Over Representation Analysis (ORA)

Calculates regulatory activities using ORA.

Usage

run_ora(
  mat,
  network,
  .source = source,
  .target = target,
  n_up = ceiling(0.05 * nrow(mat)),
  n_bottom = 0,
  n_background = 20000,
  with_ties = TRUE,
  seed = 42,
  minsize = 5,
  ...
)

Arguments

mat

Matrix to evaluate (e.g. expression matrix). Target nodes in rows and conditions in columns. rownames(mat) must have at least one intersection with the elements in network .target column.

network

Tibble or dataframe with edges and it's associated metadata.

.source

Column with source nodes.

.target

Column with target nodes.

n_up

Integer indicating the number of top targets to slice from mat.

n_bottom

Integer indicating the number of bottom targets to slice from mat.

n_background

Integer indicating the background size of the sliced targets. If not specified the number of background targets is determined by the total number of unique targets in the union of mat and network.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

seed

A single value, interpreted as an integer, or NULL for random number generation.

minsize

Integer indicating the minimum number of targets per source.

...

Arguments passed on to stats::fisher.test

workspace

an integer specifying the size of the workspace used in the network algorithm. In units of 4 bytes. Only used for non-simulated p-values larger than \(2 \times 2\) tables. Since R version 3.5.0, this also increases the internal stack size which allows larger problems to be solved, however sometimes needing hours. In such cases, simulate.p.values=TRUE may be more reasonable.

hybrid

a logical. Only used for larger than \(2 \times 2\) tables, in which cases it indicates whether the exact probabilities (default) or a hybrid approximation thereof should be computed.

hybridPars

a numeric vector of length 3, by default describing “Cochran's conditions” for the validity of the chisquare approximation, see ‘Details’.

control

a list with named components for low level algorithm control. At present the only one used is "mult", a positive integer \(\ge 2\) with default 30 used only for larger than \(2 \times 2\) tables. This says how many times as much space should be allocated to paths as to keys: see file fexact.c in the sources of this package.

or

the hypothesized odds ratio. Only used in the \(2 \times 2\) case.

alternative

indicates the alternative hypothesis and must be one of "two.sided", "greater" or "less". You can specify just the initial letter. Only used in the \(2 \times 2\) case.

conf.int

logical indicating if a confidence interval for the odds ratio in a \(2 \times 2\) table should be computed (and returned).

conf.level

confidence level for the returned confidence interval. Only used in the \(2 \times 2\) case and if conf.int = TRUE.

simulate.p.value

a logical indicating whether to compute p-values by Monte Carlo simulation, in larger than \(2 \times 2\) tables.

B

an integer specifying the number of replicates used in the Monte Carlo test.

Value

A long format tibble of the enrichment scores for each source across the samples. Resulting tibble contains the following columns:

1. statistic: Indicates which method is associated with which score.

2. source: Source nodes of network.

3. condition: Condition representing each column of mat.

4. score: Regulatory activity (enrichment score).

Details

ORA measures the overlap between the target feature set and a list of most altered molecular features in mat. The most altered molecular features can be selected from the top and or bottom of the molecular readout distribution, by default it is the top 5% positive values. With these, a contingency table is build and a one-tailed Fisher’s exact test is computed to determine if a regulator’s set of features are over-represented in the selected features from the data. The resulting score, ora, is the minus log10 of the obtained p-value.

See also

Other decoupleR statistics: decouple(), run_aucell(), run_fgsea(), run_gsva(), run_mdt(), run_mlm(), run_udt(), run_ulm(), run_viper(), run_wmean(), run_wsum()

Examples

inputs_dir <- system.file("testdata", "inputs", package = "decoupleR")

mat <- readRDS(file.path(inputs_dir, "mat.rds"))
net <- readRDS(file.path(inputs_dir, "net.rds"))

run_ora(mat, net, minsize=0)
#> # A tibble: 72 × 5
#>    statistic source condition score  p_value
#>    <chr>     <chr>  <chr>     <dbl>    <dbl>
#>  1 ora       T1     S01        3.82 0.000150
#>  2 ora       T1     S02        3.82 0.000150
#>  3 ora       T1     S03        3.82 0.000150
#>  4 ora       T1     S04        3.82 0.000150
#>  5 ora       T1     S05        0    1       
#>  6 ora       T1     S06        0    1       
#>  7 ora       T1     S07        3.82 0.000150
#>  8 ora       T1     S08        3.82 0.000150
#>  9 ora       T1     S09        3.82 0.000150
#> 10 ora       T1     S10        3.82 0.000150
#> # ℹ 62 more rows