Call the various read/ validate methods for input data surrounding an experiment

Usage

validate_inputs(
  samples_metadata,
  assay_files,
  contrasts_file = NULL,
  features_metadata = NULL,
  sample_id_col = "sample",
  assay_names = NULL,
  differential_results = NULL,
  feature_id_col = "gene_id",
  pval_column = "pval_column",
  qval_column = "qval_column",
  fc_column = "log2FoldChange",
  fold_change_scale = "auto",
  unlog_foldchanges = NULL
)

Arguments

samples_metadata: Sample metadata data frame
assay_files: List of assay matrices
contrasts_file: Contrasts definition file
features_metadata: Feature metadata data frame
sample_id_col: Column of sample metadata used for identifiers
assay_names: Optional comma-separated list of assay names
differential_results: Optional list of differential stats files
feature_id_col: Column of feature metadata used for identifiers
pval_column: P value column if differential stats files specified
qval_column: Q value column if differential stats files specified
fc_column: Fold change column if differential stats files specified
fold_change_scale: Scale of the values in fc_column: one of "auto" (default, infer and validate from the column name and data distribution), "log2" or "linear". Each differential results file is validated independently. See resolve_foldchange_scale.
unlog_foldchanges: Deprecated, use fold_change_scale instead. Boolean- should fold changes in stats files be unlogged?

Value

output A named list with feature/ observation components

Examples

sample_metadata_file <- tempfile(fileext = ".csv")
write.csv(
  data.frame(sample = paste0("s", 1:4), condition = rep(c("treated", "control"), each = 2)),
  sample_metadata_file, row.names = FALSE
)
mat <- matrix(1:12, nrow = 3, dimnames = list(paste0("gene", 1:3), paste0("s", 1:4)))
matrix_file <- tempfile(fileext = ".csv")
write.csv(
  data.frame(gene_id = rownames(mat), mat, check.names = FALSE),
  matrix_file, row.names = FALSE
)
validate_inputs(
  samples_metadata = sample_metadata_file,
  assay_files = matrix_file,
  sample_id_col = "sample"
)
#> Reading sample sheet at /tmp/RtmpBPfjqs/file29c83c4bb6ff.csv with ID col sample
#> Reading assay matrix /tmp/RtmpBPfjqs/file29c8aa18907.csv and validating against samples and features (if supplied)
#> ...  /tmp/RtmpBPfjqs/file29c8aa18907.csv matrix good
#> $`/tmp/RtmpBPfjqs/file29c83c4bb6ff.csv`
#>    sample condition
#> s1     s1   treated
#> s2     s2   treated
#> s3     s3   control
#> s4     s4   control
#> 
#> $assays
#> $assays$`/tmp/RtmpBPfjqs/file29c8aa18907.csv`
#>       s1 s2 s3 s4
#> gene1  1  4  7 10
#> gene2  2  5  8 11
#> gene3  3  6  9 12
#> 
#>