Package 'scviR'

produce a heatmap from a specialized CITE-seq SingleCellExperiment

Description

produce a heatmap from a specialized CITE-seq SingleCellExperiment

Usage

adtProfiles(x, lb = -3, ub = 3, do_z = FALSE)

Arguments

x	SingleCellExperiment instance that has an 'se.averaged' component in its metadata
lb	numeric(1) lower bound on 'breaks' sequence for ComplexHeatmap::pheatmap, defaults to -3
ub	numeric(1) upper bound on 'breaks' sequence for ComplexHeatmap::pheatmap, defaults to 3
do_z	logical(1) if TRUE, divide the residuals by their standard deviation across clusters, defaults to false

Value

ComplexHeatmap::pheatmap instance

side effect of pheatmap::pheatmap call

Note

See the OSCA book ch12.5.2 for the application.

Examples

ch12sce <- getCh12Sce()
adtProfiles(ch12sce)
adtProfiles(ch12sce, do_z = TRUE)

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basic interface to anndata

Description

basic interface to anndata

Usage

anndataR()

Value

basiliskRun result with import from reticulate, typically a Module

Examples

ad <- anndataR()
ad
ad$read

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python declarations

Description

python declarations

Usage

bsklenv

Format

An object of class BasiliskEnvironment of length 1.

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grab scvi-tools-processed PBMC CITE-seq data in anndata format (gzipped) from Open Storage Network

Description

grab scvi-tools-processed PBMC CITE-seq data in anndata format (gzipped) from Open Storage Network

Usage

cacheCiteseq5k10kPbmcs()

Value

invisibly, the path to the .h5ad file

Note

Original h5ad files obtained using scvi-tools 0.18.0 scvi.data.pbmcs_10x_cite_seq, then processed according to steps in the scviR vignette, which follow the [scvi-tools tutorial](https://colab.research.google.com/github/scverse/scvi-tutorials/blob/0.18.0/totalVI.ipynb) by Gayoso et al.

It may be advantageous to set 'options(timeout=3600)' or to allow an even greater time for internet downloads, if working at a relatively slow network connection.

Examples

h5path <- cacheCiteseq5k10kPbmcs()
cmeta <- rhdf5::h5ls(h5path)
dim(cmeta)
head(cmeta, 17)

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grab scvi-tools VAE instance built on the PBMC datasets following the tutorial

Description

grab scvi-tools VAE instance built on the PBMC datasets following the tutorial

Usage

cacheCiteseq5k10kTutvae()

Value

invisibly, the path to the .zip file holding the fitted VAE and associated data

Note

VAE construction followed tutorial at 'https://docs.scvi-tools.org/en/stable/tutorials/notebooks/totalVI.html'.

It may be advantageous to set 'options(timeout=3600)' or to allow an even greater time for internet downloads, if working at a relatively slow network connection.

Examples

zpath <- cacheCiteseq5k10kTutvae()
td <- tempdir()
utils::unzip(zpath, exdir = td)
vaedir <- paste0(td, "/vae2_ov")
scvi <- scviR()
adm <- anndataR()
hpath <- cacheCiteseq5k10kPbmcs()
adata <- adm$read(hpath)
mod <- scvi$model$`_totalvi`$TOTALVI$load(vaedir, adata) #, use_gpu = FALSE)
mod

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ADT-based cluster labels for 7472 cells in OSCA chapter 12 analysis

Description

ADT-based cluster labels for 7472 cells in OSCA chapter 12 analysis

Usage

clusters.adt

Format

factor

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mRNA-based cluster labels for 7472 cells in OSCA chapter 12 analysis

Description

mRNA-based cluster labels for 7472 cells in OSCA chapter 12 analysis

Usage

clusters.rna

Format

factor

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app to explore diversity in RNA-subclusters within ADT clusters

Description

app to explore diversity in RNA-subclusters within ADT clusters

Usage

exploreSubcl(sce, inlist, adtcls)

Arguments

sce	a SingleCellExperiment with altExp with ADT quantification
inlist	list of SingleCellExperiments (SCEs) formed by scran::quickSubCluster
adtcls	vector of ADT cluster assignments

Value

shinyApp instance

Note

TSNE should already be available in 'altExp(sce)'; follow OSCA book 12.5.2. If using example, set 'ask=FALSE'.

Examples

sce <- getCh12Sce()
all.sce <- getCh12AllSce()
data(clusters.adt)
runApp(exploreSubcl(sce, all.sce, clusters.adt)) # trips up interactive pkgdown?)

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get list of cluster-specific SCE for 10k PBMC annotated as in OSCA book chapter 12

Description

get list of cluster-specific SCE for 10k PBMC annotated as in OSCA book chapter 12

Usage

getCh12AllSce()

Value

SimpleList of SingleCellExperiment instances

Note

This is a list of SingleCellExperiment instances with data on a total of 7472 cells from a 10x CITE-seq experiment. An altExp component in each list element includes antibody-derived tag (ADT) counts on 17 proteins. The data are acquired and processed as described in ch 12 of the OSCA book, circa February 2023. List elements correspond to mRNA-based sub-clusters of ADT-based clusters.

