ANEVA-DOT

ANEVA Dosage Outlier Test

Package developed by: Pejman Mohammadi (firstname@Scripps.edu), Christina Sousa (sousa.19@osu.edu). See our paper for method descritions.

This test is designed to detect if ASE data reveals sufficient imbalance to induce an outlier in total gene expression. The test takes in in two vectors of ASE count data from an R data frame, as well as a vector of population estimates for standard deviations (sample estimates are provided). The output is a data frame containing raw and adjusted p-values using Benjamini-Hoschberg method, and a plot showing outlier data points in red.

To install the R package, you can use the devtools package:

install.packages("devtools") 
library(devtools)
install_github("PejLab/ANEVA-DOT")

Once installed, you can type ?ANEVADOT to view package documentation and ?ANEVADOT_test to view test function documentation.

Here is an example to get started with:

library(ANEVADOT)

# Define the output columns
output_columns <- c("GENE_ID", "TISSUE_ID",  "REF_COUNT", "ALT_COUNT", "TOTAL_COUNT", "NULL_RATIO")

# re organize the tables by:
# 1: Selecting only genes that have Vg scores available
# 2: Reordering ASE data and Vg scores so they align
tiss <- "MSCLSK" # The data comes from a skeletal muscle sample
covered_genes <- intersect(Vg_GTEx_v7$IDs, sample_ASE$GENE_ID)
covered_gene_Vgs <- Vg_GTEx_v7[match(covered_genes, Vg_GTEx_v7$IDs), tiss] 
covered_gene_ASE_data <- sample_ASE[match(covered_genes, sample_ASE$GENE_ID),]

# Take the square root of the Vg scores to the get the Standard Deviation (SDg)
covered_gene_SDgs <- sqrt(covered_gene_Vgs) 

# Run ANEVA-DOT
ANEVADOT_scores <- ANEVADOT_test(covered_gene_ASE_data, output_columns = output_columns, 
                          eh1 = "REF_COUNT", eh2 = "ALT_COUNT", coverage = 10, 
                          r0 = covered_gene_ASE_data$NULL_RATIO,
                          Eg_std = covered_gene_SDgs, plot = TRUE)

=======

Further notes:

• If you don't know how to generate ASE data, you can start here and here. Note: This Vg estimates are derived from variant level ASE data and are NOT fully compatible with haplotypic read counts such as Phaser data. Use haplotypic counts only if you don't have have acceess to variant level data.

• You can download our pre-calculated Vg estimates for available datasets at Datasets/Reference_Vg_Estimates.

• Consider excluding false positive prone genes based on our summary stats from running ANEVA-DOT on general population available at Datasets/ANEVA_DOT_frequencies.

If you wish to read your own file into an R data frame for use with the test, you can use one of the following commands:

#for general tabular data (replace `file` with "filename.txt", and be sure to
#set working directory):
read.table(file, header = FALSE, sep = "", dec = ".")

#for tab separated .txt file:
read.delim(file, header = TRUE, sep = "\t", dec = ".")

#for .csv files:
read.csv(file, header = TRUE, sep = ",", dec = ".")

You can also write a text or csv file from your output with the following commands:

#to write a text file (x is the data frame to write, `file` is the 
#"filename.txt" to store the file.
write.table(x, file, append = FALSE, sep = " ", dec = ".",
            row.names = TRUE, col.names = TRUE)
            
#to write a .csv file
write.csv(x, file = "my_data.csv")

BugReports: https://github.com/PejLab/ANEVA-DOT/issues

Citation: Mohammadi, Pejman, et al. Genetic regulatory variation in populations informs transcriptome analysis in rare disease. Science 366.6463 (2019): 351-356.