bioconductor-demuxmix

name	bioconductor-demuxmix
description	A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

demuxmix

Workflows

Standard Workflow

Demultiplex pooled single-cell samples labeled with multiple hashtag oligonucleotides (HTOs) using regression or naive mixture models.

library(demuxmix)

# Simulate a small example dataset
set.seed(2642)
class <- rbind(
  c(rep(TRUE, 220), rep(FALSE, 200)),
  c(rep(FALSE, 200), rep(TRUE, 220))
)
simdata <- dmmSimulateHto(class)
hto <- simdata$hto
rna <- simdata$rna

# Run demuxmix and summarize results
dmm <- demuxmix(hto, rna = rna)
summary(dmm)

# Classify droplets and plot QC histogram
classes <- dmmClassify(dmm)
table(classes$HTO)
plotDmmHistogram(dmm)

Input/Output Note: Inputs an HTO count matrix and a vector of detected genes per droplet; outputs a classified data frame of singlets, multiplets, negatives, and uncertain droplets.

Pooling Non Labeled With Labeled Cells

Demultiplex a mixture of labeled cells and non-labeled cells (e.g., rare cells) using a single HTO channel.

library(demuxmix)
data(csf)

# Filter out low-quality droplets
csf <- csf[csf$NumGenes >= 200, ]
hto <- t(matrix(csf$HTO, dimnames = list(rownames(csf), "HTO")))

# Fit regression mixture model
dmm <- demuxmix(hto, rna = csf$NumGenes)
summary(dmm)
dmmOverlap(dmm)

# Generate QC plots
plotDmmHistogram(dmm)
plotDmmScatter(dmm)

# Classify droplets into labeled (positive) and non-labeled (negative)
class <- dmmClassify(dmm)
table(class$HTO)

Input/Output Note: Inputs a single-channel HTO matrix and a vector of detected genes; outputs classifications separating labeled cells (positive) from non-labeled cells (negative).

When to Use

• To demultiplex pooled single-cell RNA-seq samples labeled with hashtag oligonucleotides (HTOs) using negative binomial regression mixture models via demuxmix().
• To leverage the positive association between the number of detected genes per droplet and HTO counts to improve classification accuracy.
• To identify multiplets and estimate false discovery rates (FDR) using summary().
• To demultiplex a mixture of labeled cells (e.g., PBMCs) and non-labeled cells (e.g., rare CSF cells) using a single HTO channel.

When NOT to Use

• For demultiplexing genetically diverse pooled samples without HTOs; use genetic demultiplexing tools like freemuxlet because demuxmix requires HTO count data.
• For general cell clustering or cell-type annotation; use Seurat or scran because demuxmix is strictly for sample demultiplexing.
• For processing raw FASTQ files into count matrices; use CellRanger or Alevin because demuxmix requires pre-computed HTO and RNA count matrices.

Data Requirements

• Input format: A raw HTO count matrix (HTOs as rows, droplets/cells as columns) and a vector of the number of detected genes per droplet (RNA).
• Structure: Droplets must be pre-filtered to remove empty and low-quality droplets (e.g., using emptyDrops).
• Normalization: Raw, untransformed integer counts are required for both HTO and gene counts.

Key Parameters

• rna (NULL): A numeric vector containing the number of detected genes per droplet.
• model ("regression"): Character string specifying the mixture model type ("regression" or "naive").
• pAcpt (0.9 or 0.729 depending on model): The minimum posterior probability required to classify a droplet; droplets below this are marked as "uncertain".

Best Practices

• Remove empty and low-quality droplets using emptyDrops before running demuxmix().
• Use the default regression mixture model (model = "regression") to leverage the positive correlation between HTO counts and detected genes.
• Verify the model fit by plotting density histograms overlaid with the mixture probability mass function using plotDmmHistogram().
• Check the overlap between positive and negative components using dmmOverlap(); an overlap of less than 0.03 indicates excellent separation.

Common Pitfalls

• Too many "uncertain" classifications: Occurs when HTO quality is moderate or background is high. Fix: Lower the acceptance probability pAcpt (e.g., pAcpt(dmm) <- 0.7 or 0) to force classification, keeping in mind this may increase the error rate.
• Flat red component in histogram: Occurs when sequencing depth is very high or many samples are pooled. Fix: Use coord_cartesian() to zoom into the critical overlap region of the histogram.
• Poor regression fit in negative droplets: Occurs when background HTO staining is extremely low. Fix: The package handles this, but you can switch to model = "naive" if the association between HTO and gene counts is absent.

Alternatives

• hashedDrops (from DropletUtils): For HTO demultiplexing without explicitly modeling the gene-HTO correlation.
• Seurat (HTODemux): For demultiplexing using a k-medoids clustering approach on normalized HTO counts.
• freemuxlet: For genetic demultiplexing of pooled samples from genetically unrelated donors.

Citations

• Stoeckius et al. (2018), Genome Biology.

References

• Homepage: bioconductor.org/packages/demuxmix
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/demuxmix/inst/doc/demuxmix.html