SanityR

Bayesian Inference and Distance Calculation for Single-Cell RNA-seq Data

SanityR provides an R interface to the Sanity model, described in Breda et al. (2021), Nature Biotechnology for single-cell gene expression analysis. It offers tools for:

• Bayesian estimation of log normalized counts and their uncertainty.
• Computing statistically sound distances between cells while accounting for uncertainty.
• Integrates with SingleCellExperiment to be used as part of the Bioconductor Single Cell Workflow

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Installation

Bioconductor installation

SanityR is available on Bioconductor.

To install this package, start R (version “4.5”) and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
    
BiocManager::install("SanityR")

GitHub installation

To install the latest version from GitHub, you can use the remotes package:

remotes::install_github("TeoSakel/SanityR")

Example

library(SanityR)

# Simulate data
sce <- simulate_branched_random_walk(N_path = 10, length_path = 10, N_gene = 200)

# Run Sanity estimation
sce <- Sanity(sce)

# Compute distances
dist <- calculateSanityDistance(sce)

# Perform clustering or visualization
plot(hclust(dist))

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Main Functions

Sanity()

sce <- Sanity(sce)
logcounts(sce)

Log-normalizes the UMI counts and estimates error bars for each value using a hierarchical Bayesian Model:

$$ \begin{aligned} n_{gc} &\sim \text{Poisson}(\lambda_c \alpha_g e^{\delta_{gc}}) \ \lambda_c &\sim \text{Uniform}(0, \infty) \ \alpha_g &\sim \text{Gamma}(a, b) \ \delta_{gc} &\sim \text{Normal}(0, v_g) \ \end{aligned} $$

where:

• $n_{gc}$ is the observed UMI count for gene $g$ in cell $c$.
• $\lambda_c$ is the cell-specific transcription rate.
• $\alpha_g$ is the mean activity quotient of the gene $g$.
• $a$ and $b$ are prior hyperparameters for the Gamma distribution.
• $\delta_{gc}$ is the log fold-change of activity for gene $g$ in cell $c$ versus the mean.
• $v_g$ is the prior variance of the log fold-change for gene $g$.

Log-normalized counts in this model are calculated as:

$$y_{gc} = \langle \log{\alpha_g} + \delta_{gc} \rangle$$

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calculateSanityDistance()

dist <- calculateSanityDistance(sce)

Computes the expected Euclidean distance between cells, accounting for measurement uncertainty:

$$ \begin{aligned} d_{cc'} &= \sqrt{\sum_g \langle \delta_{gc} - \delta_{gc'} \rangle^2} \ \delta_{gc} - \delta_{gc'} &\sim \text{Normal}(\Delta_g, \eta_g) \ \Delta_g &\sim \text{Normal}(0, \alpha v_g) \ \end{aligned} $$

where:

• $d$ is the distance between cells $c$ and $c'$
• $\delta_{gc}$ is the log fold-change of activity for gene $g$ in cell $c$ computed by Sanity.
• $\eta_g = \epsilon_{gc} + \epsilon_{gc'}$ is the sum of posterior variances of $\delta_{gc}$.
• $\Delta_g$ is the “true” distance along the dimension of gene $g$.
• $\alpha$ is a hyperparameter that controls the correlation between cells (0 = fully correlated, 2 = fully independent).

The function requires Sanity() to have been run before to estimate $\delta_{gc}$ and returns a dist object suitable for clustering or embedding.

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Simulation Functions

Provides two functions to generate synthetic datasets for benchmarking using the generative process described in the original paper:

• simulate_independent_cells(): Simulates cells with independent gene expression profiles.
• simulate_branched_random_walk(): Simulates cells with pseudo-temporal trajectories forming a tree.

sce_indep <- simulate_independent_cells(N_cell = 100, N_gene = 50)
sce_branch <- simulate_branched_random_walk(N_path = 20, length_path = 5, N_gene = 50)

Both functions return a SingleCellExperiment object.

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Reference

Breda, J., Zavolan, M., & van Nimwegen, E. Bayesian inference of gene expression states from single-cell RNA-seq data. Nature Biotechnology, 39, 1008–1016 (2021). doi:10.1038/s41587-021-00875-x

Amezquita, R.A., Lun, A.T.L., Becht, E. et al.  Orchestrating single-cell analysis with Bioconductor. Nature Methods 17, 137–145 (2020). doi:10.1038/s41592-019-0654-x