CreateMarvelObject.10x: Create Marvel object for droplet-based RNA-sequencing data

In MARVEL: Revealing Splicing Dynamics at Single-Cell Resolution

Create Marvel object for droplet-based RNA-sequencing data

Description

Creates an S3 object named Marvel for downstream analysis, specifically for droplet-based RNA-sequencing data.

Usage

CreateMarvelObject.10x(
  gene.norm.matrix = NULL,
  gene.norm.pheno = NULL,
  gene.norm.feature = NULL,
  gene.count.matrix = NULL,
  gene.count.pheno = NULL,
  gene.count.feature = NULL,
  sj.count.matrix = NULL,
  sj.count.pheno = NULL,
  sj.count.feature = NULL,
  pca = NULL,
  gtf = NULL
)

Arguments

gene.norm.matrix	Sparse matrix. UMI-collapsed, normalised, non-log2-transformed gene expression matrix.
gene.norm.pheno	Data frame. Sample metadata for annotating gene.norm.matrix columns with cell IDs.
gene.norm.feature	Data frame. Gene metadata for annotating gene.norm.matrix rows with gene names.
gene.count.matrix	Sparse matrix. UMI-collapsed, non-normalised (raw counts), non-log2-transformed gene expression matrix.
gene.count.pheno	Data frame. Sample metadata for annotating gene.count.matrix columsn with cell IDs.
gene.count.feature	Data frame. Gene metadata for annotating gene.count.matrix rows with gene names.
sj.count.matrix	Sparse matrix. UMI-collapsed, non-normalised (raw counts), non-log2-transformed splice junction expression matrix.
sj.count.pheno	Data frame. Sample metadata for annotating sj.count.matrix columsn with cell IDs.
sj.count.feature	Data frame. Splice junction metadata for annotating sj.count.matrix rows with splice junction coordinates.
pca	Data frame. Coordinates of PCA/tSNE/UMAP.
gtf	Data frame. GTF used in cellranger. Will be used for annotating splice junctions downstream.

Value

An object of class S3.

Examples

# Retrieve, observe format of pre-saved input files
marvel.demo.10x.raw <- readRDS(system.file("extdata/data",
                               "marvel.demo.10x.raw.rds",
                               package="MARVEL")
                               )
# Gene expression (Normalised)
    # Matrix
    df.gene.norm <- marvel.demo.10x.raw$gene.norm.matrix
    df.gene.norm[1:5, 1:5]

    # phenoData
    df.gene.norm.pheno <- marvel.demo.10x.raw$sample.metadata
    head(df.gene.norm.pheno)

    # featureData
    df.gene.norm.feature <- data.frame("gene_short_name"=rownames(df.gene.norm),
                                       stringsAsFactors=FALSE
                                       )
    head(df.gene.norm.feature)

# Gene expression (Counts)
    # Matrix
    df.gene.count <- marvel.demo.10x.raw$gene.count.matrix
    df.gene.count[1:5, 1:5]

    # phenoData
    df.gene.count.pheno <- data.frame("cell.id"=colnames(df.gene.count),
                                       stringsAsFactors=FALSE
                                       )
    head(df.gene.count.pheno)

    # featureData
    df.gene.count.feature <- data.frame("gene_short_name"=rownames(df.gene.count),
                                       stringsAsFactors=FALSE
                                       )
    head(df.gene.count.feature)

# SJ (Counts)
    # Matrix
    df.sj.count <- marvel.demo.10x.raw$sj.count.matrix
    df.sj.count[1:5, 1:5]

    # phenoData
    df.sj.count.pheno <- data.frame("cell.id"=colnames(df.sj.count),
                                     stringsAsFactors=FALSE
                                     )
    head(df.sj.count.pheno)

    # featureData
    df.sj.count.feature <- data.frame("coord.intron"=rownames(df.sj.count),
                                       stringsAsFactors=FALSE
                                       )
    head(df.sj.count.feature)

# tSNE coordinates
df.coord <- marvel.demo.10x.raw$pca
head(df.coord)

# GTF
gtf <- marvel.demo.10x.raw$gtf
head(gtf)

# Create MARVEL object
marvel.demo.10x <- CreateMarvelObject.10x(gene.norm.matrix=df.gene.norm,
                     gene.norm.pheno=df.gene.norm.pheno,
                     gene.norm.feature=df.gene.norm.feature,
                     gene.count.matrix=df.gene.count,
                     gene.count.pheno=df.gene.count.pheno,
                     gene.count.feature=df.gene.count.feature,
                     sj.count.matrix=df.sj.count,
                     sj.count.pheno=df.sj.count.pheno,
                     sj.count.feature=df.sj.count.feature,
                     pca=df.coord,
                     gtf=gtf
                     )

MARVEL documentation built on Oct. 31, 2022, 5:07 p.m.