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Compute replicate-aware barcode statistics

Source: R/gene_level_statistics.R
compute_barcode_statistics.Rd

Tests, for each barcode, the comparison given by the contrast with the edgeR quasi-likelihood F-test (glmQLFit, glmQLFTest). Unlike compute_metrics, which averages the replicates before computing the log2 fold change, this uses the variability between replicates: barcodes that are noisy across replicates get a smaller score. The score is reported as a signed Z-score (sign(Log2FC) * qnorm(1 - PValue / 2)), so that the table can be used with find_gene_zscore_hit and find_discordant_barcodes.

Usage

compute_barcode_statistics(screenR_Object, matrix_model, contrast)

Arguments

screenR_Object

The ScreenR object obtained using create_screenr_object, normalized with normalize_data. The model uses its raw counts with its effective library sizes as offsets, the same normalization as the plots and the data table (see create_edger_obj).

matrix_model

The design matrix of the linear model, for example created with create_design_matrix (or with model.matrix). Rows must be named with sample identifiers in object order (see create_design_matrix); mismatched names or order are errors. Named contrast coefficients are aligned to design columns.

contrast

The comparison to test: the name or index of a column of matrix_model, a numeric contrast vector (for example created with create_contrast) or a one-column matrix created with makeContrasts.

Value

A tibble with one row for each barcode and the columns Barcode, Gene, Log2FC, logCPM, F, PValue, FDR (Benjamini-Hochberg, over all the barcodes) and Zscore.

See also

compute_metrics, find_gene_zscore_hit, create_design_matrix, create_contrast

Examples

object <- get0("object", envir = asNamespace("ScreenR"))
design <- create_design_matrix(object)
contrast <- create_contrast(design,
    treatment = "Treated",
    control = "Control"
)
statistics <- compute_barcode_statistics(object, design, contrast)
head(statistics)
#> # A tibble: 6 × 8
#>   Barcode Gene      Log2FC logCPM      F  PValue   FDR Zscore
#>   <chr>   <chr>      <dbl>  <dbl>  <dbl>   <dbl> <dbl>  <dbl>
#> 1 10208   Gene_380  0.0346   8.56 0.0756 0.785   0.989  0.273
#> 2 10502   Gene_380 -0.151    4.97 0.129  0.722   0.987 -0.356
#> 3 10796   Gene_380 -0.104    6.61 0.0894 0.767   0.987 -0.297
#> 4 11089   Gene_380 -0.0515   8.44 0.149  0.702   0.983 -0.383
#> 5 11382   Gene_380 -0.621    5.62 2.63   0.114   0.934 -1.58 
#> 6 11675   Gene_380 -0.275    9.66 8.72   0.00573 0.790 -2.76