: 'cosine', 'L2', 'L1', 'cauchy', 'euclidean' (default='cosine')
center: boolean Whether to use centering when distance='cosine' (default=TRUE). The parameter is ignored otherwise.
x: counts or reduction to use (default=NULL). If NULL, uses counts. Otherwise, checks for the reduction in self$reductions[[type]]
p: (default=NULL)
var.scale: boolean Apply scaling if using raw counts (default=TRUE). If type="counts", var.scale is TRUE by default.
verbose: boolean Whether to give verbose output (default=TRUE)

Returns

kNN graph, stored in self$graphs

Examples

\donttest{
## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=300)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object   
p2_object <- Pagoda2$new(counts,log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 
## Normalize gene expression variance
p2_object$adjustVariance(plot=TRUE, gam.k=10)
## Generate a kNN graph of cells that will allow us to identify clusters of cells
p2_object$makeKnnGraph(k=20, center=FALSE, distance='L2')
}

Method `getKnnClusters()`

Calculate clusters based on the kNN graph

Usage

Pagoda2$getKnnClusters(
  type = "counts",
  method = igraph::multilevel.community,
  name = "community",
  n.cores = self$n.cores,
  g = NULL,
  min.cluster.size = 1,
  persist = TRUE,
  ...
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
method: Method to use (default=igraph::multilevel.community). Accepted methods are either 'igraph::infomap.community' or 'igraph::multilevel.community'. If NULL, if the number of vertices of the graph is greater than or equal to 2000, 'igraph::multilevel.community' will be used. Otherwise, 'igraph::infomap.community' will be used.
name: string Name of the community structure calculated from 'method' (default='community')
n.cores: numeric Number of cores to use (default=1)
g: Input graph (default=NULL). If NULL, access graph from self$graphs[[type]].
min.cluster.size: Minimum size of clusters (default=1). This parameter is primarily used to remove very small clusters.
persist: boolean Whether to save the clusters and community structure (default=TRUE)
...: Additional parameters to pass to 'method'

Returns

the community structure calculated from 'method'

Method `geneKnnbyPCA()`

Deprecated function. Use makeGeneKnnGraph() instead.

Usage

Pagoda2$geneKnnbyPCA()

Method `getHierarchicalDiffExpressionAspects()`

Take a given clustering and generate a hierarchical clustering

Usage

Pagoda2$getHierarchicalDiffExpressionAspects(
  type = "counts",
  groups = NULL,
  clusterName = NULL,
  method = "ward.D",
  dist = "pearson",
  persist = TRUE,
  z.threshold = 2,
  n.cores = self$n.cores,
  min.set.size = 5,
  verbose = TRUE
)

Arguments

type: string Data type of the reduction (default='counts'). If type='counts', this will access the raw counts. Otherwise, 'type' must be name of the reductions.
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
clusterName: string Cluster name to access (default=NULL)
method: string The agglomeration method to be used in stats::hcust(method=method) (default='ward.D'). Accepted values are: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). For more information, see stats::hclust().
dist: string 'pearson', 'spearman', 'euclidean', 'L2', 'JS' (default='pearson')
persist: boolean Whether to save the clusters and community structure (default=TRUE)
z.threshold: numeric Threshold of z-scores to filter, >=z.threshold are kept (default=2)
n.cores: numeric Number of cores to use (default=1)
min.set.size: integer Minimum threshold of sets to keep (default=5)
verbose: boolean Whether to give verbose output (default=TRUE)

Returns

hierarchical clustering

Method `makeGeneKnnGraph()`

Calculates gene Knn network for gene similarity

Usage

Pagoda2$makeGeneKnnGraph(
  nPcs = 100,
  center = TRUE,
  fastpath = TRUE,
  maxit = 1000,
  k = 30,
  n.cores = self$n.cores,
  verbose = TRUE
)

