sleuth_fit | R Documentation |
This function is a wrapper for fitting a measurement error model using
sleuth
. It performs the technical variance estimation from the boostraps, biological
variance estimation, and shrinkage estimation.
sleuth_fit(obj, formula = NULL, fit_name = NULL, ...)
obj |
a |
formula |
an R formula specifying the design to fit OR a design matrix.
If you are interested in only fitting the model that was specified in |
fit_name |
the name to store the fit in the sleuth object (at so$fits$fit_name).
If |
... |
advanced options for |
For most users, simply providing the sleuth object should be sufficient. By default, this behavior will fit the full model initially specified and store it in the sleuth object under 'full'.
To see which models have been fit, users will likely find the function
models
helpful.
There are some advanced options for users how wish to customize the fitting procedure. Note that these options have not been thoroughly tested, so their effect on the accuracy of the results are unknown. Here are those advanced options:
Advanced options for modeling choice:
which_var
: which kind of data (counts or TPMs) should be fit? Sleuth by
default models the estimated counts, but can model the TPMs. This argument only accepts
'obs_counts'
(default) or 'obs_tpm'
. Note that if gene_mode
is TRUE
,
and transcript counts were aggregated to the gene-level, 'obs_counts'
will model
the 'scaled_reads_per_base'
summary statistic.
Advanced options for the sliding window shrinkage procedure (these options are passed to
sliding_window_grouping
):
n_bins
: the number of bins that the data should be split for the sliding window shrinkage
using the mean-variance curve. The default is 100.
lwr
: the lower range of variances within each bin that should be included for the shrinkage
procedure. The default is 0.25 (meaning the 25th percentile).
upr
: the upper range of variances within each bin that should be included for the shrinkage
procedure. The default is 0.75 (meaning the 75th percentile).
a sleuth object with updated attributes.
models
for seeing which models have been fit,
sleuth_prep
for creating a sleuth object,
sleuth_wt
to test whether a coefficient is zero,
sleuth_lrt
to test nested models.
# If you specified the formula in sleuth_prep, you can simply run to run the full model so <- sleuth_fit(so) # The intercept only model can be fit like this so <- sleuth_fit(so, ~1, 'reduced')