sleuth_prep: Constructor for a 'sleuth' object
In pachterlab/zika: Tools for investigating RNA-Seq

Description Usage Arguments Value See Also Examples

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,
  aggregation_column = NULL, ...)

`sample_to_covariates`	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`.
`full_model`	an R `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`. You can fit multiple covariates by joining them with '+' (see example)
`filter_fun`	the function to use when filtering.
`target_mapping`	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.
`max_bootstrap`	maximum number of bootstrap values to read for each transcript.
`norm_fun_counts`	a function to perform between sample normalization on the estimated counts.
`norm_fun_tpm`	a function to perform between sample normalization on the TPM
`aggregation_column`	a string of the column name in `target_mapping` to aggregate targets
`...`	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_wt or sleuth_lrt to perform hypothesis testing

# Assume we have run kallisto on a set of samples, and have two treatments,
genotype and drug.
colnames(s2c)
# [1] "sample"  "genotype"  "drug"  "path"
so <- sleuth_prep(s2c, ~genotype + drug)