Filter

The key functions for filtering are filter() (dplyr-compatible) and filter_immundata(), which are the same function with a slightly different arguments due to the necessity to comply with dplyr interface. Repertoires are reaggregated automatically. Functions filter_receptors() and filter_barcodes() are used for conveniently filter receptor and barcode identifiers, correspondingly.

To filter data, you simply pass predicates like in dplyr. Optionally, you can pass seq_options that allow you to filter by exact sequence match, regex pattern, or sequence distances using hamming or edit/levenshtein distances. You can pass multiple patterns via patterns = c("pattern_1", "pattern_2").

Run this code before running examples below:

R

library(immundata)

inp_files <- paste0(system.file("extdata/single_cell", "", package = "immundata"), "/*.csv.gz")
md_file <- system.file("extdata/single_cell", "metadata.tsv", package = "immundata")
md_table <- read_metadata(md_file)
cells_file <- system.file("extdata/single_cell", "cells.tsv.gz", package = "immundata")
cells <- readr::read_tsv(cells_file)

schema <- make_receptor_schema(features = c("cdr3", "v_call"), chains = c("TRB"))

idata <- read_repertoires(
    path              = inp_files, 
    schema            = schema, 
    metadata.         = md_table, 
    barcode_col       = "barcode", 
    locus_col         = "locus", 
    umi_col           = "umis", 
    preprocess        = make_default_preprocessing("10x"), 
    repertoire_schema = "Tissue")

Filter by any annotation

R

idata |> filter(v_call == "TRBV2")

idata |> filter(Tissue == "Blood")

Chain filters together

R

# this expression:
idata |> filter(v_call == "TRBV2", imd_proportion >= 0.0002)

# is the same as this one:
idata |> filter(v_call == "TRBV2") |> filter(imd_proportion >= 0.0002)

Filter by sequence distance

R

idata |> filter(seq_options = make_seq_options(patterns = "CASSELAGYRGEQYF", query_col = "cdr3", method = "lev", max_dist = 3))

idata |> filter(v_call == "TRBV2", seq_options = make_seq_options(patterns = "CASSELAGYRGEQYF", query_col = "cdr3", method = "lev", max_dist = 3))

Filter by receptor identifiers

R

idata |> filter_receptors(c(1,2,3))

Filter by barcodes

R

target_bc <- cells$barcode[1:3]
idata |> filter_barcodes(target_bc)

Filter by repertoire

R

```r idata |> filter(imd_repertoire_id == 1)

idata |> filter(Tissue %in% c("Blood", "Tumor"))