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Prepare Expression Profile Data From Loose Tables

Source: R/profile.R
prepare_profile_data.Rd

Table-based counterpart of profile_data(): combines a transcript expression table, sample metadata, a transcript-to-gene table and differential expression results into the long profile table consumed by plot_tx_expr(). Gene expression is the sum of the isoform values unless txi_gene is supplied.

Usage

prepare_profile_data(
  txi_transcript,
  sample_metadata,
  tx_to_gene,
  de_result_gene = NULL,
  de_result_transcript = NULL,
  var,
  var_levels = NULL,
  txi_gene = NULL,
  gene_name = NULL,
  pvalue_cutoff = 0.05,
  lfc_cutoff = 1,
  use_fdr = TRUE
)

Arguments

txi_transcript

Transcript expression: a matrix or data frame with transcript IDs as row names or in a transcript_id column, or a tximport list (its abundance element is used).

sample_metadata

Data frame whose first column (or sample_id column) holds the sample IDs and that contains the var column.

tx_to_gene

Data frame with transcript_id, gene_id, transcript_name, gene_name and transcript_type columns (see make_tx_to_gene()).

de_result_gene

Gene-level results with gene_id or gene_name, log2FC and qvalue/pvalue columns. May be NULL.

de_result_transcript

Transcript-level results with transcript_id or transcript_name, log2FC and qvalue/pvalue columns. May be NULL.

var

Name of the grouping column in sample_metadata.

var_levels

Order of the group levels. NULL uses the order of appearance.

txi_gene

Optional gene expression table (same formats as txi_transcript, with gene IDs).

gene_name

Optional genes to keep (names or IDs); NULL keeps all.

pvalue_cutoff, lfc_cutoff, use_fdr

Significance cutoffs.

Value

A tibble in the format described in profile_data().

Examples

iso <- example_isoformic()
prof <- prepare_profile_data(
  txi_transcript = assay(iso, "abundance"),
  sample_metadata = col_data(iso),
  tx_to_gene = tx_annot(iso),
  de_result_gene = de_gene(iso),
  de_result_transcript = de_tx(iso),
  var = "condition",
  gene_name = "RBPJ"
)
head(prof)
#> # A tibble: 6 × 10
#>   feature_id feature_name feature_type gene_id gene_name group mean_expr sd_expr
#>   <chr>      <chr>        <chr>        <chr>   <chr>     <fct>     <dbl>   <dbl>
#> 1 ENSG00000… RBPJ         gene         ENSG00… RBPJ      pree…   1297.    427.  
#> 2 ENSG00000… RBPJ         gene         ENSG00… RBPJ      cont…   1367.    195.  
#> 3 ENST00000… RBPJ-201     protein_cod… ENSG00… RBPJ      pree…     11.2    25.1 
#> 4 ENST00000… RBPJ-201     protein_cod… ENSG00… RBPJ      cont…      3.95    8.30
#> 5 ENST00000… RBPJ-202     protein_cod… ENSG00… RBPJ      pree…    838.    303.  
#> 6 ENST00000… RBPJ-202     protein_cod… ENSG00… RBPJ      cont…   1095.    169.  
#> # ℹ 2 more variables: n_samples <int>, is_de <chr>