Souporcell for Genotype-Based Single-Cell Clustering

Analyzing single-cell RNA sequencing (scRNA-seq) data presents challenges in distinguishing genetic identities within mixed populations. Souporcell is a computational method designed to address this by clustering cells based on genotype without prior knowledge of sample composition. This approach enables researchers to deconvolve genetically distinct cell populations from pooled samples, making it valuable for studies involving patient-derived or multi-donor datasets.

By leveraging naturally occurring genetic variations, Souporcell enhances single-cell analysis accuracy and helps identify multiplets—instances where multiple cells are captured together. Its application improves our understanding of cellular heterogeneity while refining interpretations of gene expression patterns.

Foundations Of Genotype-Based Clustering

Genotype-based clustering relies on genetic variation to distinguish individual cells in single-cell RNA sequencing (scRNA-seq) data. Unlike clustering methods that depend solely on transcriptomic profiles, this approach uses single nucleotide polymorphisms (SNPs) to assign cells to their genetic origins. Computational tools like Souporcell analyze these sequence differences to separate cells from different individuals within a pooled sample, even when their gene expression patterns are highly similar. This is especially useful in mixed-donor experiments, where traditional clustering methods struggle to differentiate biologically distinct but transcriptionally overlapping cell types.

The effectiveness of genotype-based clustering depends on accurate SNP identification. Since scRNA-seq captures only a fraction of the transcriptome, the available genetic information is sparse and prone to technical noise. Algorithms must infer genotypes from incomplete data while accounting for sequencing errors and allelic dropout. Souporcell employs a probabilistic framework to estimate genotype likelihoods, allowing it to robustly assign cells to clusters even when coverage is low. This statistical approach improves genetic demultiplexing resolution, reducing misclassification risks from missing or ambiguous variant calls.

Beyond grouping cells by genotype, this method enhances multiplet detection—instances where multiple cells are encapsulated in the same droplet during sequencing. Traditional clustering techniques may misinterpret these mixed signals as intermediate cell states, leading to incorrect conclusions. By identifying inconsistencies in genotype assignments, Souporcell distinguishes true single-cell profiles from multiplets, refining downstream analyses. This capability is particularly relevant in large-scale studies where misclassified cells can skew results, underscoring genotype-based clustering’s role in ensuring data integrity.

Steps To Distinguish Individual Cells

Distinguishing individual cells in scRNA-seq data requires computational steps to accurately assign genetic identities. Souporcell follows a structured approach involving SNP identification, genotype likelihood estimation, and barcode assignment. These steps ensure correct classification of cells from different individuals within pooled samples, minimizing errors from technical noise or sequencing artifacts.

SNP Calling

The first step is identifying SNPs within sequencing data. Since scRNA-seq captures only a subset of the transcriptome, SNP detection occurs in expressed genome regions. Souporcell extracts variant sites by aligning sequencing reads to a reference genome and identifying nucleotide differences. Unlike whole-genome sequencing, where SNPs are detected with high confidence due to deep coverage, scRNA-seq presents challenges such as allelic dropout and sequencing errors. To mitigate these issues, Souporcell applies filtering criteria to exclude low-confidence variants, focusing on consistently detected SNPs. This ensures selected variants distinguish genetic identities rather than reflecting sequencing noise. Prioritizing SNPs with high minor allele frequencies further strengthens discriminatory power when clustering mixed samples.

Genotype Likelihood Estimation

Once SNPs are identified, the next step is estimating genotype probabilities at these variant sites. Since scRNA-seq data is sparse, Souporcell employs a probabilistic model to infer genotype likelihoods based on sequencing reads. This model accounts for sequencing depth, base quality scores, and expected allele distributions. By integrating these parameters, Souporcell calculates the likelihood of each possible genotype for every cell, allowing informed assignments even with low coverage. This approach reduces the impact of technical artifacts, such as allelic dropout, where one allele of a heterozygous SNP is missing. The probabilistic framework also distinguishes true genetic variation from sequencing errors, improving clustering accuracy.

Barcode Assignment

After estimating genotype likelihoods, Souporcell assigns cells to genetic clusters based on their barcoded sequencing reads. Each cell in an scRNA-seq experiment is tagged with a unique barcode during library preparation, enabling transcriptomic data tracking. Souporcell groups cells with similar genotype profiles into distinct clusters corresponding to different individuals in a pooled sample. The clustering process uses a variant of the k-means algorithm, iteratively refining cell assignments based on genotype similarity. To improve accuracy, Souporcell incorporates a contamination correction step that accounts for ambient RNA—free-floating transcripts that can introduce noise. By adjusting for this background signal, barcode assignments reflect true genetic identities rather than artifacts of sample preparation. This final step enables precise analysis of single-cell data from mixed-donor experiments.