Arguments

nPcs: integer Number of principal components (default=100). This is the parameter 'nv' in irlba::irlba(), the number of right singular vectors to estimate.
center: boolean Whether to center the PCA (default=TRUE)
fastpath: boolean Whether to try a (fast) C algorithm implementation if possible (default=TRUE). This parameter is equivalent to 'fastpath' in irlba::irlba().
maxit: integer Maximum number of iterations (default=1000). This parameter is equivalent to 'maxit' in irlba::irlba().
k: integer Number of k clusters for calculating k-NN on the resulting principal components (default=30).
n.cores: numeric Number of cores to use (default=1)
verbose: boolean Whether to give verbose output (default=TRUE)

Returns

graph with gene similarity

Method `getDensityClusters()`

Calculate density-based clusters

Usage

Pagoda2$getDensityClusters(
  type = "counts",
  embeddingType = NULL,
  name = "density",
  eps = 0.5,
  v = 0.7,
  s = 1,
  verbose = TRUE,
  ...
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
embeddingType: The type of embedding used when calculating with ‘getEmbedding()' (default=NULL). Accepted values are: ’largeVis', 'tSNE', 'FR', 'UMAP', 'UMAP_graph'
name: string Name fo the clustering (default='density').
eps: numeric value of the eps parameter, fed into dbscan::dbscan(x=emb, eps=eps, ...)
v: numeric The “value” to be used to complete the HSV color descriptions (default=0.7). Equivalent to the 'v' parameter in grDevices::rainbow().
s: numeric The “saturation” to be used to complete the HSV color descriptions (default=1). Equivalent to the 's' parameter in grDevices::rainbow().
verbose: boolean Whether to give verbose output (default=TRUE)
...: Additional parameters passed to dbscan::dbscan(emb, ...)

Returns

density-based clusters

Method `getDifferentialGenes()`

Determine differentially expressed genes, comparing each group against all others using Wilcoxon rank sum test

Usage

Pagoda2$getDifferentialGenes(
  type = "counts",
  clusterType = NULL,
  groups = NULL,
  name = "customClustering",
  z.threshold = 3,
  upregulated.only = FALSE,
  verbose = FALSE,
  append.specificity.metrics = TRUE,
  append.auc = FALSE
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
clusterType: Optional cluster type to use as a group-defining factor (default=NULL)
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
name: string Slot to store the results in (default='customClustering')
z.threshold: numeric Minimal absolute Z score (adjusted) to report (default=3)
upregulated.only: boolean Whether to report only genes that are expressed significantly higher in each group (default=FALSE)
verbose: boolean Whether to give verbose output (default=FALSE)
append.specificity.metrics: boolean Whether to append specifity metrics (default=TRUE). Uses the function sccore::appendSpecificityMetricsToDE().
append.auc: boolean If TRUE, append AUC values (default=FALSE). Parameter ignored if append.specificity.metrics is FALSE.

Returns

List with each element of the list corresponding to a cell group in the provided/used factor (i.e. factor levels) Each element of a list is a data frame listing the differentially epxressed genes (row names), with the following columns: Z - adjusted Z score, with positive values indicating higher expression in a given group compare to the rest M - log2 fold change highest- a boolean flag indicating whether the expression of a given gene in a given vcell group was on average higher than in every other cell group fe - fraction of cells in a given group having non-zero expression level of a given gene

Method `plotDiffGeneHeatmap()`

Plot heatmap of DE results

Usage

Pagoda2$plotDiffGeneHeatmap(
  type = "counts",
  clusterType = NULL,
  groups = NULL,
  n.genes = 100,
  z.score = 2,
  gradient.range.quantile = 0.95,
  inner.clustering = FALSE,
  gradientPalette = NULL,
  v = 0.8,
  s = 1,
  box = TRUE,
  drawGroupNames = FALSE,
  ...
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
clusterType: Optional cluster type to use as a group-defining factor (default=NULL)
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
n.genes: integer Number of genes to plot (default=100)
z.score: numeric Threshold of z-scores to filter (default=2). Only greater than or equal to this value are kept.
gradient.range.quantile: numeric Trimming quantile (default=0.95)
inner.clustering: boolean Whether to cluster cells within each cluster (default=FALSE)
gradientPalette: palette of colors to use (default=NULL). If NULL, uses 'colorRampPalette(c('gray90','red'), space = "Lab")(1024)'
v: numeric The “value” to be used to complete the HSV color descriptions (default=0.7). Equivalent to the 'v' parameter in grDevices::rainbow().
s: numeric The “saturation” to be used to complete the HSV color descriptions (default=1). Equivalent to the 's' parameter in grDevices::rainbow().
box: boolean Whether to draw a box around the current plot in the given color and linetype (default=TRUE)
drawGroupNames: boolean Whether to draw group names (default=FALSE)
...: Additional parameters passed to internal function used for heatmap plotting, my.heatmap2()

