alias: GSM3677718
title: GSM3677718: Patient2_Control; Homo sapiens; RNA-Seq
library_construction_protocol: After 24 h, cell culture growth media was collected, and RNA was isolated and stored in TRI Reagent (#R2050-1-200, Zymo Research, Irvine, CA) for analysis. Sequencing libraries were constructed from 100 to 500 ng of total RNA using the TruSeq® Stranded Total RNA Library Prep (#20020596; Illumina, San Diego, CA) and Ribo-Zero™ rRNA Removal (#MRZH11124; Illumina, San Diego, CA) kits. Fragment size of RNA-seq libraries was verified using 2100 Bioanalyzer Instrument (#G2939BA; Agilent Technologies, Santa Clara, CA) and concentrations were determined using Qubit 4 Fluorometer (#Q33226; ThermoFisher Scientific, Waltham, MA). Libraries were loaded onto HiSeq® 3000 (#SY-401-3001; Illumina, San Diego, CA) for 2 x75 bp paired-end read sequencing. Fastq files were generated using bcl2fastq Conversion Software v2.20 (Illumina, San Diego, CA). Rigorous quality controls of paired-end reads were assessed using FastQC (Babraham Bioinformatics, Cambridge, UK). If required, adapter sequences and low-quality bases were trimmed using cutadapt software. Reads were aligned to the reference genome using HISAT2 v2.1.0 (Johns Hopkins University Center for Computational Biology, Baltimore, MD). Only uniquely mapped paired-end reads were used for subsequent analyses. featureCounts (WEHI Bioinformatics, Parkville, Melbourne, Victoria, Australia) was used for gene level abundance estimation using GENCODE Comprehensive gene annotations v25. Principal component analysis (PCA) was used to assess outlier samples. Genes were kept in the analysis if they had Counts Per Million (CPM) >1 in at least half the samples. Differential expression analysis comparing APOE-treated versus controls at the gene levels of summarization were then carried out using open-source Limma R package (Bioconductor).  Limma-voom was employed to implement a gene-wise linear modelling which processes the read counts into log2 counts per million (logCPM) with associated precision weights. The logCPM values were normalized between samples using trimmed mean of M-values (TMM). Adjustment for multiple testing was performed by reporting the false discovery rate (FDR) q-values for each feature. Features with q-value <5% were regarded as genome-wide significant. R statistical software environment with the GAGE Bioconductor packages was used to carry out analyses on pre-defined gene ontology (GO) gene sets by conducting two sample t-tests on the log-based fold changes of target gene set and control sets. The GO categories included were Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). FDR q-values were estimated to correct the p-values for the multiple testing issue.
identifiers.namespace: GEO
identifiers.id: GSM3677718