A sleuth is a group of kallistos. Borrowing this terminology, a 'sleuth' object stores a group of kallisto results, and can then operate on them while accounting for covariates, sequencing depth, technical and biological variance.
sleuth_prep(kal_dirs, sample_to_covariates, full_model,
filter_fun = basic_filter, target_mapping = NULL, max_bootstrap = NULL,
...)a character vector of length greater than one where each string points to a kallisto output directory
is a data.frame which contains a mapping
from sample (a column) to some set of experimental conditions or
covariates. The column sample should be in the same order as the
corresponding entry in kal_dirs
is a formula which explains the full model (design)
of the experiment. It must be consistent with the data.frame supplied in
sample_to_covariates
the function to use when filtering.
a data.frame that has at least one column
'target_id' and others that denote the mapping for each target. if it is not
NULL, target_mapping is joined with many outputs where it
might be useful. For example, you might have columns 'target_id',
'ensembl_gene' and 'entrez_gene' to denote different transcript to gene
mappings.
maximum number of bootstrap values to read for each transcript.
additional arguments passed to the filter function
a sleuth object containing all kallisto samples, metadata,
and summary statistics
sleuth_fit to fit a model, sleuth_test to
test whether a coeffient is zero