Returns

heatmap of DE results

Method `getRefinedLibSizes()`

Recalculate library sizes using robust regression within clusters

Usage

Pagoda2$getRefinedLibSizes(
  clusterType = NULL,
  groups = NULL,
  type = "counts",
  n.cores = self$n.cores
)

Arguments

clusterType: Name of cluster to access (default=NULL). If NULL, takes the most recently generated clustering. Parameter ignored if groups is not NULL.
groups: factor named with cell names specifying the clusters of cells (default=NULL)
type: string Either 'counts' or the name of a stored embedding, names(self$embeddings) (default=NULL)
n.cores: numeric Number of cores to use (default=self$n.cores=1)

Returns

recalculated library sizes

Method `plotGeneHeatmap()`

Plot heatmap for a given set of genes

Usage

Pagoda2$plotGeneHeatmap(
  genes,
  type = "counts",
  clusterType = NULL,
  groups = NULL,
  gradient.range.quantile = 0.95,
  cluster.genes = FALSE,
  inner.clustering = FALSE,
  gradientPalette = NULL,
  v = 0.8,
  s = 1,
  box = TRUE,
  drawGroupNames = FALSE,
  useRaster = TRUE,
  smooth.span = max(1, round(nrow(self$counts)/1024)),
  ...
)

Arguments

genes: character vector Gene names
type: string Data type (default='counts'). Currently only 'counts' supported.
clusterType: Optional cluster type to use as a group-defining factor (default=NULL)
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
gradient.range.quantile: numeric Trimming quantile (default=0.95)
cluster.genes: boolean Whether to cluster genes within each cluster using stats::hclust() (default=FALSE)
inner.clustering: boolean Whether to cluster cells within each cluster (default=FALSE)
gradientPalette: palette of colors to use (default=NULL). If NULL, uses 'colorRampPalette(c('gray90','red'), space = "Lab")(1024)'
v: numeric The “value” to be used to complete the HSV color descriptions (default=0.7). Equivalent to the 'v' parameter in grDevices::rainbow().
s: numeric The “saturation” to be used to complete the HSV color descriptions (default=1). Equivalent to the 's' parameter in grDevices::rainbow().
box: boolean Whether to draw a box around the current plot in the given color and linetype (default=TRUE)
drawGroupNames: boolean Whether to draw group names (default=FALSE)
useRaster: boolean If TRUE a bitmap raster is used to plot the image instead of polygons (default=TRUE). The grid must be regular in that case, otherwise an error is raised. For more information, see graphics::image().
smooth.span: (default=max(1,round(nrow(self$counts)/1024)))
...: Additional parameters passed to internal function used for heatmap plotting, my.heatmap2()

Returns

plot of gene heatmap

Method `plotEmbedding()`

Show embedding

Usage

Pagoda2$plotEmbedding(
  type = NULL,
  embeddingType = NULL,
  clusterType = NULL,
  groups = NULL,
  colors = NULL,
  gene = NULL,
  plot.theme = ggplot2::theme_bw(),
  ...
)

Arguments

type: string Either 'counts' or the name of a stored embedding, names(self$embeddings) (default=NULL)
embeddingType: string Embedding type (default=NULL). If NULL, takes the most recently generated embedding.
clusterType: Name of cluster to access (default=NULL). If NULL, takes the most recently generated clustering. Parameter ignored if groups is not NULL.
groups: factor named with cell names specifying the clusters of cells (default=NULL)
colors: character vector List of gene names (default=NULL)
gene: (default=NULL)
plot.theme: (default=ggplot2::theme_bw())
...: Additional parameters passed to sccore::embeddingPlot()

Returns

plot of the embedding

Method `getOdGenes()`

Get overdispersed genes

Usage

Pagoda2$getOdGenes(
  n.odgenes = NULL,
  alpha = 0.05,
  use.unadjusted.pvals = FALSE
)

Arguments

n.odgenes: integer Number of overdispersed genes to retrieve (default=NULL). If NULL, will return all.
alpha: numeric The Type I error probability or the significance level (default=5e-2). This is the criterion used to measure statistical significance, i.e. if the p-value < alpha, then it is statistically significant.
use.unadjusted.pvals: boolean Whether to use Benjamini-Hochberg adjusted p-values (default=FALSE).

