blsd: Barcode level signal denoising

In rsahmi: Single-Cell Analysis of Host-Microbiome Interactions

Barcode level signal denoising

Description

True taxa are detected on multiple barcodes and with a proprotional number of total and unique k-mer sequences across barcodes, measured as a significant Spearman correlation between the number of total and unique k-mers across barcodes. (padj < 0.05)

Usage

blsd(
  kmer,
  method = "spearman",
  ...,
  p.adjust = "BH",
  min_kmer_len = 3L,
  min_number = 3L
)

Arguments

kmer	kmer data returned by prep_dataset().
method	A character string indicating which correlation coefficient is to be used for the test. One of "pearson", "kendall", or "spearman", can be abbreviated.
...	Other arguments passed to cor.test.
p.adjust	Pvalue correction method, a character string. Can be abbreviated. Details see p.adjust.
min_kmer_len	An integer, the minimal number of kmer to filter taxa. SAHMI use 2.
min_number	An integer, the minimal number of cell per taxid. SAHMI use 4.

Value

A polars DataFrame

See Also

https://github.com/sjdlabgroup/SAHMI

Examples

## Not run: 
# 1. `sahmi_datasets` should be the output of all samples from 
      `prep_dataset()` 
# 2. `real_taxids_slsd` should be the output of `slsd()`
umi_list <- lapply(sahmi_datasets, function(dataset) {
    # Barcode level signal denoising (barcode k-mer correlation test)
    blsd <- blsd(dataset$kmer)
    real_taxids <- blsd$filter(pl$col("padj")$lt(0.05))$get_column("taxid")
    # only keep taxids pass Sample level signal denoising
    real_taxids <- real_taxids$filter(real_taxids$is_in(real_taxids_slsd))
    # remove contaminants
    real_taxids <- real_taxids$filter(
        real_taxids$is_in(attr(truly_microbe, "truly"))
    )
    # filter UMI data
    dataset$umi$filter(pl$col("taxid")$is_in(real_taxids))
})

## End(Not run)

rsahmi documentation built on April 4, 2025, 1:46 a.m.