In JustinaXie/MDATA: Multi-trait for De novo mutation Association Test with Annotations

knitr::opts_chunk$set(echo = TRUE)

1. Load the de novo mutation data

load("./data/Multi_Toy_Data.rda")

The toy data has 5 columns: Gene, mutability of a gene (mut_dn), de novo mutation counts for the first trait (dn_trait1), de novo mutation counts for the second trait (dn_trait2), and 3 gene-level annotations (Anno1-Anno3).

For real data multi-trait analysis, please prepare a de novo mutation data containing Gene,mut_dn,dn_trait1,dn_trait2, and two annotation dataframe Anno1, Anno2 containg gene-level annotations for two traits respectively.

1. Single-trait without annotation analysis

#Input the data as dnm
dnm<-Multi_Toy_Data
Single_Analysis<-Single_No(dnm,"dn_trait1",N_1=5000)
#Get candidate genes based on FDR threshold 0.05
Get_Single_Gene(Single_Analysis)

1. Single-trait with annotation analysis

#Input the data as dnm, and annotation data as Anno
dnm<-Multi_Toy_Data
Anno<-cbind(dnm$Anno1,dnm$Anno2)
#Here, we recommend to use the estimated beta0 from step 2 as the input here
Single_Analysis<-Single_Anno(dnm,"dn_trait1",N_1=5000,Anno,beta0_init = 3)
#Get candidate genes based on FDR threshold 0.05
Get_Single_Gene(Single_Analysis)

1. Multi-trait without annotation analysis

#Input the data as dnm, and annotation data as Anno
dnm<-Multi_Toy_Data
Multi_Analysis<-Multi_No(dnm,N_1=5000,N_2=5000)
#Get candidate genes based on FDR threshold 0.05
Get_Multi_Gene(Multi_Analysis)

#Input the data as dnm, and annotation data as Anno
dnm<-Multi_Toy_Data
Multi_Analysis<-Multi_Anno(dnm,Anno,Anno,N_1=5000,N_2=5000,beta0_trait1_init = 3,beta0_trait2_init = 2)
#Get candidate genes based on FDR threshold 0.05
Get_Multi_Gene(Multi_Analysis)

JustinaXie/MDATA documentation built on Dec. 18, 2021, 2:34 a.m.