ERC (Evolutionary Rate Covariation)

This software is for determining Evolutionary Rate Covariation (ERC) between pairs of genes, using R.

Input:

You will need a .tre file containing Newick format trees of the genes you want to examine. Part of the workflow is to create a master tree of all the species and their corresponding branch lengths. In light of this, optionally, you can provide a master tree, but if you do not have one the program will make one from the list of trees you provide in your .tre file.

Output:

The code will output a correlation residual matrix and a matrix with branch values. The residual matrix has numbers representing ERC correlations; The higher the value is, the more correlated the gene pair's relative evolutionary rates are.

Installation:

Check our installation page for more details.

Use Instructions:

The script you should open and use is RunERC.R. This contains the workflow, which is comprised of several data-processing (and time-consuming) subfunctions.

For an overview of what these functions input, output, and do, please visit our functions page.

First, set the tree and output file names, on lines 11 and 12. You may also need to add the full path to the other R files in this package, such as Updates2022.R (further up in the code). Finally, you're ready to run the code!

At the end of execution, you will have a data object called "corres" which will also be saved as your output .RDS file. Use corres[["cor"]] to see and operate on the correlation matrix. corres[["count"]] represents the number of observations/branches that went into each correlation. To further examine the output, use the following analysis functions.

Analysis functions

Supplementary analysis functions to evaluate your ERC matrices are found in the ERC_supp.R file. Here is what they do:

• clean_list(list, names): makes sure that every item in list is found in names
• pair_list(list, erc_matrix, na.val = -2): creates vector list of gene x gene ERC values, with no repeats or self x self entries. Optional NA replacement value
• make_symmetric(erc_matrix): makes input ERC matrix symmetrical.
• permTestMat(list,matrix,perms=10000): tests for perms permutatinos whether list gene list is significantly different from genes selected by random chance
• fishertransformed_updated(erc_matrix): used to fisher transform the values from computeERC
• betweencomplex(list1,list2, erc_matrix): returns subset matrix based on two gene lists

Quickstart Guide

0. Install dependencies (visit install page)
1. Download package (either download from github or use git clone)
2. Open RunERC.R. This is the file that you will need to execute the pipeline.
3. Set input and output file names on lines 11-12 of RunERC.R
4. Uncomment line 3, then run the program
   • You may need to be explicit about source file paths if the code gives you an error.
   • If using Rstudio, go to Session>Set Working Directory>To Source File Location to set up file imports
5. Comment line 3 until you next boot up R
6. Manipulate corres matrices

Sample analysis

#select random 10 genes, for demonstration
genes = colnames(corres[["cor"]])[sample(1:220, 10, replace=FALSE)]

# You could also type them into a list manually:
# genes = c("NSE5_1", "NSE6_3",  "CSE1_3",  "CSE1_1",  "EXO70_1", "MCM2_4",  "MDY2_1",  "ATP1_2",  "MCM5_1",  "SEC8_2")

#select a matrix of the 10 genes against themselves
filtered = betweencomplex(genes,genes,corres[["cor"]])

#make it symmetrical
sym = make_symmetric(filtered)

#output
sym