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Alevin is a tool within the Salmon ecosystem for quantifying gene abundances from droplet-based scRNA-seq data. ReadAlevin() wraps tximport to load alevin output files directly into a Seurat-compatible count matrix, with optional support for genomic range metadata via tximeta.
Citation: Srivastava et al. (2019) Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biology, 20(1):65. doi: 10.1186/s13059-019-1670-ySource: COMBINE-lab/salmon (GitHub)

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Installation

# Required
BiocManager::install(c('tximport', 'fishpond'))

# Optional: for genomic range metadata
BiocManager::install('tximeta')

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Key Function

ReadAlevin() — Reads the quants_mat.gz file produced by alevin and returns a Seurat object with the count matrix populated.

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How It Works

ReadAlevin() calls tximport (or tximeta when getMeta = TRUE) to parse alevin’s compressed output into a gene-by-cell count matrix. The matrix is then passed to CreateSeuratObject. Optionally, meanAndVariance = TRUE stores bootstrapped mean estimates in the counts slot and variance estimates in the data slot.

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ReadAlevin Parameters

​
file
character
required
Path to the quants_mat.gz file within the alevin output directory. This file is located at <alevin_out>/alevin/quants_mat.gz.
​
getMeta
logical
default:"FALSE"
If TRUE, uses tximeta to programmatically fetch genomic range information for each gene. Range data is stored in the meta.features slot under columns chr, start, and end. Requires the tximeta package.
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meanAndVariance
logical
default:"FALSE"
If TRUE, retrieves bootstrapped mean and variance estimates from the alevin output instead of point estimates. Mean counts are stored in the counts slot; variance estimates are stored in the data slot.
​
...
extra arguments
Additional arguments passed through to tximport. For example, alevinArgs = list(filterBarcodes = TRUE) activates alevin’s internal barcode filtering.

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Workflow

1

Run alevin

Run alevin externally to produce quantification output. The output directory will contain an alevin/ subdirectory with quants_mat.gz.
salmon alevin \
  -l ISR \
  --index /path/to/index \
  -1 read1.fastq.gz \
  -2 read2.fastq.gz \
  --chromiumV3 \
  -p 8 \
  --tgMap txp2gene.tsv \
  -o alevin_out
2

Import counts into Seurat

library(SeuratWrappers)
library(tximport)

pbmc <- ReadAlevin("~/alevin_out/alevin/quants_mat.gz")
ReadAlevin returns a fully constructed Seurat object ready for downstream processing.
3

Standard Seurat analysis

The returned object can be used directly with the standard Seurat workflow:
pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc)
pbmc <- ScaleData(pbmc)
pbmc <- RunPCA(pbmc)
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc)
pbmc <- RunUMAP(pbmc, dims = 1:10)
DimPlot(pbmc)

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Advanced Options

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Barcode filtering

Pass alevin-specific arguments through ... to tximport: 
pbmc <- ReadAlevin(
  "~/alevin_out/alevin/quants_mat.gz",
  alevinArgs = list(filterBarcodes = TRUE)
)

​
Mean and variance estimates

For bootstrap-based uncertainty quantification, retrieve mean and variance estimates: 
pbmc <- ReadAlevin(
  "~/alevin_out/alevin/quants_mat.gz",
  meanAndVariance = TRUE
)
# counts slot contains bootstrapped mean expression
# data slot contains bootstrapped variance

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Genomic range metadata

Attach chromosomal position information to the feature metadata: 
pbmc <- ReadAlevin(
  "~/alevin_out/alevin/quants_mat.gz",
  getMeta = TRUE
)
# Accesses chr, start, end from the RNA assay meta.features
head(pbmc[["RNA"]][[]])
getMeta = TRUE requires the tximeta Bioconductor package. If tximeta is not installed, ReadAlevin will stop with an informative error.

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