Standardize the locus subset

After querying a subset of the full summary statistics, this function converts it into a standardized format that the rest of echolocatoR can work with.

Usage

standardize(
  query,
  subset_path = NULL,
  locus = NULL,
  colmap = construct_colmap(),
  compute_n = "ldsc",
  return_dt = TRUE,
  nThread = 1,
  verbose = TRUE
)

Arguments

query

data.frame of summary stat.

subset_path

Path where the query should be saved after standardization.

locus

Locus name.

colmap

Column mapping object created using construct_colmap.

compute_n

How to compute per-SNP sample size (new column "N").
If the column "N" is already present in dat, this column will be used to extract per-SNP sample sizes and the argument compute_n will be ignored.
If the column "N" is not present in dat, one of the following options can be supplied to compute_n:

0

N will not be computed.

>0

If any number >0 is provided, that value will be set as N for every row. **Note**: Computing N this way is incorrect and should be avoided if at all possible.

"sum"

N will be computed as: cases (N_CAS) + controls (N_CON), so long as both columns are present.

"ldsc"

N will be computed as effective sample size: Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)).

"giant"

N will be computed as effective sample size: Neff = 2 / (1/N_CAS + 1/N_CON).

"metal"

N will be computed as effective sample size: Neff = 4 / (1/N_CAS + 1/N_CON).

return_dt

Return data.table or path to saved data.table.

nThread

Number of threads to parallelise saving across.

verbose

Print messages.

Examples

query <- echodata::BST1
#### Screw up data
query$rsid <- query$SNP
query <- query[,-c("MAF","SNP")]
query[c(10,30,55),"Freq"] <- 0
query[c(12,22),"Freq"] <- NA 
subset_path <- file.path(tempdir(),"BST1.tsv")
colmap = construct_colmap(SNP="rsid")
query2 <- echodata::standardize(query = query,
                                subset_path = subset_path,
                                locus = "BST1",
                                colmap = colmap)
#> Standardizing summary statistics subset.
#> Standardizing main column names.
#> ++ Preparing A1,A1 cols
#> ++ Preparing MAF,Freq cols.
#> ++ Inferring MAF from frequency column.
#> ++ Removing SNPs with MAF== 0 | NULL | NA or >1.
#> ++ Preparing N_cases,N_controls cols.
#> ++ Preparing proportion_cases col.
#> ++ Calculating proportion_cases from N_cases and N_controls.
#> Preparing sample size column (N).
#> Computing effective sample size using the LDSC method:
#>  Neff = (N_CAS+N_CON) * (N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))[(N_CAS+N_CON)==max(N_CAS+N_CON)]))
#> + Mapping colnames from MungeSumstats ==> echolocatoR
#> + Imputing t-statistic from Effect and StdErr.
#> ++ Ensuring Effect,StdErr,P are numeric.
#> ++ Ensuring 1 SNP per row and per genomic coordinate.
#> ++ Removing extra whitespace
#> + Standardized query: 6,211 SNPs x 28 columns.
#> ++ Saving standardized query ==> /tmp/RtmpIRVypB/BST1.tsv