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ALRA (Adaptively-thresholded Low Rank Approximation) imputes dropout values in scRNA-seq data while preserving true biological zeros. It computes a rank-k approximation to the normalized expression matrix, then thresholds values based on the distribution of negative entries produced by the approximation.
No additional package installation is required. ALRA is implemented directly in SeuratWrappers and uses rsvd, which is already imported as a dependency.

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Reference

Linderman, G. C., Zhao, J., & Kluger, Y. (2018). Zero-preserving imputation of scRNA-seq data using low rank approximation. bioRxiv, 397588. https://doi.org/10.1101/397588 Source: KlugerLab/ALRA

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How it works

ALRA operates on a normalized expression matrix:
  1. Computes a rank-k approximation using randomized SVD (via rsvd).
  2. For each gene, determines a threshold from the quantile of the approximation’s negative values — a signal of the noise floor.
  3. Sets values below the threshold to zero, preserving true biological zeros.
  4. Rescales imputed values to match the original nonzero distribution.
The rank k can be chosen automatically using a statistical test on the singular value spacings, or specified manually.

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Key functions

  • RunALRA() — Runs ALRA imputation on a Seurat object or matrix. Creates an alra assay containing imputed values.
  • ALRAChooseKPlot() — Plots singular values, spacings, and p-values from the automated rank selection step. Useful for inspecting the chosen k before running imputation.

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Workflow

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Simple one-step usage

RunALRA will automatically select the rank k and perform imputation in a single call: 
library(Seurat)
library(SeuratData)
library(SeuratWrappers)

InstallData("pbmc3k")
data("pbmc3k")

# Preprocess
pbmc3k <- SCTransform(pbmc3k) |> RunPCA() |> RunUMAP(dims = 1:30)

# Run ALRA — creates an "alra" assay and sets it as the default
pbmc3k <- RunALRA(pbmc3k)

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Two-step workflow (inspect k first)

Use k.only = TRUE to run rank selection without performing imputation, inspect the diagnostic plot, then run imputation using the stored k:
1

Choose k

Run ALRA in rank-selection-only mode. The chosen k is stored in the object’s tool slot.
pbmc3k <- RunALRA(pbmc3k, k.only = TRUE)
2

Inspect the rank selection plot

Plot the singular value spectrum, spacings, and p-values to verify the chosen k is sensible.
ggouts <- ALRAChooseKPlot(pbmc3k)
do.call(gridExtra::grid.arrange, c(ggouts, nrow = 1))
3

Run ALRA with the chosen k

Call RunALRA() again. It detects the previously computed k from the tool slot and proceeds with imputation.
pbmc3k <- RunALRA(pbmc3k)

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Visualize original vs. imputed expression

# Normalize the original RNA assay for comparison
pbmc3k <- NormalizeData(pbmc3k, assay = "RNA")

features.plot <- c("CD3D", "MS4A1", "CD8A", "GZMK", "NCAM1", "FCGR3A")

DefaultAssay(pbmc3k) <- "RNA"
plot1 <- FeaturePlot(pbmc3k, features.plot, ncol = 2)

DefaultAssay(pbmc3k) <- "alra"
plot2 <- FeaturePlot(pbmc3k, features.plot, ncol = 2, cols = c("lightgrey", "red"))

CombinePlots(list(plot1, plot2), ncol = 1)

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Parameters

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RunALRA()

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k
integer | NULL
default:"NULL"
Rank of the approximation. When NULL, k is chosen automatically using the singular value spacing heuristic.
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q
integer
default:"10"
Number of additional power iterations in the randomized SVD. Higher values improve accuracy at the cost of speed.
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quantile.prob
numeric
default:"0.001"
Quantile probability used to determine the threshold for each gene from its negative approximation values.
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assay
character
default:"NULL"
Assay to use. Defaults to the default assay of the Seurat object.
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slot
character
default:"data"
Slot (layer) within the assay to use as input.
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setDefaultAssay
logical
default:"TRUE"
If TRUE, sets the new alra assay as the default assay after imputation.
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genes.use
character vector
default:"NULL"
Subset of genes to impute. Defaults to all genes in the object.
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K
integer
default:"100"
Number of singular values to compute during automated rank selection. Must be less than the smallest dimension of the expression matrix.
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noise.start
integer
default:"NULL"
Index from which singular values are considered noise during automated k selection. Defaults to K - 20.
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q.k
integer
default:"2"
Number of additional power iterations used when computing the SVD for automated k selection.
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k.only
logical
default:"FALSE"
If TRUE, only computes the optimal k and stores it in the object without performing imputation.

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ALRAChooseKPlot()

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object
Seurat
A Seurat object on which RunALRA(k.only = TRUE) has already been called.
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start
integer
default:"0"
Index to start plotting singular value spacings from. Defaults to floor(k / 2) when set to 0, which skips the large early spacings for a cleaner visualization.
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combine
logical
default:"TRUE"
If TRUE, returns the three plots combined into a single ggplot object using CombinePlots().

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