Anna Lorenc

Immunogenomics, Gosia Trynka

al16@sanger.ac.uk

Bioinformatics for immunology

genome

Technology

Bioinformatics tools and resources for immunology

transcriptome

proteome

TCRs/BCRs

epitopes

open chromatin

HLA

scRNAseq

scATACseq

CITEseq...

Bulk RNAseq

Bulk proteome

Bulk Chipseq

Flow cytometry

CYTOF

TCR/BCR

bulk/sc

RNA/DNA

Image analysis

Databases and datasets

Reference sequences for HLA/TCR/BCR

Aggregation of experimental datasets

(TCRs, flow cytometry, gene expression)

Topic databases of experimental data (TCR-antigen, antibody-epitope)

Specific experiments

proteome

TCR/BCR

5

VDJdb

ImmuneACCESS

McPass-TCR

TCRdb

iReceptor

VDJserver

OAS

Tools, references

Receptors

(tools + references)

MHC

(references + tools)

TRUST4

arcasHLA

R packages

Immunotation (Convert HLA alleles)

IgGeneUsage(bayesian tests for allele usage)

COMPASS (bayesian test for cell subset differences)

immunarch (CRAN)

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Olga

(probability of generation)

TCRdist3

www.scrna-tools.org

github.com/seandavi/awesome-single-cell

R CRAN

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Bioconductor

packages, vignettes, workflow examples, past courses, books...

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Python

Where to look for help

stackoverflow

Biostars

Bioc list

github

scikit-learn

Scverse

single cell Python packages

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Where to look for inspiration

rstats.wtf (also youtube)

www.r-bloggers.com

Advanced R https://adv-r.hadley.nz/

Package/framework – specific sites

(often github)

Organising tools into workflows:

Shell scripts

Snakemake

Nextflow

Installing & organising tools

Conda (package manager) with thousands of biology relevant packages. Recipes to install ‘complicated’ software

Run a ready docker container – where all the software is already installed. Grab results

What shape?

What slots?

How to put/ access/ change the data in a container?

Input data?

Parameters?

Default values?

Methods implemented?

Type of output?

AnnData <-> SingleCellExperiment<-> Seurat

your code

raw data

your code

raw data

Raw data

Document!

For collaborators

For posteriority

For yourself

For publication

#Comment code

Comment choice of methods, parameters

Note software and references versions

Notebooks, markdown files – analysis narrative + code

RProjects

Git (+GitHub) - for version control and as a repository

Structure your working directories

README files

your code

raw data

Working with R projects

• A way of keeping together all files related to a project: code files, results, notes, history etc.
• Easy for version control

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Start an Rproject for a project

Rproject <= scientific project

When you reopen an R project

• A new R session (process) is started
• The .Rprofile file in the project's main directory (if any) is sourced by R
• The .RData file in the project's main directory is loaded (if project options indicate that it should be loaded).
• The .Rhistory file in the project's main directory is loaded into the RStudio History pane (and used for Console Up/Down arrow command history).
• The current working directory is set to the project directory.
• Previously edited source documents are restored into editor tabs
• Other RStudio settings (e.g. active tabs, splitter positions, etc.) are restored to where they were the last time the project was closed.

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Not recommended

local machine

online repo

git clone https://github.com/SomeUser/SomeRepo.git

git clone https://github.com/AnnaLorenc/tutorials22.git

Commandline, git managers

RStudio has a version control interface too

time

Git

├── .git

├── .gitignore

├── README

├── analysis

│   ├── R

│   │   ├── 01.summarise_genotypes.Rmd

│   │   └── 02.summarise_expression.Rmd

│   ├── eqtl

│   │   └── 04.tensor_flow

│   └── python

│       └── 03.join_expression_to_genotypes.ipynb

├── doc

│   ├── manuscript.md

│   └── notebook.md

├──external_data

│   ├──DICE_expression�│   └──README

├── raw_data

│   ├── expression

│   ├── genotypes

│   └── meta_data

└── results

    ├── eqtl_tensor_flow

    └── plots

├── README.md

├── analysis

├── cellranger

├── count_tables

├── cytoimmgen-phase-2-env-spec-list.txt

├── jupyter_files

├── logs

├── metadata

├── plots

├── README.md

├── cellDeconvolution

│   ├── genotypingData

│   └── vireoOutput

├── colocalization

│   ├── Biogen_coloc_results

│   ├── README.md

│   ├── coloc_config_file.json

│   ├── colocalizing-snps_pp0.6_immune-traits_pseudobulked-time-points.tsv

│   ├── cytoimmgen_ld_calculations

│   ├── downstream_analysis

│   └── results

├── eQTL_mapping

│   ├── cytoimmgen-phase2-genotypes-rsids-dbSNP-v151-grch38-collapsed.tsv

│   ├── cytoimmgen-phase2-genotypes-rsids-dbSNP-v151-grch38.txt

│   ├── eQTL_catalogue_files

│   ├── eqtl_file_list.txt

│   ├── eqtl_groups_list.txt

│   ├── eqtl_sample_sizes.tsv

│   ├── lead_signals_with_q_value

│   ├── median_expression_file_list.txt

│   └── qtl_number.csv

└── scRNAseq_data_analysis

    ├── Activation-time-course-CD4-T-cells_farm.Rmd

    ├── EDA_resting_T_cells.ipynb

    ├── Effectorness_analysis_resting_CD4_cells.Rmd

.git

.gitignore

raw data

R

python

Shade A, Teal TK (2015) Computing

Workflows for Biologists: A Roadmap. PLoS Biol

Raw data + metadata safely backed up

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Considering & trying many possible tools and workflows

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Optimising parameters for the tools

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Extracted final version of the analysis

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Shade A, Teal TK (2015) Computing

Workflows for Biologists: A Roadmap. PLoS Biol

Wilson G, Bryan J, Cranston K, Kitzes J,

Nederbragt L, Teal TK (2017) Good enough

practices in scientific computing

PLoS Comput Biol

Box 1. Summary of practices

1. Data management
1. Save the raw data.
2. Ensure that raw data are backed up in more than one location.
3. Create the data you wish to see in the world.
4. Create analysis-friendly data
5. Record all the steps used to process data.
6. Anticipate the need to use multiple tables, and use a unique identifier for every record.
7. Submit data to a reputable DOI-issuing repository so that others can access and cite it.
2. Software
• Place a brief explanatory comment at the start of every program.
• Decompose programs into functions.
• Be ruthless about eliminating duplication.
• Always search for well-maintained software libraries that do what you need.
• Test libraries before relying on them.
• Give functions and variables meaningful names.
• Make dependencies and requirements explicit.
• Do not comment and uncomment sections of code to control a program’s behavior.
• Provide a simple example or test data set.
• Submit code to a reputable DOI-issuing repository.

....

A lot of biologists suffered when testing essentiality of these rules!

Anna Lorenc

Marta Alcantara

Elena Lukyanova

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Immunogenomics, Gosia Trynka

al16@sanger.ac.uk

R, Python intro

Exercise 1

Open exercises description in exercises.html

Activate conda

Start RStudio and navigate to a work directory

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Start jupyter notebook

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Create directory structure for your work

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Exercise 2

Python with jupyter notebook

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Exercise 3

tidyverse

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Exercise 4

Working out an unknown package

/home/training/Bioinfo-T-Cell-Immunology-July22/Day1/tutorials22

Conda: group2_training_env

Change directory

cd

Display directory content

ls

Make directory DIR

mkdir DIR