Recursos de scRNAseq v 1.0

Os repositórios contêm bases de dados mais de nosso interesse

Para rodar grandes bases de dados, melhor usar servidor remoto. Dependendo do tamanho do dataset, o Galaxy deve servir, ele roda R e Jupyter

Inclui tutoriais de scRNAseq tanto para R quanto para Python. Escolham sua linguagem antes. Têm algoritmos equivalentes. É questão de gosto mesmo. Galaxy aceita ambos.

Um problema que encontramos foi a estrutura das matrizes disponibilizadas. Entao tem um recurso sobre matrizes e conversão de matrizes

#Conceitos básicos de scRNAseq#

https://www.parsebiosciences.com/blog/the-why-and-how-of-scrna-seq-a-guide-for-beginners/ (bem básico mas bem ilustrado)

https://www.10xgenomics.com/what-is-single-cell-rna-seq (também bem básico, mas em video)

https://www.sc-best-practices.org/introduction/scrna_seq.html

https://bioconductor.org/books/3.15/OSCA.basic/

#PROGRAMMING LANGUAGES #

Uma interface muito popular para rodar o R é o R Studio: https://www.datacamp.com/tutorial/r-studio-tutorial

Para o Python, um dos favoritos é o Jupyter https://www.dataquest.io/blog/jupyter-notebook-tutorial/

#DICAS e AJUDA#

ChatGPT

Stack Overflow

Biostar

Usando o R Notebook para codificar

#Repositories#

#sites where scRNAseq datasets are kept#

#Delile et al Mouse Embryonic Spinal cord E9.5-E13.5#

doi:10.1242/dev.173807

 https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-7320/ 

more files in https://github.com/juliendelile/MouseSpinalCordAtlas

#Mouse DRG E11,5-P42 Sharma et al #

doi.org/10.1038/s41586-019-1900-1

https://kleintools.hms.harvard.edu/tools/springViewer_1_6_dev.html?datasets/Sharma2019/all (clustered data in an OK platform)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139088 (raw data)

#DRG E9,5-10,5 Faure et al #

doi.org/10.1038/s41467-020-17929-4

http://pklab.med.harvard.edu/nikolas/pagoda2/frontend/current/pagodaLocal/index.html (clustered data in an awful platform)

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150150 (raw data)

#Whole Mouse Embrio E9,5-E13,5  Cao et al.

doi.org/10.1038/s41586-019-0969-x

https://oncoscape.v3.sttrcancer.org/atlas.gs.washington.edu.mouse.rna/downloads  (clustered data in a OK platform)

https://shendure-web.gs.washington.edu/content/members/cao1025/public/mouse_embryo_atlas/ (pre-processed data)

#Curated repository#

A curated database reveals trends in single-cell Transcriptomics doi:10.1093/database/baaa073

www.nxn.se/single-cell-studies/data.tsv

#GALAXY- use galaxy server for big datasets#

#disk space for users#

https://galaxyproject.org/main/#user-data-and-job-quotas 

#how to use R in galaxy#

 https://training.galaxyproject.org/training-material/topics/introduction/tutorials/r-basics/tutorial.html

 #how to use Python and Jupyter in Galaxy#

 https://training.galaxyproject.org/training-material/topics/galaxy-interface/tutorials/jupyterlab/tutorial.html

#Step by Step tutorials R+SEURAT#

# R-Seurat>10x Genomics NextSeq500 PBMC #

https://satijalab.org/seurat/articles/pbmc3k_tutorial.html

 #R-Bioconductor-Seurat>Smartseq from Tabula Muris> kidney  Detailed quality control steps# 

https://biocellgen-public.svi.edu.au/mig_2019_scrnaseq-workshop/public/introduction-to-single-cell-rna-seq.html

 #R-Seurat> 10 x genomics Non-small Cell Lung Cancer Cells#

https://broadinstitute.github.io/2020_scWorkshop/data-wrangling-scrnaseq.html

#Learn Python#

#Exercises for Scientific Python#

https://www.oreilly.com/library/view/elegant-scipy/9781491922927/ch01.html

#Very clear step by step of big data tutorial analysis in Python#

https://www-users.york.ac.uk/~dj757/BIO00047I/PythonHandout.pdf  (also here)

#Step by Step tutorials Python+Scampy#

#Python > Smartseq from Tabula Muris> brain counts#

https://chanzuckerberg.github.io/scRNA-python-workshop/preprocessing/00-tabula-muris.html

 #Python> 10 x Genomics NextSeq500 PBMC data ( same as Seurat ran on Python)#

https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html

E-book for beginners:

https://www.freecodecamp.org/news/the-python-code-example-handbook/

#DATA MATRICES#

#General concepts (very clear) about RNAseq datasets (regardless of technology)# 

https://bioconductor.org/books/3.13/OSCA.intro/getting-scrna-seq-datasets.html

https://hbctraining.github.io/scRNA-seq/lessons/readMM_loadData.html

# Concepts on how expression matrix are built (well illustrated) #

https://hbctraining.github.io/scRNA-seq/lessons/02_SC_generation_of_count_matrix

#More information about UMIs and their applications#

 https://dnatech.genomecenter.ucdavis.edu/faqs/what-are-umis-and-why-are-they-used-in-high-throughput-sequencing/

# How to construct expression matrix from raw data#

 https://scrnaseq-course.cog.sanger.ac.uk/website/construction-of-expression-matrix.html

#What kind of data matrix does 10x genomics generate#

https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/output/matrices

#Build Sparse Matrix with R-takes up less space# 

https://slowkow.com/notes/sparse-matrix/

#Convert genecodes to gene names

https://pypi.org/project/mygene/

#Read different format of datasets

https://bioconductor.org/books/3.13/OSCA.intro/getting-scrna-seq-datasets.html

#GRAPHS#

https://satijalab.org/seurat/articles/visualization_vignette 

https://satijalab.org/seurat/reference/dimplot