PRPSSLextension: PRPS self-training (used to be called self-learning)...

In ajiangsfu/PRPS: Binary classification with PRPS, PRPS-ST and more

PRPS self-training (used to be called self-learning) extension

Description

This is a PRPS self-training extension function to calculate PRPS (Probability ratio based classification predication score) scores and make binary classification calls for a testing data set with a PRPS self-training object, e.g., output of PRPSstableSLwithWeights. The selected feature list, these features' parameters are extracted from the given PRPS self-training object.

Usage

PRPSSLextension(
  PRPSSLObj,
  newdat,
  standardization = FALSE,
  classProbCut = 0.9,
  imputeNA = FALSE,
  byrow = TRUE,
  imputeValue = c("median", "mean")
)

Arguments

PRPSSLObj	a PRPS self-training object that is the output from function PRPS_stableSLwithWeights or PRPSSLwithWeights or PRPSSLwithWeightsPrior or PRPSSLwithWeightsEM.
newdat	a new data matrix or data frame, which is comparable to self-training data set, with columns for samples and rows for features
standardization	a logic variable to indicate if standardization is needed before classification score calculation
classProbCut	a numeric variable within (0,1), which is a cutoff of Empirical Bayesian probability, often used values are 0.8 and 0.9, default value is 0.9. Only one value is used for both groups, the samples that are not included in either group will be assigned as UNCLASS
imputeNA	a logic variable to indicate if NA imputation is needed, if it is TRUE, NA imputation is processed before any other steps, the default is FALSE
byrow	a logic variable to indicate direction for imputation, default is TRUE, which will use the row data for imputation
imputeValue	a character variable to indicate which value to be used to replace NA, default is "median", the median value of the chose direction with "byrow" data to be used

Details

This is the function to calculate PRPS scores, Empirical Bayesian probabilities and make binary classification for a testing data set. This new testing data set should be comparable to the self-training data set as much as possible. Within this current function, standardization step is included as an option to minimize the difference between self-training and testing data sets. Whether or not a user decides to do standardization, this should be consistent between self-training and testing data sets, otherwise this current testing function will not work. Notice that standardization step is only done to make distributions of each selected feature comparable within each data set. Be aware that this feature-wise standardization cannot make the sample-wise distributions comparable. For example, the self-training data set must have two classification groups, however, the proportion of one group might be much smaller than the other group in the testing data set compared to the self-training data set, or even worse, the testing data set might contain one classification group only. This is the common problem for classification and feature-wise standardization cannot solve the problem.

In order to solve the problem, we should make data comparable as much as possbile before classification step. For example, use the same pre-processing settings and make suitable batch effect correction. For classification with PRPS approach, we also suggest to combine self-training and testing data together as "newdat" for this PRPSSLextension function, to avoid forcing samples into two groups while there is actual only one group in the testing data set.

PRPS calculation is based on Ennishi 2018. The fomula is: PRPS(X_i) = \sum (|a_j| log10(P1(x_ij)/P0(x_ij))) Here, a_j represents the jth selected feature weights, and x_ij is the corresponding feature value for the ith sample, P1 and P0 are the probabilities that the ith sample belongs to two different groups. The therotic cutoff is 0 to make classification calls based on PRPS score, alternatively, we can use empirical Bayesian approach to make calls.

When a Empirical Bayesian probability is calculated, by default, the 1st group in the input mean and sd vectors is treated as the test group. When we calculate the probabilities, we first calcualte probability that a sample belongs to either group, and then use the following formula to get Empirical Bayesian probability: prob(x) = d_test(x)/(d_test(x) + d_ref(x)) Here prob(x) is the Empirical Bayesian probability of a given sample, d_test(x) is the density value assuming that a given sample belongs to the test group, d_ref(x) is the density value assuming that a given sample belongs to the reference group. In the current function, however, we calculate Empirical Bayesian probabilities for both directions.

Value

A data frame with PRPS scores, Empirical Bayesian probabilites for two groups and classification, and classification based on 0 natural cutoff on PRPS scores.

Author(s)

Aixiang Jiang

References

Ennishi D, Jiang A, Boyle M, Collinge B, Grande BM, Ben-Neriah S, Rushton C, Tang J, Thomas N, Slack GW, Farinha P, Takata K, Miyata-Takata T, Craig J, Mottok A, Meissner B, Saberi S, Bashashati A, Villa D, Savage KJ, Sehn LH, Kridel R, Mungall AJ, Marra MA, Shah SP, Steidl C, Connors JM, Gascoyne RD, Morin RD, Scott DW. Double-Hit Gene Expression Signature Defines a Distinct Subgroup of Germinal Center B-Cell-Like Diffuse Large B-Cell Lymphoma. J Clin Oncol. 2019 Jan 20;37(3):190-201.

Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A. 2003 Aug 19;100(17):9991-6.

ajiangsfu/PRPS documentation built on April 29, 2023, 10:13 p.m.