Approaches For Deconvolving Mixed Samples

Deconvolving mixed samples in scRNA-seq requires distinguishing genetic identities within pooled datasets. Traditional transcriptomic clustering methods struggle with biologically similar yet genetically distinct cells, making genotype-based approaches like Souporcell valuable. By leveraging SNPs, this method untangles mixed-cell populations without prior knowledge of donor composition. The challenge lies in resolving individual contributions while accounting for technical artifacts such as allelic dropout, sequencing errors, and ambient RNA contamination.

One strategy involves optimizing SNP selection to enhance clustering resolution. Not all SNPs are equally informative—variants with high minor allele frequencies provide stronger discriminatory power, while rare or low-confidence mutations introduce noise. Filtering SNPs based on expression consistency across cells improves genotype assignment reliability. Statistical models that incorporate prior expectations about allele distributions refine these assignments, reducing misclassification. Souporcell’s probabilistic framework infers missing data points, improving clustering robustness, particularly in low-coverage scRNA-seq data.

Another critical aspect is addressing mixed-genotype signals caused by multiplets—instances where multiple cells are encapsulated in the same droplet. These can distort analyses by generating misleading intermediate profiles. Computational methods like Souporcell use genotype inconsistency patterns to detect and flag these occurrences, enabling researchers to exclude or correct problematic data points. Incorporating background RNA contamination correction further refines clustering, ensuring ambient RNA does not artificially inflate certain alleles’ presence.

Identifying Multiplets In Single-Cell Data

Multiplets—instances where multiple cells are captured in the same droplet during scRNA-seq—pose challenges in data interpretation. These events can produce misleading transcriptomic profiles that resemble hybrid states, confounding analyses. While multiplets typically account for 5–10% of captured droplets in platforms like 10x Genomics, their impact depends on sequencing depth and sample preparation. Identifying and filtering these occurrences ensures single-cell datasets reflect true biological heterogeneity rather than technical artifacts.

One effective way to detect multiplets is through genotype inconsistencies. In pooled experiments, multiplets display conflicting alleles at multiple SNP sites. Unlike single-cell profiles, which exhibit a diploid genotype corresponding to one individual, multiplets show mixed genotypic signals. Computational tools like Souporcell flag these discrepancies by clustering cells based on genetic similarity and identifying outliers with excessive heterozygosity. This approach helps distinguish genuine single-cell profiles from artificial mixtures, reducing misclassification.

Relevance In Studying Cellular Heterogeneity

Genotype-based single-cell clustering is crucial for understanding cellular heterogeneity, particularly in complex tissues and disease states. Traditional clustering methods relying solely on transcriptomic profiles struggle to differentiate cells with similar gene expression but distinct genetic origins. This limitation is pronounced in pooled samples from multiple donors, where transcriptional similarities mask genetic differences. By incorporating genomic information, Souporcell provides a refined approach to distinguishing genetically distinct cell populations, allowing deeper exploration of cellular composition.

This capability is especially useful in cancer research, where tumor heterogeneity complicates treatment strategies. Tumors often contain multiple subclones with distinct genetic mutations, and standard transcriptomic clustering may fail to separate them. By leveraging SNPs to identify genetically distinct clones, genotype-based clustering enables researchers to track tumor evolution, identify drug-resistant subclones, and tailor therapies. In stem cell research, distinguishing between donor-derived and host-derived cells is essential for understanding engraftment dynamics in transplantation studies. Resolving genetic differences at the single-cell level provides deeper insights into cellular diversity’s role in biological processes and disease mechanisms.

Handling Errors And Ambiguous Assignments

Despite its advantages, genotype-based clustering faces challenges in handling errors and ambiguous genotype assignments in scRNA-seq data. Low sequencing depth, allelic dropout, and technical noise introduce uncertainty in SNP detection and genotype calls, leading to misclassification. Addressing these issues requires computational strategies that account for missing data and sequencing artifacts while maintaining high resolution in distinguishing genetic identities.

One approach is using probabilistic models that estimate genotype likelihoods rather than making deterministic calls. Souporcell applies a statistical framework incorporating sequencing depth, base quality scores, and expected allele frequencies to refine genotype assignments. This reduces the impact of missing data by inferring the most likely genotype for each cell. Post-clustering quality control steps, such as filtering cells with excessive heterozygosity or inconsistent SNP profiles, minimize errors. Another strategy involves integrating external reference datasets, such as bulk RNA or whole-genome sequencing data, to validate and correct genotype calls. These safeguards enhance genotype-based clustering accuracy and ensure reliable single-cell dataset interpretation.