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(sample_to_covariates, full_model, filter_fun = basic_filter,
target_mapping = NULL, max_bootstrap = NULL,
norm_fun_counts = norm_factors, norm_fun_tpm = norm_factors, ...)is a data.frame which contains a mapping
from sample (a column) to some set of experimental conditions or
covariates. The column path is also required, which is a character
vector where each element points to the corresponding kallisto output directory. The column
sample should be in the same order as the corresponding entry in
path.
is a formula which explains the full model (design)
of the experiment OR a design matrix. 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.
a function to perform between sample normalization on the estimated counts.
a function to perform between sample normalization on the TPM
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 in the model is zero