Plan an RNA-seq differential expression analysis

Help me plan and run an RNA-seq differential gene expression analysis. Organism and reference: [e.g. "human, GRCh38 + GENCODE"] Experimental design: [conditions, number of biological replicates per group, any batches, paired samples] Library and sequencing: [polyA or rRNA-depleted, stranded or not, single or paired-end, read length, depth] Data I have now: [raw FASTQ / count matrix / aligned BAMs] Tools and environment: [R, Python, command line, HPC, Galaxy] Main question: [e.g. "which genes change between treated and control at 24h"] Give me: 1. Design check: whether my replicates and batches support the comparison I want, and the design formula to use (e.g. ~ batch + condition). 2. Workflow with a recommended tool at each step and why: QC (FastQC/MultiQC), trimming if needed, alignment or pseudo-alignment, quantification, import into R. 3. QC checkpoints: what to look at (mapping rate, library size, PCA, sample clustering, outliers) and what values should worry me. 4. Differential expression: DESeq2 or edgeR/limma-voom for my design, filtering low counts, shrinkage of fold changes, multiple testing correction, and how to set thresholds. 5. Visualization: PCA, MA plot, volcano plot, heatmap of top genes, with example code. 6. Next steps: enrichment analysis options and how to interpret them cautiously. 7. Pitfalls: confounded batches, too few replicates, using normalized counts as input to DESeq2, over-reading p-values. Write code in [R/Python] with comments, and keep everything scripted so the analysis is reproducible. Do not invent results; tell me what to check in my own output.