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.
1 2 3 4 | 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 |
full_model |
an R |
filter_fun |
the function to use when filtering. |
target_mapping |
a |
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 |
... |
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
1 2 3 4 5 | # 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.