CONIPHER

CONIPHER mutation clustering and phylogenetic tree building R package

This is the official github repository for the R package to perform mutation clustering and phylogenetic tree building using CONIPHER. For details on how to run mutation clustering and phylogenetic tree builing consecutively with one wrapper script from the command line, please refer to the github repository CONIPHER-wrapper. For full details of all the inputs into CONIPHER clustering and tree building, refer to our protocol (https://doi.org/10.1038/s41596-023-00913-9).

Software

The current implementation of CONIPHER is written in R>=3.6.1 and is distributed as an R package.

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CONIPHER installation

CONIPHER can be installed and run in multiple modes.

1. To run mutation clustering + phylogenetic tree building, install the conipher conda environment from bioconda using the command below. This environment contains R version 4.1.3, the CONIPHER R package, PyClone v0.13.1 (which is required for mutation clustering), and all other dependencies.

conda create -n conipher -c conda-forge -c bioconda conipher

2. To run phylogenetic tree building only, the conda environment can also be used but is not strictly necessary. Alternatively, just the CONIPHER R package can be installed, provided the R package devtools >= 2.4.1 is installed.

To install the CONIPHER R package from an R console, run the following command:

library(devtools)
devtools::install_github("McGranahanLab/CONIPHER")

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Quick start

Running clustering + tree building end-to-end

To get start quickly, you can install CONIPHER and perform mutation clustering and phylogenetic tree reconstruction on the example data provided using the following instructions.

Step 1. Install the conipher conda environment using the instructions above.

Step 2. Start R and load the 'CONIPHER' and 'tidyverse' R packages using the following command:

library(CONIPHER)
library(tidyverse)

Step 3. Specify a parent output directory where the clustering and tree building results will be saved in individual subfolders, for example using the following command:

out_dir <- "conipher_results/"

Step 4. Specify the location of the input table .tsv file. For example, the file path of the toy input table provided in this package is specified using the following command:

input_tsv_loc <- system.file("extdata", "input_table.tsv", package = "CONIPHER", mustWork = TRUE)

Step 5. Run clustering + tree building end-to-end (interactively) using the following command:

conipher_run(case_id = "CRUKTOY001",
             prefix = "CRUK",
             out_dir = out_dir,
             input_tsv_loc = input_tsv_loc)

Running clustering only

Run steps 1 - 4 as described in "Running clustering + tree building end-to-end" above.

Step 5a. Run clustering (interactively) using the following command:

conipher_clustering(case_id = "CRUKTOY001", 
                    out_dir = out_dir, 
                    input_tsv_loc = input_tsv_loc)

Running tree building only

Run steps 1 - 4 as described in "Running clustering + tree building end-to-end" above.

Step 1b. Alternatively, instead of installing the conipher conda environment, install the CONIPHER R package only using the instructions described above.

Step 5b. Run tree building (interactively) using the following command:

conipher_treebuilding(prefix = "CRUK",
                      out_dir = out_dir,
                      input_tsv_loc = tree_input_tsv_loc)

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Anticipated results

The clustering output will include the following output files (examples are in "conipher_results/Clustering"):

The tree building output will include 3 output files (examples are in "conipher_results/Trees"):

• <CASE_ID>.tree.RDS: an R list object containing tree building output information
• pytree_and_bar.pdf: a plot of the default reconstructed tree and barplot
• pytree_multipletrees.pdf: a plot showing all possible alternative phylogenetic trees found by CONIPHER