The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Getting started with SNPkit

Overview

SNPkit provides a set of S4 tools for reading, organising, summarising, filtering and exporting single nucleotide polymorphism (SNP) genotype data. The central data structure is SNPDataLong, which bundles a genotype matrix (snpStats::SnpMatrix), a marker map (data.frame), and metadata about the data source.

This vignette walks through the typical steps:

Building an SNPDataLong object from a toy genotype matrix.
Inspecting the object with summary().
Applying quality-control filters with qcSNPs().
Exporting the cleaned data for use with external tools.

All file output uses tempdir() so the example does not write to the user’s home filespace.

Building an `SNPDataLong` object

We simulate a tiny dataset with 10 individuals and 10 SNPs.

set.seed(123)

raw_mat <- matrix(
  as.raw(sample(1:3, 100, replace = TRUE)),
  nrow = 10, ncol = 10
)
rownames(raw_mat) <- paste0("ind", 1:10)
colnames(raw_mat) <- paste0("snp", 1:10)

geno <- new("SnpMatrix", raw_mat)

map <- data.frame(
  Name       = colnames(geno),
  Chromosome = rep(1, 10),
  Position   = seq_len(10),
  stringsAsFactors = FALSE
)

snp_data <- new(
  "SNPDataLong",
  geno      = geno,
  map       = map,
  path      = tempfile(),
  xref_path = "chip1"
)

snp_data
#> An object of class "SNPDataLong"
#> Slot "geno":
#> A SnpMatrix with  10 rows and  10 columns
#> Row names:  ind1 ... ind10 
#> Col names:  snp1 ... snp10 
#> 
#> Slot "map":
#>     Name Chromosome Position
#> 1   snp1          1        1
#> 2   snp2          1        2
#> 3   snp3          1        3
#> 4   snp4          1        4
#> 5   snp5          1        5
#> 6   snp6          1        6
#> 7   snp7          1        7
#> 8   snp8          1        8
#> 9   snp9          1        9
#> 10 snp10          1       10
#> 
#> Slot "path":
#> [1] "/var/folders/gg/p6chdt1j0vsfdxppxd7lkvm00000gn/T//RtmpUDYrQc/filef0ca2f4aab22"
#> 
#> Slot "xref_path":
#> [1] "chip1"

Inspecting the object

The summary() method returns a summary.SNPDataLong object that can be printed for a human-readable description or queried programmatically.

s <- summary(snp_data)
s$n_individuals
#> [1] 10
s$n_snps
#> [1] 10
s$prop_missing
#> [1] 0
print(s)
#> Summary of SNPDataLong object
#> -----------------------------
#> Individuals : 10 
#> SNPs        : 10 
#> 
#> Missing data (NA):
#>  - Total     : 0 of 100 
#>  - Proportion: 0 %
#> 
#> Distribution of SNPs by chromosome:
#> chr_info_clean
#>  1 
#> 10 
#> 
#> SNPs with missing data by chromosome:
#> 1 
#> 0

Quality control

qcSNPs() applies a flexible set of filters. The action argument controls whether the function returns a report of removed SNPs ("report"), a filtered SNPDataLong ("filter"), or both.

filtered <- qcSNPs(
  snp_data,
  min_snp_cr   = 0.8,
  min_maf      = 0.05,
  snp_mono     = TRUE,
  no_position  = TRUE,
  action       = "filter"
)
#> 
#> +===============================+
#> |    Quality Control on SNPs    |
#> +===============================+
#> Initial number of SNPs: 10
#> Applying quality control filters...
#>   - Call rate filter: 0 SNP(s) removed; 10 retained.
#>   - MAF filter: 0 SNP(s) removed; 10 retained.
#>   - No-position filter: 0 SNP(s) removed; 10 retained.
#>   - Monomorphic filter: 0 SNP(s) removed; 10 retained.
filtered
#> An object of class "SNPDataLong"
#> Slot "geno":
#> A SnpMatrix with  10 rows and  10 columns
#> Row names:  ind1 ... ind10 
#> Col names:  snp1 ... snp10 
#> 
#> Slot "map":
#>     Name Chromosome Position
#> 1   snp1          1        1
#> 2   snp2          1        2
#> 3   snp3          1        3
#> 4   snp4          1        4
#> 5   snp5          1        5
#> 6   snp6          1        6
#> 7   snp7          1        7
#> 8   snp8          1        8
#> 9   snp9          1        9
#> 10 snp10          1       10
#> 
#> Slot "path":
#> [1] "/var/folders/gg/p6chdt1j0vsfdxppxd7lkvm00000gn/T//RtmpUDYrQc/filef0ca2f4aab22"
#> 
#> Slot "xref_path":
#> [1] "chip1"

Exporting

savePlink() and saveFImpute() write files to a user-supplied directory. For this vignette we use tempdir().

out_dir <- file.path(tempdir(), "snpkit_demo")
dir.create(out_dir, showWarnings = FALSE)

savePlink(
  filtered,
  path       = out_dir,
  name       = "demo",
  run_plink  = FALSE,
  chunk_size = 5
)
#> 
#> +====================================+
#> |    Saving Files in Plink Format    |
#> +====================================+
#> Writing .ped file in chunks...
#>  Wrote individuals 1 to 5
#>  Wrote individuals 6 to 10
#> .ped file written: /var/folders/gg/p6chdt1j0vsfdxppxd7lkvm00000gn/T//RtmpUDYrQc/snpkit_demo/demo.ped
#> Writing .map file...
#> .map file written: /var/folders/gg/p6chdt1j0vsfdxppxd7lkvm00000gn/T//RtmpUDYrQc/snpkit_demo/demo.map
#> Skipping PLINK binary conversion as requested.
#> All done.
list.files(out_dir, pattern = "demo")
#> [1] "demo.map" "demo.ped"

Where to go next

See ?qcSNPs, ?savePlink, ?saveFImpute, and ?runAnticlusteringPCA for details on the individual functions. Functions that wrap external software (FImpute, PLINK, ADMIXTURE) require the corresponding binary to be installed on the system.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.