blsd function

Barcode level signal denoising

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)

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.

Returns

A polars DataFrame

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 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)

See Also

https://github.com/sjdlabgroup/SAHMI