verifyDoppelgangers: Verifies the functionality of Doppelgangers

In lr98769/doppelgangerIdentifier: Identifies Doppelgangers Between Datasets With PPCC And Meta data

Verifies the functionality of Doppelgangers

Description

The user constructs a csv file with with training-validation set pairs ideally incrementing the number of Doppelgangers between training and validation sets. For each training-validation set pair, 12 models with different feature sets will be trained. 10 random feature sets and 2 features sets of highest and lowest variance would be generated. If an increase in validation accuracy of the 10 random models with increasing number of doppelgangers can be observed, we can conclude that the doppelgangers included are functional doppelgangers.

Usage

verifyDoppelgangers(
  experiment_plan_filename,
  raw_data,
  meta_data,
  feature_set_portion = 0.1,
  seed_num = 2021,
  separator = "\\.",
  do_batch_corr = TRUE,
  k = 5,
  num_random_feature_sets = 10,
  size_of_val_set = 8,
  batch_corr_method = "ComBat",
  neg_con_seed = 10
)

Arguments

experiment_plan_filename	Name of file containing csv experiment plan. The csv file has a header with the names of the training_validation sets (e.g. "Doppel_0.train" or "Doppel_0.valid"). In each column (e.g. "Doppel_0.train" column), we include the names of all samples included in this training/validation set.
raw_data	Dataframe of count matrix before batch correction
meta_data	Dataframe of meta data
feature_set_portion	Proportion of variables to be used for feature set generation
seed_num	Seed number for random feature set generation
separator	The character separating the name of the training_validation pair e.g. "0 Doppel" from the "train", "valid" label. Name of each column should be in format "0 Doppel.train" if . is used as separator
do_batch_corr	If False, no batch correction is carried out
k	k hyperparameter for KNN classification models
num_random_feature_sets	Number of random feature sets for each training-validation set
size_of_val_set	Size of each validation set (We assume the size of each validation set is the same, this is used for the binomial model)
batch_corr_method	Batch correlation method used. Only 2 options are accepted "ComBat" or "ComBat_seq".
neg_con_seed	Seed used for negative control

Details

Troubleshooting tips:

• Ensure all the headers have no spaces.

• If excel is used for planning, save the spreadsheet as "CSV (MS-DOS) (*.csv)"

• Use the exact label "train" and "valid" (take note of capital letters)

• Ensure the separator does not exist in the name of the training-validation set (E.g. Doppel.0 is not allowed)

• Try to put both training-validation columns beside each other and leave no column gaps

• Refer to the csv file in the tutorial on the GitHub README.

Value

Validation Accuracies

Examples

## Not run: 
verificationResults = verifyDoppelgangers(
experiment_plan_filename = "tutorial/experimentPlan.csv",
raw_data = rc,
meta_data = rc_metadata)

## End(Not run)

lr98769/doppelgangerIdentifier documentation built on Aug. 2, 2022, 9:41 a.m.