rsahmi (version 0.0.2)

blsd: 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
)

Value

A polars DataFrame

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.

See Also

https://github.com/sjdlabgroup/SAHMI

Examples

if (FALSE) {
# 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))
})
}