One tends to use absolute expression metrics for quality control and to guarantee statistical power - i.e., to ensure that only genes with sufficient expression are considered. One tends to apply differential expression analysis to detect significant regulatory shifts (even if the baseline is, as in this case, historical).

Why? Return to the assay itself. Exactly what is RNA sequencing? How, exactly, is it performed? Understand this and we will understand why subtle variations in a step of the RNA‑Seq workflow mean significant differences in absolute expression measurements:

1. Sample Handling and RNA Extraction: Even minor inconsistencies in how samples are collected, stored, or processed can reduce RNA quality and yield, setting the stage for skewed expression values.

2. Reverse Transcription Efficiency: Small differences in the efficiency of converting RNA to cDNA can lead to inconsistent representation of transcripts, affecting downstream quantification.

3. Library Preparation (Fragmentation, Adapter Ligation, PCR Amplification): Variability during fragmentation or adapter ligation may unevenly capture transcripts, while slight biases during PCR amplification can disproportionately boost certain fragments, all of which can distort the true abundance of transcripts.

4. Sequencing Platform and Batch Effects: Minor variations in sequencing runs or machine performance can further amplify these initial discrepancies, leading to large differences in the reported absolute expression levels.

The use and popularity of differential expression analysis is best understood as one of many techniques needed to get usable information about two conditions in a setting of such high variability, and not even the most important one ...

Again, the idea is that while the variability that can emerge from these steps is a challenge, if you:

A) Rigorously design an experiment controlled for everything but the condition

B) Process both samples the same

C) Use QC procedures to "deal with" differences in RNA integrity, library complexity, read quality, and so forth.

D) Additionally perform sophisticated within and between sample normalization procedures (like those found in DESeq or EdgeR)

E) Deal with or remove batch effects, remove outliers, and control for covariates during statistical testing

F) Analyze Differential Expression judiciously

you may be able to draw reasonable conclusions about gene expression differences between two conditions. But you aren't going to get there through absolute metrics.

In summary:

• the sensitivity of the process of sequencing itself to subtle conditions essentially precludes the use of of absolute expression metrics for anything but basic QC.
• some use them also to cut down on multiple testing penalties by removing before hand any genes whose counts are too low to offer hope of a reliable statistical comparison.

Even then, keep in mind RNA-seq results are incomplete, tend not to correlate with other metrics of interest reliably (e.g., RNA-seq doesn't predict protein quantity well), and are easily thrown off by subtle differences in assay or bioinformatic processing.