netie_readme.md

In tianshilu/Netie: Neoantigen-T cell Interaction Estimation

Netie

An R package to infer the anti-tumor selection pressure for tumors.

Introduction

The Bayesian Hierarchical Model named Neoantgien-T cell interaction estimation (Netie) is to investigate the neoantigens observed in the patient tumors to esti- mate the history of the immune pressure on the evolution of the tumor clones. The estimation results will answer whether the host immune system has been conferring strong or weaker selection pressure on the tumor clones over the time of tumor development. This may give us a peak into the future of how the mutations and clones will evolve for that patient. The model is based on pyclone estimation results. Essentially, each clone is modelled separately, but sharing some random variables.

Please refer to our lab’s website for more information https://qbrc.swmed.edu/labs/wanglab/software.php.

Installation of the package:

To install our package, you may simply execute the following codes.

# install.packages("devtools") 

devtools::install_github("tianshilu/netie", subdir = "netie") # don't forget to specify subdir!

Dependencies

PyClone; R(version>3.4)

Guided Tutorial

Command:

netie(input_data,sigma_square = 100000 ,
      alpha = 10,beta = 2,sigma_p_sqr = 0.1,sigma_a_sqr = NULL,max_iter =100000,multi_sample = T)

• input_data: a list with each data frame as the data for each patient. Each data frame consists 7 columns and each row is for one mutation. The 7 columns are mutation ID, sample ID, cluster ID, cellular prevalence, variant allele prevalence, variant allele frequency, and neoantigen load with column names as “mutation_id”,“sample_id”,“cluster_id”,“cellular_prevalence”,“variant_allele_frequency”, and “neoantigen_load”. Please use PyClone or other softwares (https://github.com/tianshilu/Phylogenetic-Tree) to get information of cluster id and cellular prevalence. Please use QBRC mutation calling pipeline (https://github.com/tianshilu/QBRC-Somatic-Pipeline) to call mutations for whole exome sequenicng; QBRC neoantigen calling pipeline (https://github.com/tianshilu/QBRC-Neoantigen-Pipeline) to call neoantigens for whole exome sequencing and RNA sequencing.

examples of input_data:

example_input1= read.table('example_input1.txt', header=T,sep='\t',stringsAsFactors = F)
head(example_input1)

##                   mutation_id       sample_id cluster_id cellular_prevalence
## 1 TCGA-D3-A2JG-06 7 142881273 TCGA-D3-A2JG-06          0           0.3670081
## 2  TCGA-D3-A2JG-06 5 71016376 TCGA-D3-A2JG-06          0           0.3893199
## 3 TCGA-D3-A2JG-06 11 74015355 TCGA-D3-A2JG-06          0           0.3963911
## 4 TCGA-D3-A2JG-06 12 95914926 TCGA-D3-A2JG-06          0           0.3966120
## 5 TCGA-D3-A2JG-06 7 140453136 TCGA-D3-A2JG-06          0           0.3990956
## 6  TCGA-D3-A2JG-06 1 47746773 TCGA-D3-A2JG-06          0           0.3993479
##   cellular_prevalence_std variant_allele_frequency neo_load
## 1              0.07829626                0.1771429        0
## 2              0.09846122                0.2000000        0
## 3              0.11910942                0.2864583        0
## 4              0.12477982                0.3121387        5
## 5              0.12635598                0.3260870        1
## 6              0.11804737                0.2951807        0

library(ggplot2)

ggplot(example_input1,aes(variant_allele_frequency,neo_load))+ geom_point(colour = "seagreen3", size = 3) +labs(x = "Variant Allele Frequency",y="#Neoantigen per mutation")

example_input2= read.table('example_input2.txt', header=T,sep='\t',stringsAsFactors = F)
head(example_input2)

##                         mutation_id       sample_id cluster_id
## 1 TCGA-BF-A3DN-01 GL000205.1 117561 TCGA-BF-A3DN-01          0
## 2       TCGA-BF-A3DN-01 1 153270485 TCGA-BF-A3DN-01          0
## 3         TCGA-BF-A3DN-01 2 1926514 TCGA-BF-A3DN-01          0
## 4        TCGA-BF-A3DN-01 4 47663875 TCGA-BF-A3DN-01          0
## 5       TCGA-BF-A3DN-01 17 11840675 TCGA-BF-A3DN-01          0
## 6        TCGA-BF-A3DN-01 7 93108821 TCGA-BF-A3DN-01          0
##   cellular_prevalence cellular_prevalence_std variant_allele_frequency neo_load
## 1           0.3441747              0.06237205                0.1290323        0
## 2           0.3450515              0.03324016                0.1538462        0
## 3           0.3470656              0.05007811                0.1372549        0
## 4           0.3529826              0.05362554                0.1600000        0
## 5           0.3537355              0.03360195                0.1655172        3
## 6           0.3588706              0.06354249                0.3085714        0

ggplot(example_input1,aes(variant_allele_frequency,neo_load))+ geom_point(colour = "blue", size = 3) +labs(x = "Variant Allele Frequency",y="#Neoantigen per mutation")

• sigma_square, alpha, beta, sigma_p_sqr, sigma_a_sqr: hyperparameters for prior distributions. Please refer to the paper for more details.

• max_iter: the iterations of Markov chain Monte Carlo.

• multi_sample: use True if one patient has more than one sample.

Two example input datasets can be found here: https://github.com/tianshilu/Netie/tree/main/example

Output

The output is a list with the information of the anti-tumor selection pressure for each clone ac and for the whole tumor a.

Two example output results can be found here: https://github.com/tianshilu/Netie/tree/main/example

examples of output

load('example_output1.RData')
print(example_output$a)

## [1] 1.577462

load('example_output2.RData')
print(example_output$a)

## [1] 3.885662

tianshilu/Netie documentation built on Jan. 21, 2022, 6:24 p.m.