ALICE

KO | EN

gitlite — search

#javascript #hacktoberfest #react #ai #golang #android #machine-learning #deep-learning #linux #docker #nodejs #windows #awesome #pytorch

(★ 28)

Detecting TCR involved in immune responses from single RepSeq datasets

ALICE 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# ALICE
Detecting TCR involved in immune responses from single RepSeq datasets.

## About
Here we provide an R implementation of ALICE approach, see [paper](https://www.biorxiv.org/content/early/2018/07/23/375162) for details.
## Software requirements
Any OS where R is available (Linux, OS X, Windows), however parallel computing is currently not available on Windows.

## Installation

1. Install R distribution of choice (i.e. from [R Core team](https://cloud.r-project.org/), or [Microsoft R Open](https://mran.microsoft.com/open/) )
2. Install BioStrings package from bioconductor: open R console and execute following commands: 
```R
source("https://bioconductor.org/biocLite.R")
biocLite("Biostrings")
```
3. Install *data.table*, *stringdist*, *igraph* packages:
```R
install.packages("igraph")
install.packages("data.table")
install.packages("stringdist")
```
4. Download this github repository.

## Quick start
Lets load some data first and organize it to the list.

This is one VJ combination (TRBV9-TRBJ2-7) from S1 donor from [link](https://www.biorxiv.org/content/early/2018/04/13/300343) on day 0 and day 15 after yellow fever immunization.

```R
library(data.table)
S1d15<-fread("sample/S1_d15_V9_J2_7.tsv")
S1d0<-fread("sample/S1_d0_V9_J2_7.tsv")
S1<-list(d0=S1d0,d15=S1d15)
```

Now lets run ALICE pipeline. Note, that algorithm will create a folder for files produced, if it does not exist:
```R
source("ALICE.R")
S1_alice<-ALICE_pipeline(DTlist=S1,folder="S1_res",cores=1,iter=10,nrec=5e5) #this takes few minutes to run
sapply(S1_alice,nrow)
```
For this VJ-combination we have no significant results in day 0 timepoint, and 34 significant hits in day 15 timepoint.

Note, that for demo purposes we run it on 1 core with 10 iterations (0.5 mln sequences in each iteration) for generative probability estimation simulation. Total number of simulated TCR sequences (both inframe and out-of-frame) in this case is 5 million.

In [paper](https://www.biorxiv.org/content/early/2018/07/23/375162) we used 100 mln simulated sequences for each VJ-class, and this takes a lot of time. 
Datasets from the paper are available [here](https://github.com/pogorely/ALICE_sample_data).

### Additional parameters

*Read_count_filter*(default 0) and *Read_count_neighbour*(default 1) parameters are two conceptually different count threshold for clones considered by the algorithm.

Algorithm discards all clones with count  *Read_count_filter* or less prior to analysis, and it does not consider as neighbours clone with count *Read_count_neighbour* or less (but such clones are not discarded, so if they have a lot of high count neighbours, it could be significant hit).

*qL*(default is FALSE) uses different selection factor for different lengths instead of universal selection factor.

*cor_method* (default "BH") specifies multiple testing correction method supplied to *p.adjust* function from *stats* package. *P_thres* (default 0.001) determines p-value threshold after correction.

## Input file format
Algorithm operates on R *data.table* with following mandatory columns:

*CDR3.amino.acid.sequence*, *bestVGene*, *bestJGene*, *Read.count*.

See sample datasets in *sample* folder.

There is a pipeline to import data in immunoSEQ format. It imports all files in given *folder* with counts larger than *Read_thres*, and also identifies CDR3nt and collapses clones with unique CDR3nt+V+J. immunoSEQ data with short reads do not contain complete CDR3nt sequence. For such data specify *trim* parameter (for reads of length 60 typically *trim=3*), in this case algorithm will identified CDR3nt trimmed from J side by certain number of amino acids.

```R
source("import.R")
dtlist<-import_immunoseq_pipeline(folder="path",trim=3,Read_thres=0)
results<-ALICE_pipeline(dtlist)
```

## (Experimental) Using pipeline with OLGA for Pgen estimation
Install OLGA first (see [OLGA github](https://github.com/zsethna/OLGA) for details).

ALICE will call OLGA in background. Multi-core usage is not available on Windows.

```R
source("ALICE.R")

#import data

S1d15<-fread("sample/S1_d15_V9_J2_7.tsv")
S1d0<-fread("sample/S1_d0_V9_J2_7.tsv")
S1<-list(d0=S1d0,d15=S1d15)

S1_alice<-ALICE_pipeline_OLGA(DTlist=S1,cores=1)
sapply(S1_alice,nrow)

```
For this VJ-combination we have 0 significant results at the day 0 timepoint, and 32 significant hits at the day 15 timepoint.

Note:
On `Rstudio` server environments with limited write access you may need to install OLGA using the --user command:
```
git clone <OLGA>
cd OLGA
python setup.py install --user
```

This installs `olga-compute_pgen` and `olga-generate_sequences` to `~/Username/.local/bin`, so this must be added to you PATH within your `.Rprofile` file:
`Sys.setenv(PATH="/Username/.local/bin:usr/bin")`

Remember to then reload your .Rprofile using 'source(".Rprofile")'

ALICE

ALICE 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

ALICE

ALICE 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README