Examples

ch12_allsce <- getCh12AllSce()
vapply(ch12_allsce, ncol, numeric(1))

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get SCE for 10k PBMC annotated as in OSCA book chapter 12

Description

get SCE for 10k PBMC annotated as in OSCA book chapter 12

Usage

getCh12Sce(clear_cache = FALSE)

Arguments

clear_cache

logical(1) will delete relevant entries in available cache before continuing, defaults to FALSE

Value

SingleCellExperiment instance

Note

This is a SingleCellExperiment instance with data on 7472 cells from a 10x CITE-seq experiment. An altExp component includes antibody-derived tag (ADT) counts on 17 proteins. The data are acquired and processed as described in ch 12 of the OSCA book, circa February 2023. A metadata element (se.averaged) includes the result of averaging protein abundance estimates within ADT-based clusters, as is done to give rise to Figure 12.8 of the OSCA book.

Examples

ch12sce <- getCh12Sce()
ch12sce

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helper to get the processed anndata for CITE-seq PBMCs from scvi-tools tutorial

Description

helper to get the processed anndata for CITE-seq PBMCs from scvi-tools tutorial

Usage

getCiteseq5k10kPbmcs()

Value

python reference to anndata

Note

It may be advantageous to set 'options(timeout=3600)' or to allow an even greater time for internet downloads, if working at a relatively slow network connection.

Examples

getCiteseq5k10kPbmcs()

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helper to get the tutorial VAE for PBMCs from scvi-tools tutorial

Description

helper to get the tutorial VAE for PBMCs from scvi-tools tutorial

Usage

getCiteseqTutvae(use_gpu = FALSE)

Arguments

use_gpu

logical(1), defaulting to FALSE, passed to TOTALVI.load

Value

python reference to anndata

Note

March 2024 use_gpu ignored

Examples

getCiteseqTutvae()

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get an anndata reference to 5k10k protein after totalVI from tutorial

Description

get an anndata reference to 5k10k protein after totalVI from tutorial

Usage

getPro5k10kAdata()

Value

python reference to anndata

Note

It may be advantageous to set 'options(timeout=3600)' or to allow an even greater time for internet downloads, if working at a relatively slow network connection.

Examples

getPro5k10kAdata()

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get lmFit for heterogeneity across subclusters

Description

get lmFit for heterogeneity across subclusters

Usage

getSubclLM(inlist, clname)

Arguments

inlist	list of SingleCellExperiments (SCEs) formed by scran::quickSubCluster
clname	character(1) name of cluster SCE to assess

Value

limma::lmFit output

Note

It is assumed that 'logcounts' is an assay element, and that 'subcluster' is a colData element of each SCE in inlist

Examples

all.sce <- getCh12AllSce()
lm3 <- getSubclLM(all.sce, "3")
names(lm3)

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get lmFit F-stat based collection of n genes most varying in mean across subclusters

Description

get lmFit F-stat based collection of n genes most varying in mean across subclusters

Usage

getSubclusteringFeatures(inlist, clname, n = 20)

Arguments

inlist	list of SingleCellExperiments (SCEs) formed by scran::quickSubCluster
clname	character(1) name of cluster SCE to assess
n	numeric(1) number to preserve

Value

list with two elements, feat = rowData corresponding to variable genes, stats = topTable result

Note

Symbol will be taken from feat and placed in stats component if available

Examples

all.sce <- getCh12AllSce()
scl <- getSubclusteringFeatures(all.sce, "3", 10)
names(scl)

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get anndata reference to full totalVI processing of 5k10k data

Description

get anndata reference to full totalVI processing of 5k10k data

Usage

getTotalVI5k10kAdata()

Value

python reference to anndata

Examples

full <- getTotalVI5k10kAdata()
full

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get matrices of normalized quantifications from full totalVI 5k10k from tutorial

Description

get matrices of normalized quantifications from full totalVI 5k10k from tutorial

Usage

getTotalVINormalized5k10k()

Value

list of matrices

Examples

nmlist <- getTotalVINormalized5k10k()
vapply(nmlist, dim, numeric(2))

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basic interface to MuData

Description

basic interface to MuData

Usage

MuDataR()

Value

basiliskRun result with import from reticulate, typically a Module

Examples

md <- MuDataR()
md
head(names(md))

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helper to get text from python help utility – may need handling through basilisk

Description

helper to get text from python help utility – may need handling through basilisk

Usage

pyHelp2(object)

Arguments

object

a reference to a python module typically with class 'python.builtin.module'

Value

character vector of lines from python help result

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shiny app that helps access documentation on python-accessible components

Description

shiny app that helps access documentation on python-accessible components

Usage

scanpyHelper()

Value

shinyApp instance

---

basic interface

Description

basic interface

Usage

scanpyR()

Value

basiliskRun result with import from reticulate, typically a Module

Examples

sc <- scanpyR()
sc
sc$pp

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shiny app that helps access documentation on python-accessible components

Description

shiny app that helps access documentation on python-accessible components

Usage

scviHelper()

Value

shinyApp instance

---

basic interface

Description

basic interface

Usage

scviR()

Value

basiliskRun result with import from reticulate, typically a Module

Examples

scvi <- scviR()
scvi
scvi$model

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