DeepGenomeScan: Implementation of deep learning based genome scan

In xinghuq/DeepGenomeScan: DeepGenomeScan: A Deep Learning Approach for Whole Genome Scan (WGS) and Genome-wide Association Studies (GWAS)

Implementation of deep learning based genome scan

Description

Deep learning based genome scan with various ready-to-use models and self-defined models.

Usage

DeepGenomeScan(genotype, ...)

## Default S3 method:
DeepGenomeScan(
  genotype, 
  env,
  method = "modelRSNNSmlpdecay",
  preProcess = NULL,
  ...,
  weights = NULL,
  metric = ifelse(is.factor(y), "Accuracy", "RMSE"),
  maximize = ifelse(metric 
  trControl = trainControl(),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3),
seed = 123)

## S3 method for class 'formula'
DeepGenomeScan(form, data, ..., weights, subset, na.action = na.fail, contrasts = NULL,seed = 123)

## S3 method for class 'recipe'
DeepGenomeScan(
  genotype,
  data,
  method = "modelRSNNSmlpdecay",
  ...,
  metric = ifelse(is.factor(y_dat), "Accuracy", "RMSE"),
  maximize = ifelse(metric 
  trControl = trainControl(),
  tuneGrid = NULL,
  tuneLength = ifelse(trControl$method == "none", 1, 3),
seed = 123)
  

Arguments

genotype	The genotype matrix, or other omics-based variations matrix. This is a matrix where samples are in rows and variations/features are in columns. This could be a simple matrix, data frame or other type (e.g. sparse matrix) but must have column names (see Details below). Preprocessing using the preProcess argument only supports matrices or data frames. When using the recipe method, _"genotype"_ should be an unprepared recipe object that describes the model terms (i.e. outcome, predictors, etc., e.g. the h2o model in our example) as well as any pre-processing that should be done to the data. This is an alternative approach to specifying the model. Note that, when using the recipe method, any arguments passed to preProcess will be ignored. See the links and example below for more details using recipes.
env	env/trait data. The environmental variables/geographic coordinates, or the latent geographic genetic structure vairbales produced from KLFDAPC. For GWAS, this shoud be the traits or phenotypic variables. The data can be a numeric or factor vector corresponding to each sample.
method	The read-to-use models or user defined models. A string specifying which model to use. A list of functions can also be passed for a custom model function, see our example tutorials.
...	Arguments passed to the defined models (such as "mlpWeightDecayML"). Errors will occur if values for tuning parameters are passed here.

Value

This step is the model training and tuning process. Values return the final fitted model and parameters, as well as the details of model tuning metrics. In terms of estimating variation (variable) importance, feed the model to varImp and then use the importance to estimate the q-values (DL_qvalues).

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.
#### example
#load("sim_example.RData")
#genotype=sim_example[,-c(1:14)]
#env=sim_example[,2:11]
## The function is currently defined as

xinghuq/DeepGenomeScan documentation built on Sept. 20, 2022, 8:46 a.m.