README.md

In farkkilab/ovaHRDscar: Optimized detection of homologous recombination deficiency for high grade serus ovarian cancer

ovaHRDscar R package manual

• Introduction
  • Contact
• Before to use it
  • Requirements
  • Installation
  • Citation
• Hands-on
  • Input file example
  • Usage example
• References

Introduction

R package to quantify allelic imbalances associated with homologous recombination deficiency in ovarian cancer. Use as input a list of segments per samples as those generated by ASCAT. Have fun with it!

The allelic imbalances quantified by this package are:

1. LOH events bigger than 10Mb but smaller than 50Mb
2. Large scale transitions (LSTs): Two consecutive allelic imbalances of at least 12Mb, with a separition between then no more than 1Mb
3. Telomeric allelic imbalances: Allelic imbalances that extend up to the telomere but do not cross the centromer.

The total number of the previous allelic imbalances are the resultant ovaHRDscar value.

Contact

• Fernando Perez-Villatoro ([e-mail])

Before to use it

Requirements

• Installation of R in any Operating system: Linux, OS X or Windows
• R recomended version 4.3.0
• R package devtools:

install.packages("devtools")

Installation

The ovaHRDscar can be installed via R::devtools from github:

library(devtools)
install_github('farkkilab/ovaHRDscar')

Citation

Please include this citation to your references if you use this tool:

Perez-Villatoro, F., Oikkonen, J., Casado, J., Chernenko, A., Gulhan, D. C., Tumiati, M., ... & Färkkilä, A. (2022). Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer. NPJ precision oncology, 6(1), 96. DOI: https://doi.org/10.1038/s41698-022-00339-8

Hands-on

Input file example

The input is a list of allele specific copy number (ASCN) segments generated by tools like ASCAT. Example of input file:

a<-read.table("/examples/segments.txt", header=T)
head(a)

##         SampleID Chromosome Start_position End_position total_cn A_cn B_cn
## 1 SamplePatient1       chr1          14574       952448        5    0    5
## 2 SamplePatient1       chr1         953394      1259701        3    0    3
## 3 SamplePatient1       chr1        1278085      4551743        2    0    2
## 4 SamplePatient1       chr1        4551885     14124232        2    0    2
## 5 SamplePatient1       chr1       14161231     31062374        3    1    2
## 6 SamplePatient1       chr1       31074785     47428120        4    2    2

The input list should contain columns with the previous order. Each row represent a ASCN segment. It is not necessary to keep a proper row order.

Column description:

• A_cn: A allele copy number value

• B_cn: B allele copy number value

• total_cn: Sum of A and B values

*Note: For each segment, the package will re-order the A and B copy number values, considering as A the one with highest ASCN.

Usage example

Get the number of allelic imbalances associated with HRD in ovarian cancer with the function get.ovaHRDscar:

library("ovaHRDscar")
a <-read.table("/examples/segments.txt", header=T)
get.ovaHRDscars(a, chrominfo ="grch38")

After running it will produce the next output:

##           nLOH LSTs nTAIs ovaHRDscar
## SamplePatient1  25  35    33        93

Ruing parameters: - seg -- Input dataframe with segments - chrominfo -- the reference genome used, grch38 or grch37 (default: grch38)

References

Some of the functions used are adaptations of the package scarHRD (https://github.com/sztup/scarHRD).

farkkilab/ovaHRDscar documentation built on Oct. 11, 2023, 11:04 p.m.