Investigating neuron intrinsic defects in 4H and Globoid Leukodystrophy 97 3 mRNA Sequencing Analysis A pre-established pipeline was used to process the samples1. This pipeline performed the quality control of reads, carried the trimming of adapters, aligned the reads and performed the quality control of the resulting alignment. The data was sequenced using a paired-end stranded library (reverse strand) and sequenced using the Illumina technology. Specifically, sequence alignment was carried in STAR v2.7.10a with the human GRCh38 reference (GENCODE v44). Counts per gene were generated by STAR. Prior to alignment, reads were pre-processed in TrimGalore to remove adapters sequences. If after adapter trimming the resulting read had a length below 35 bp, it was removed from the analysis. Count data processed above was evaluated in DESeq2 to detect differentially expressed genes. A principal component analysis was performed to check for outliers and how samples were being separated by cluster. Differential expression (DE) analysis was carried out in R v4.1.2 using DESeq2 (v1.34). For each comparison, the log2FC was shrunken using the apeglm algorithm. Gene-set enrichment analysis, using the pre-ranked list of genes (signed log2FC x -log10(padj)) was done in fgsea using five different molecular ontology databases: Hallmarks (biological states or processes), GO_BP (Biological Process), GO_CC (Cellular Component), GO_MF (Molecular Function) and KEGG (pathways). Only findings with and FDR below 0.25 were considered as a FDR value of 0.25 indicates that the result is likely to be valid 3 out of 4 times, which is reasonable in the setting of exploratory discovery where one is interested in finding candidate hypothesis to be further validated as a results of future research. Statistics Data analysis was started by addressing potential outliers, we applied the Robust Regression and Outlier Removal (ROUT) method (Q=1%) (Motulsky & Brown, 2006). For cocultures, the data exhibited unbalanced sampling and frequent violations of variance homogeneity and normality, hence, group differences were evaluated using the KruskalWallis test, a nonparametric method relatively robust to unequal sample sizes compared to ANOVA. Post-hoc testing with Dunn’s multiple comparisons test was performed for nearsignificant result of comet density. All results are detailed in Supplementary Table II. 1 https://i3s-bioinformaticsservice.github.io/analysis/transcriptomics/rnaseq_star_default/
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