README.md

In BudhlakotiN/OGS: Outlier in Genomics Data

OGS: Outlier in Genomics Data (Neeraj Budhlakoti, D C Mishra, Anil Rai and Rajeev Ranjan Kumar)

Description: Detection of influential observation is one of the crucial step of pre-processing to identify suspicious elements in data that may be due to error or some other unknown source. Several statistical measures are already developed for detectionof influential observation but still there is a challenge to detect a true influential observation for high dimension data like gene expression, genotyping data. This package identifies influential observation by implementing meta-analysis based approach to combining various least absolute shrinkage and selection operator (LASSO) based diagnostic (Rajaratnam (2019) ) in genomic data hence named as OGS (i.e. outlier in genomic data) based on their p-value. This package identifies outlier in genomic data using different p-value combination methods (i.e. inverse chi, logit, meanp, meanz, sumz, sumlog, sump) with suitable p-value cutoff.

BudhlakotiN/OGS documentation built on Jan. 6, 2020, 12:43 a.m.