In lukatrkla/lanpAnalysis: lanpAnalysis Analyzes HLA-I-binding Neoantigens Significant for Lung Adenocarcinoma Immunotherapy

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(lanpAnalysis)

Overview

lanpAnalysis: (Lung Adenocarcinoma Neoantigen Patient Analysis) is an R package that aims to analyze patient peptide data, for probable HLA-I-binding neoantigens that may be significant for lung adenocarcinoma immunotherapy. In recent years, neoantigens have been be an immunotherapy target, as they may produce the rejection of tumours. lanpAnalysis streamlines this process by indexing and analyzing a patient's genes, and returns cruicial information on the presence of these neoantigens, their relative affinities to HLA Alleles, and their location within the gene.

Installation

You can install the released version of lanpAnalysis from GitHub with:

require("devtools")
devtools::install_github("lukatrkla/lanpAnalysis", build_vignettes = TRUE)
library("lanpAnalysis")

The ultimate aim of the package is to provide a graphical output to the patient's neoantigens that were discovered in their genes. Below are information pertaining to the 8 functions included in this package, as well as additional dataSets for testing and learning.

graph_affinity

graph_affinity(geneName, seq, colorFlag = 0)

This function Barplots a gene's predicted percentage ranks for its present mutant neoantigen peptides (which have HLA alleles affinities information available in the lung cancer neoantigen database.)

geneName - The string name of the gene.
seq - The string peptide sequence associated with the gene.
colorFlag - By Default, colorFlag is set to 0. If it is set to 1, all subsequent affinity barplots will be coloured according to binding strength between the mutation and the HLA-Allele. If it is set to 2, all subsequent affinity barplots will be coloured according to confidence level between mutation and the HLA-Allele.
Try using this on your pc:

graph_affinity("TP53", tp53mutated3times, 1)

tp53mutated3times is included in the package; depending on how you've loaded the package, make sure the syntax is correct when calling the function. This is true for all examples below that I encourage you to try.

mutation_gene_data

mutation_gene_data(geneName, seq)

This function analyzes seq for mutations in the lung cancer neoantigen database, for neoantigens pertaining to geneName. It returns a data.frame with the information pertaining to each neoantigen discovered, as well as the position in seq where the mutation was found.
geneName - The string name of the gene.
seq - The string peptide sequence associated with the gene.
Try using this on your pc:

mutation_gene_data("TP53", tp53mutated3times)

mutation_positions

mutation_positions(geneName, seq)

This function analyzes seq for mutations in the lung cancer neoantigen database, for neoantigens for pertaining to geneName, and returns a data.frame with the information pertaining to where each neoantigen mutation was positioned in seq, as well as where the mutation's length.
geneName - The string name of the gene.
seq - The string peptide sequence associated with the gene.
Try using this on your pc:

mutation_positions("TP53", tp53mutated3times)

patient_affinity_visualizer

mutation_positions(geneName, seq)

This function barplots a set of gene's predicted percentage ranks, for their present mutant neoantigen peptides (which have HLA alleles affinities information available in the lung cancer neoantigen database dbPepNeo.)
geneSet - A data.frame of gene names and associated gene peptide sequence.
colNum - Number of Columns the resulting set of barplots will have.
colorFlag - By Default, colorFlag is set to 0. If it is set to 1, all subsequent affinity barplots will be coloured according to binding strength between the mutation and the HLA-Allele. If it is set to 2, all subsequent affinity barplots will be coloured according to confidence level between mutation and the HLA-Allele.
Try using this on your pc:

patient_affinity_visualizer(testingGeneSet, colNum=1, colorFlag=0)

patient_location_visualizer

patient_location_visualizer(testingGeneSet, colNum=1, confidence=0)

This function produces barplots for a set of gene's predicted percentage ranks, for their present mutant neoantigen peptides (which have HLA alleles affinities information available in the lung cancer neoantigen database dbPepNeo.)
geneSet - A data.frame of gene names and associated gene peptide sequence.
colNum - Number of Columns the resulting set of barplots will have.
colorFlag - By Default, colorFlag is set to 0. If it is set to 1, all subsequent affinity barplots will be coloured according to binding strength between the mutation and the HLA-Allele. If it is set to 2, all subsequent affinity barplots will be coloured according to confidence level between mutation and the HLA-Allele.
Try using this on your pc:

patient_location_visualizer(testingGeneSet, colNum=1, confidence=0)

patient_neoantigen_list

patient_neoantigen_list(geneSet)

This function produces a list of valid indexes into geneSet where a neoantigen target for lung cancer was discovered. This serves to effectively remove all the genes from query that do not have neoantigen target information for lung cancer available.
geneSet - A data.frame of gene names and associated gene peptide sequences.
Try using this on your pc:

patient_neoantigen_list(testingGeneSet)

plot_mutation_location

plot_mutation_location(geneName, seq, confidence = 0)

This function produces a graphical output portraying a geneName's neoantigen positions, as well as predicted percentage ranks for their present mutant neoantigen peptides (which have HLA alleles affinities information available in the lung cancer neoantigen database.)
geneName - The string name of the gene.
seq - The string peptide sequence associated with the gene.
confidence - By default, confidence is set to 0. If it is set to 1, scatterplot will be coloured according to the confidence level of the neoantigens. Try using this on your pc:

plot_mutation_location("TP53", tp53mutated3times, confidence = 1)

surrounding_peptides

surrounding_peptides(geneName, seq, sides = 3)

Often, the immediate peptides to a mutation are relevant for analyzing a neoantigen. surrounding_peptides finds all neoantigens within seq for geneName that are in the lung cancer database, and returns a dataframe containing the strings for each of these found neoantigens with sides number of flanking peptides in the peptide sequence (in lower case), and the peptides' position.
geneName - The string name of the gene.
seq - The string peptide sequence associated with the gene.
sides - The number of peptides you wish to view flanking the ends of seq. By default this is 3.
Try using this on your pc:

plot_mutation_location("TP53", tp53mutated3times, confidence = 1)

lungData

LungData is a dataset modified from dbPepNeo [http://www.biostatistics.online/dbPepNeo/index.php]. It includes neoantigen data relevant to non-small lung Cancer.

SessionInfo

As per feed back given to me, I was encouraged to include sessionInfo into my vignette.

sessionInfo("lanpAnalysis")

lukatrkla/lanpAnalysis documentation built on Jan. 1, 2021, 8:26 a.m.