Returns

vector of overdispersed genes

Method `getNormalizedExpressionMatrix()`

Return variance-normalized matrix for specified genes or a number of OD genes

Usage

Pagoda2$getNormalizedExpressionMatrix(genes = NULL, n.odgenes = NULL)

Arguments

genes: vector of gene names to explicitly return (default=NULL)
n.odgenes: integer Number of overdispersed genes to retrieve (default=NULL). If NULL, will return all.

Returns

cell by gene matrix

Method `calculatePcaReduction()`

Calculate PCA reduction of the data

Usage

Pagoda2$calculatePcaReduction(
  nPcs = 20,
  type = "counts",
  name = "PCA",
  use.odgenes = TRUE,
  n.odgenes = NULL,
  odgenes = NULL,
  center = TRUE,
  cells = NULL,
  fastpath = TRUE,
  maxit = 100,
  verbose = TRUE,
  var.scale = (type == "counts"),
  ...
)

Arguments

nPcs: numeric Number of principal components (PCs) (default=20)
type: string Dataset view to reduce (counts by default, but can specify a name of an existing reduction) (default='counts')
name: string Name for the PCA reduction to be created (default='PCA')
use.odgenes: boolean Whether pre-calculated set of overdispersed genes should be used (default=TRUE)
n.odgenes: integer Number of overdispersed genes to retrieve (default=NULL). If NULL, will return all.
odgenes: Explicitly specify a set of overdispersed genes to use for the reduction (default=NULL)
center: boolean Whether data should be centered prior to PCA (default=TRUE)
cells: optional subset of cells on which PCA should be run (default=NULL)
fastpath: boolean Use C implementation for speedup (default=TRUE)
maxit: numeric Maximum number of iterations (default=100). For more information, see 'maxit' parameter in irlba::irlba().
verbose: boolean Whether to give verbose output (default=TRUE)
var.scale: boolean Apply scaling if using raw counts (default=TRUE). If type="counts", var.scale is TRUE by default.
...: additional arguments forwarded to irlba::irlba

Returns

Invisible PCA result (the reduction itself is saved in self$reductions[[name]])"

Method `expandOdGenes()`

Reset overdispersed genes 'odgenes' to be a superset of the standard odgene selection (guided by n.odgenes or alpha), and a set of recursively determined odgenes based on a given group (or a cluster info)

Usage

Pagoda2$expandOdGenes(
  type = "counts",
  clusterType = NULL,
  groups = NULL,
  min.group.size = 30,
  od.alpha = 0.1,
  use.odgenes = FALSE,
  n.odgenes = NULL,
  odgenes = NULL,
  n.odgene.multiplier = 1,
  gam.k = 10,
  verbose = FALSE,
  n.cores = self$n.cores,
  min.odgenes = 10,
  max.odgenes = Inf,
  recursive = TRUE
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
clusterType: Optional cluster type to use as a group-defining factor (default=NULL)
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
min.group.size: integer Number of minimum cells for filtering out group size (default=30)
od.alpha: numeric The Type I error probability or the significance level for calculating overdispersed genes (default=1e-1). This is the criterion used to measure statistical significance, i.e. if the p-value < alpha, then it is statistically significant.
use.odgenes: boolean Whether pre-calculated set of overdispersed genes should be used (default=FALSE)
n.odgenes: integer Number of overdispersed genes to retrieve (default=NULL). If NULL, will return all.
odgenes: Explicitly specify a set of overdispersed genes to use for the reduction (default=NULL) #' @param odgenes (default=NULL)
n.odgene.multiplier: numeric (default=1)
gam.k: integer The k used for the generalized additive model 'v ~ s(m, k =gam.k)' (default=10). If gam.k<2, linear regression is used 'lm(v ~ m)'.
verbose: boolean Whether to give verbose output (default=TRUE)
n.cores: numeric Number of cores to use (default=1)
min.odgenes: integer Minimum number of overdispersed genes to use (default=10)
max.odgenes: integer Maximum number of overdispersed genes to use (default=Inf)
recursive: boolean Whether to determine groups for which variance normalization will be rerun (default=TRUE)

