Skip to main content

Documentation Index

Fetch the complete documentation index at: https://mintlify.com/satijalab/seurat-wrappers/llms.txt

Use this file to discover all available pages before exploring further.

ReadAlevin() reads the output of the alevin salmon quantification tool and creates a Seurat object with the resulting count matrix.

​
Syntax

ReadAlevin(
  file,
  getMeta = FALSE,
  meanAndVariance = FALSE,
  ...
)

​
Parameters

​
file
character
required
Path to the alevin output directory or quant.sf file.
​
getMeta
logical
default:"FALSE"
If TRUE, uses tximeta instead of tximport to load data with genomic range metadata (requires tximeta to be installed). When successful, gene-level metadata (chromosome, start, end) is added to the assay.
​
meanAndVariance
logical
default:"FALSE"
If TRUE, loads the alevin mean and variance matrices instead of counts. The mean matrix becomes the counts slot and the variance matrix becomes the data slot.
​
...
any
Additional parameters passed to tximport::tximport() or tximeta::tximeta().

​
Returns

A Seurat object created from the alevin count matrix. When getMeta = TRUE and genomic ranges are available, the assay includes chr, start, and end feature-level metadata.

​
Prerequisites

BiocManager::install(c("tximport", "fishpond"))
# Optional for metadata:
BiocManager::install("tximeta")

​
Examples

library(SeuratWrappers)
BiocManager::install(c("tximport", "fishpond"))

# Basic import
object <- ReadAlevin(file = "alevin/output/dir")

# Import with genomic metadata
object <- ReadAlevin(file = "alevin/output/dir", getMeta = TRUE)

# Access genomic ranges
head(object[["RNA"]][[c("chr", "start", "end")]])

# Continue with standard Seurat workflow
object <- NormalizeData(object)
object <- FindVariableFeatures(object)

​
See Also

Build docs developers (and LLMs) love

Get started for freeTalk to us