sleuth_resultsR Documentation

Extract Wald or Likelihood Ratio test results from a sleuth object


This function extracts Wald or Likelihood Ratio test results from a sleuth object.


sleuth_results(obj, test, test_type = "wt", which_model = "full",
  rename_cols = TRUE, show_all = TRUE,
  pval_aggregate = obj$pval_aggregate, ...)



a sleuth object


a character string denoting the test to extract. Possible tests can be found by using models(obj).


'wt' for Wald test or 'lrt' for Likelihood Ratio test.


a character string denoting the model. If extracting a wald test, use the model name. Not used if extracting a likelihood ratio test.


if TRUE will rename some columns to be shorter and consistent with the vignette


if TRUE will show all transcripts (not only the ones passing filters). The transcripts that do not pass filters will have NA values in most columns.


if TRUE and both target_mapping and aggregation_column were provided, to sleuth_prep, use lancaster's method to aggregate p-values by the aggregation_column.


advanced options for sleuth_results. See details.


The columns returned by this function will depend on a few factors: whether the test is a Wald test or Likelihood Ratio test, and whether pval_aggregate is TRUE.

The sleuth model is a measurement error in the response model. It attempts to segregate the variation due to the inference procedure by kallisto from the variation due to the covariates – the biological and technical factors of the experiment (represented by the columns in obj$sample_to_covariates). For the Wald test, the 'b' column represents the estimate of the selected coefficient. In the default setting, it is analogous to, but not equivalent to, the fold-change. The transformed values are on the natural-log scale, and so the the estimated coefficient is also on the natural-log scale. This value is taking into account the estimated 'inferential variance' estimated from the kallisto bootstraps.

If the user wishes to get gene-level results from this function, there are two ways of doing so:

An important note if pval_aggregate or the old gene_mode is TRUE: when combining the gene annotations from obj$target_mapping, all of the columns except for the transcript ID, obj$target_mapping$target_id, will be included. If there are transcript-level entries for any of the other columns, this will result in duplicate rows in the results table (usually an undesirable result).

Here are advanced options for customizing the p-value aggregation procedure:


If pval_aggregate is FALSE, returns a data.frame with the following columns:

If pval_aggregate is TRUE, returns a data.frame with the following columns:

See Also

sleuth_wt and sleuth_lrt to compute tests, models to view which models, tests to view which tests were performed (and can be extracted)


models(sleuth_obj) # for this example, assume the formula is ~condition,
                     and a coefficient is IP
results_table <- sleuth_results(sleuth_obj, 'conditionIP')