Returns

List of overdispersed genes

Method `localPcaKnn()`

local PCA implementation

Usage

Pagoda2$localPcaKnn(
  nPcs = 5,
  type = "counts",
  clusterType = NULL,
  groups = NULL,
  k = 30,
  b = 1,
  a = 1,
  min.group.size = 30,
  name = "localPCA",
  od.alpha = 0.1,
  n.odgenes = NULL,
  gam.k = 10,
  verbose = FALSE,
  n.cores = self$n.cores,
  min.odgenes = 5,
  take.top.odgenes = FALSE,
  recursive = TRUE,
  euclidean = FALSE,
  perplexity = k,
  return.pca = FALSE,
  skip.pca = FALSE
)

Arguments

nPcs: integer Number of principal components (default=5)
type: string Data type (default='counts'). Currently only 'counts' supported.
clusterType: Optional cluster type to use as a group-defining factor (default=NULL)
groups: factor named with cell names specifying the clusters of cells to be compared (one against all) (default=NULL). To compare two cell clusters against each other, simply pass a factor containing only two levels.
k: integer Number of components for kNN graph (default=30)
b: numeric Constant within exp(-b*(ncid/cldsd)^2), used for calculating cell relevance per cluster (default=1)
a: numeric Constant within "(1-exp(-a*(dsq)/(p$pcs$trsd^2)))*(pk /outerproduct pk)" (default=1)
min.group.size: integer Number of minimum cells for filtering out group size (default=30)
name: string Title (default='localPCA')
od.alpha: numeric Significance level for calculating overdispersed genes (default=1e-1). P-values will be filtered by <log(od.alpha).
n.odgenes: integer Number of overdispersed genes to retrieve (default=NULL). If NULL, will return all.
gam.k: integer The k used for the generalized additive model 'v ~ s(m, k =gam.k)' (default=10). If gam.k<2, linear regression is used 'lm(v ~ m)'.
verbose: boolean Whether to give verbose output (default=TRUE)
n.cores: numeric Number of cores to use (default=1)
min.odgenes: integer Minimum number of overdispersed genes to use (default=5)
take.top.odgenes: boolean Take top overdispersed genes in decreasing order (default=FALSE)
recursive: boolean Whether to recursively determine groups for which variance normalization will be rerun (default=FALSE)
euclidean: boolean Whether to applied euclidean-based distance similarity during variance normalization (default=FALSE)
perplexity: integer Perplexity parameter within Rtsne::Rtsne() (default=k). Please see Rtsne for more details.
return.pca: boolean Whether to return the PCs (default=FALSE)
skip.pca: boolean If TRUE and return.pca=TRUE, will return a list of scale factors, cells, and overdispersed genes, i.e. list(sf=sf, cells=cells, odgenes=odgenes) (default=FALSE). Otherwise, ignored.

Returns

localPcaKnn return here

Method `testPathwayOverdispersion()`

Test pathway overdispersion Note: this is a compressed version of the PAGODA1 approach in SCDE <https://hms-dbmi.github.io/scde/>

Usage

Pagoda2$testPathwayOverdispersion(
  setenv,
  type = "counts",
  max.pathway.size = 1000,
  min.pathway.size = 10,
  n.randomizations = 5,
  verbose = FALSE,
  n.cores = self$n.cores,
  score.alpha = 0.05,
  plot = FALSE,
  cells = NULL,
  adjusted.pvalues = TRUE,
  z.score = qnorm(0.05/2, lower.tail = FALSE),
  use.oe.scale = FALSE,
  return.table = FALSE,
  name = "pathwayPCA",
  correlation.distance.threshold = 0.2,
  loading.distance.threshold = 0.01,
  top.aspects = Inf,
  recalculate.pca = FALSE,
  save.pca = TRUE
)

