make_lr_sleuth_object: Make Compositional Sleuth Object
In warrenmcg/sleuth-ALR: Compositional Approach to Sleuth Data Analysis

This is a wrapper function for the sleuth pipeline that applies the compositional normalization approach. Many of the arguments are ones that will be input into different parts of the sleuth pipeline: sleuth_prep, sleuth_fit, sleuth_wt, and sleuth_lrt.

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make_lr_sleuth_object(sample_to_covariates, denom_name, lr_type = "alr",
  full_model = NULL, beta = NULL, null_model = NULL,
  run_models = FALSE, ...)

`sample_to_covariates,`	the sample_to_covariates `data.frame` for sleuth
`denom_name,`	target ID names or index numbers of features to be used for the denominator when using compositional analysis; this argument is required for the ALR transformation. Using 'clr' or 'iqlr' for 'lr_type' overrides this argument (and the CLR / IQLR transformation will be used). If 'best' is used, then the internal function `choose_best_denoms` will be used to identify the feature(s) with the most consistent abundance across all samples in the experiment. See choose_denom for extra options.
`lr_type,`	either "alr", "clr", or "iqlr" ("ALR" / "CLR" / "IQLR" are also accepted), indicating additive, centered, or interquartile logratio transformation
`full_model,`	the full model for sleuth
`beta,`	the beta you wish to use for the Wald test. If `NULL` (the default), the Wald test will be skipped.
`null_model,`	the null model to be the baseline for the LR test. If `NULL` (the default), this step is skipped.
`run_models`	boolean to see if the modeling step should be done. If `FALSE`, the default, only sleuth_prep is done.
`...`	extra options that will tweak the analysis, specifically for `get_lr_functions`, `sleuth_prep`, and `sleuth_fit`. for details on which options can be specified for `sleuth_prep` and `sleuth_fit`, please see ?get_lr_functions, ?sleuth::sleuth_prep or ?sleuth::sleuth_fit for details. Note that for `sleuth_prep`, `read_bootstrap_tpm` and `extra_bootstrap_summary` are `TRUE` by default to allow for modeling estimated or TPMs downstream.

a sleuth object that has been prepped using the compositional analysis for the normalization and transformation steps, and fitted using the full model (if run_models is TRUE) and null model (if specified). It will also run the Wald test (if beta is specified) and the LR test (if applicable).

warrenmcg/sleuth-ALR documentation built on Oct. 27, 2020, 4:30 a.m.