This repository includes essential scripts for producing final figures embedded in the following manuscript:
**Costa Verdera, et al.,** _AAV vectors trigger DNA damage response-dependent pro-inflammatory signalling in human iPSC-derived CNS models and mouse brain_
PubMed:
DOI:
GEO: GSE253824
---
The repository is divided in five folders, 1 bulk RNAseq analysis and 3 scRNAseq analyses and 1 snRNAseq analysis.
Below a brief description of the bulk and scRNAseq workflows adopted in this work.
**Bulk RNAseq** analysis was performed using a standard pipeline that includes the follwing steps:
1. Quality control by [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
2. Trimming of bad quality reads with [TrimGalore](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)<details><summary>Running command</summary>trim_galore --quality 20 --fastqc --length 25 --output_dir {outdir} --paired {input.r1} {inout.r2}</details>
3. Alignment with [STAR](https://github.com/alexdobin/STAR)
<details><summary>Running command</summary>
"STAR " +
"--runThreadN {threads} " +
"--genomeDir {input.genome} " +
"--readFilesIn {params.trim_seq} " +
"--outSAMstrandField intronMotif " +
"--outFileNamePrefix {params.aln_seq_prefix} " +
"--outSAMtype BAM SortedByCoordinate " +
"--outSAMmultNmax 1 " +
"--outFilterMismatchNmax 10 " +
"--outReadsUnmapped Fastx " +
"--readFilesCommand zcat "
</details>
4. Gene expression quantification with [featureCounts](https://academic.oup.com/bioinformatics/article/30/7/923/232889)
<details><summary>Running command</summary>
"featureCounts " +
"-a {input.annot} " +
"-o {output.fcount} " +
"-g gene_name " +
"-p -B -C " +
"-s {params.strand} " +
"--minOverlap 10 " +
"-T {threads} " +
"{input.bams} "
</details>
5. Differential Expression analysis with [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html).
For Differential Gene Expression analysis we followed the standard workflow provided by package.
6. Dowstream functional Analysis with [ClusterProfiler](https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html).
In order to retrieve functional annotation from DE analysis, we performed **G**ene **S**et **E**nrichment **A**nalysis by using the _GSEA_ function provided by the package.
**GSEA** analysis was performed in particular using the Hallmark gene set from the MSigDB database (version 7.2)
---
**scRNAseq** analysis was performed using a standard pipeline that includes the following steps:
scRNAseq analysis was performed with [Seurat](https://satijalab.org/seurat/). Below are the main steps of the basic data analysis workflow that start from a minimal object after loading of 10X data to markers identification:
1. Quality control and filtering
2. Cell cycle scoring
3. Normalization (default seurat settings)
4. Scaling (with following variables to regress out: percent.mt + nCount_RNA and CC.Difference calculated as show in [vignette](https://satijalab.org/seurat/articles/cell_cycle_vignette.html#alternate-workflow-1))
5. Dimensionality reduction: PCA
6. Clustering
7. Markers identification
- [7.1] Clusters related markers
- [7.2] Intracluster differential expression analysis according to comparison of interest