Arguments

setenv: Specific environment for pathway analysis
type: string Data type (default='counts'). Currently only 'counts' supported.
max.pathway.size: integer Maximum number of observed genes in a valid gene set (default=1e3)
min.pathway.size: integer Minimum number of observed genes that should be contained in a valid gene set (default=10)
n.randomizations: numeric Number of random gene sets (of the same size) to be evaluated in parallel with each gene set (default=5). (This can be kept at 5 or 10, but should be increased to 50-100 if the significance of pathway overdispersion will be determined relative to random gene set models.)
verbose: boolean Whether to give verbose output (default=TRUE)
n.cores: numeric Number of cores to use (default=1)
score.alpha: numeric Significance level of the confidence interval for determining upper/lower bounds (default=0.05)
plot: boolean Whether to output the plot (default=FALSE)
cells: character vector Specific cells to investigate (default=NULL)
adjusted.pvalues: boolean Whether to use adjusted p-values (default=TRUE)
z.score: numeric Z-score to be used as a cutoff for statistically significant patterns (default=qnorm(0.05/2, lower.tail = FALSE))
use.oe.scale: boolean Whether the variance of the returned aspect patterns should be normalized using observed/expected value instead of the default chi-squared derived variance corresponding to overdispersion Z-score (default=FALSE)
return.table: boolean Whether to return a text table with results (default=FALSE)
name: string Title (default='pathwayPCA')
correlation.distance.threshold: numeric Similarity threshold for grouping interdependent aspects in pagoda.reduce.redundancy() (default=0.2)
loading.distance.threshold: numeric Similarity threshold for grouping interdependent aspects in pagoda.reduce.loading.redundancy() (default=0.2)
top.aspects: Restrict output to the top N aspects of heterogeneity (default=Inf)
recalculate.pca: boolean Whether to recalculate PCA (default=FALSE)
save.pca: boolean Whether to save the PCA results (default=TRUE). If TRUE, caches them in self$misc[['pwpca']].

Returns

pathway output

Method `getEmbedding()`

Return embedding

Usage

Pagoda2$getEmbedding(
  type = "counts",
  embeddingType = "largeVis",
  name = NULL,
  dims = 2,
  M = 1,
  gamma = 1/M,
  perplexity = 50,
  verbose = TRUE,
  sgd_batches = NULL,
  diffusion.steps = 0,
  diffusion.power = 0.5,
  distance = "pearson",
  n.cores = self$n.cores,
  n.sgd.cores = n.cores,
  ...
)

Arguments

type: string Data type (default='counts'). Currently only 'counts' supported.
embeddingType: string Type of embedding to construct (default='largeVis'). Possible values are: 'largeVis', 'tSNE', 'FR' (Fruchterman–Reingold), 'UMAP', 'UMAP_graph'
name: string Name of the embedding (default=NULL). If NULL, the name = embeddingType.
dims: integer Parameter 'dims' Matrix::sparseMatrix(); a non-negative, integer, dimensions vector of length 2 (default=2). See Matrix package documentation for more details.
M: numeric (largeVis) The number of negative edges to sample for each positive edge (default=5). Parameter only used if embeddingType is 'largeVis'.
gamma: numeric (largeVis) The strength of the force pushing non-neighbor nodes apart (default=7). Parameter only used if embeddingType is 'largeVis'.
perplexity: numeric Parameter 'perplexity' within largeVis::buildWijMatrix() (default=50). Please see the largeVis documentation for more details.
verbose: boolean Whether to give verbose output (default=TRUE)
sgd_batches: numeric The number of edges to process during SGD (default=NULL). Passed to projectKNNs(). Defaults to a value set based on the size of the dataset. If the parameter given is between 0 and 1, the default value will be multiplied by the parameter.
diffusion.steps: integer Iteration steps to use. If 0, no steps are run. (default=0)
diffusion.power: numeric Factor to be used when calculating diffusion, (default=0.5)
distance: string 'pearson', 'spearman', 'euclidean', 'L2', 'JS' (default='pearson')
n.cores: numeric Number of cores to use (default=1)
n.sgd.cores: numeric Number of cores to use (default=n.cores)
...: Additional parameters passed to embedding functions, Rtsne::Rtsne() if 'L2', uwot::umap() if 'UMAP', embedKnnGraphUmap() if 'UMAP_graph'

Returns

embedding stored in self$embedding

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Pagoda2$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

Simon Steiger

Examples


## ------------------------------------------------
## Method `Pagoda2$new`
## ------------------------------------------------

 
## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=500)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object 
p2_object <- Pagoda2$new(counts, log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 



## ------------------------------------------------
## Method `Pagoda2$adjustVariance`
## ------------------------------------------------


## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=500)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object 
p2_object <- Pagoda2$new(counts,log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 
## Normalize gene expression variance
p2_object$adjustVariance(plot=TRUE, gam.k=10)



## ------------------------------------------------
## Method `Pagoda2$makeKnnGraph`
## ------------------------------------------------


## Load pre-generated a dataset of 50 bone marrow cells as matrix
cm <- readRDS(system.file("extdata", "sample_BM1_50.rds", package="pagoda2"))
## Perform QC, i.e. filter any cells that
counts <- gene.vs.molecule.cell.filter(cm, min.cell.size=300)
rownames(counts) <- make.unique(rownames(counts))
## Generate Pagoda2 object   
p2_object <- Pagoda2$new(counts,log.scale=TRUE, min.cells.per.gene=10, n.cores=1) 
## Normalize gene expression variance
p2_object$adjustVariance(plot=TRUE, gam.k=10)
## Generate a kNN graph of cells that will allow us to identify clusters of cells
p2_object$makeKnnGraph(k=20, center=FALSE, distance='L2')

pagoda2 documentation built on June 24, 2024, 9:09 a.m.

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pagoda2 Single Cell Analysis and Differential Expression

Pagoda2: Pagoda2 R6 class In pagoda2: Single Cell Analysis and Differential Expression

Pagoda2 R6 class

Description

Public fields

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method setCountMatrix()

Usage

Arguments

Returns

Method adjustVariance()

Usage

Arguments

Returns

Examples

Method makeKnnGraph()

Usage

Arguments

Returns

Examples

Method getKnnClusters()

Usage

Arguments

Returns

Method geneKnnbyPCA()

Usage

Method getHierarchicalDiffExpressionAspects()

Usage

Arguments

Returns

Method makeGeneKnnGraph()

Usage

Arguments

Returns

Method getDensityClusters()

Usage

Arguments

Returns

Method getDifferentialGenes()

Usage

Arguments

Returns

Method plotDiffGeneHeatmap()

Usage

Arguments

Returns

Method getRefinedLibSizes()

Usage

Arguments

Returns

Method plotGeneHeatmap()

Usage

Arguments

Returns

Method plotEmbedding()

Usage

Arguments

Returns

Method getOdGenes()

Usage

Arguments

Returns

Method getNormalizedExpressionMatrix()

Usage

Arguments

Returns

Method calculatePcaReduction()

Usage

Arguments

Returns

Method expandOdGenes()

Usage

Arguments

Returns

pagoda2
Single Cell Analysis and Differential Expression

Pagoda2: Pagoda2 R6 class
In pagoda2: Single Cell Analysis and Differential Expression

Method `new()`

Method `setCountMatrix()`

Method `adjustVariance()`

Method `makeKnnGraph()`

Method `getKnnClusters()`

Method `geneKnnbyPCA()`

Method `getHierarchicalDiffExpressionAspects()`

Method `makeGeneKnnGraph()`

Method `getDensityClusters()`

Method `getDifferentialGenes()`

Method `plotDiffGeneHeatmap()`

Method `getRefinedLibSizes()`

Method `plotGeneHeatmap()`

Method `plotEmbedding()`

Method `getOdGenes()`

Method `getNormalizedExpressionMatrix()`

Method `calculatePcaReduction()`

Method `expandOdGenes()`

Method `localPcaKnn()`

Method `testPathwayOverdispersion()`

Method `getEmbedding()`

Method `clone()`