# Normalizing RNA-seq Data with Housekeeping Genes: Why It's Risky

Housekeeping genes are transcripts assumed to be expressed at roughly constant levels across cell types, tissues, and experimental conditions. Normalizing RNA-seq data with housekeeping genes means adjusting every sample's read counts so that the measured abundance of a chosen set of these genes comes out equal across samples, instead of using the statistical normalization methods designed for sequencing data.

The idea is tempting because it feels familiar. Anyone trained on RT-qPCR learned to normalize target genes to a reference gene such as GAPDH or ACTB, and importing that habit into RNA-seq analysis seems harmless. It is not harmless. The housekeeping assumption fails often enough, and in ways that are hard to detect, that a small set of "stable" genes can quietly distort fold changes, inflate false positives, and produce the kind of rna seq error that only surfaces months later during review. This article explains what the method actually does, why the assumption behind it breaks down in sequencing data, where it can still be defended, and what to use instead.

## What Does RNA-seq Normalization with Housekeeping Genes Mean?

Before evaluating the risk, define the operation precisely. Suppose you have a raw count matrix with genes in rows and samples in columns. Raw counts are not comparable across samples for two reasons. First, samples have different library sizes, meaning different total numbers of sequenced reads. Second, the composition of each library differs, because a few highly expressed transcripts can consume a large share of the reads in one sample but not another. Normalization exists to remove these technical differences while preserving real biological differences in expression.

Housekeeping-gene normalization approaches this with a specific assumption: that a defined set of genes, typically classic metabolic and structural transcripts such as GAPDH, ACTB, B2M, HPRT1, or ribosomal protein genes, is expressed identically in every sample. Under that assumption, any difference in these genes' observed counts must be technical. The workflow is simple:

1. Select one or more housekeeping genes.
2. Compute each sample's counts for those genes, often the [geometric mean](/blog/data-analysis/geometric-mean-definition-formula) across the set.
3. Divide each sample's counts by its housekeeping value to produce a scale factor.
4. Multiply or divide all genes in that sample by the same factor so the housekeeping signal becomes identical across samples.

The critical phrase is "under that assumption." Every other gene in the dataset inherits whatever error is built into the scale factor. If the chosen genes actually drop 20 percent in your treated samples, every genuinely upregulated gene appears 20 percent less upregulated than it is, and every downregulated gene appears more downregulated. The error is multiplicative, genome-wide, and invisible unless you go looking for it. That is the core reason [rna-seq normalization](/blog/guides/rna-seq-normalization) housekeeping genes interpretation is considered risky by analysts who work with sequencing data regularly.

## How Housekeeping Normalization Differs From Standard RNA-seq Methods

![Comparison of housekeeping scaling, TMM, median-of-ratios and TPM on what they scale to and their main failure.](/article-images/illustrations/rna-seq-normalization-housekeeping-genes-method-assumptions.webp)

A comparison table helps separate the method from the alternatives it is most often confused with.

| Approach | What it scales to | Core assumption | Typical use | Main failure mode |
|--|--|--|--|--|
| Housekeeping-gene scaling | Counts of a few presumed invariant genes | Selected genes are truly invariant | Rare targeted panels, qPCR-style comparisons | Selected genes vary biologically, noisy scale factors |
| Total-count (CPM) scaling | Library size | All genes shift proportionally with depth | Coarse QC only | Composition bias from highly expressed genes |
| TMM (edgeR) | Trimmed mean of log fold changes between sample pairs | Most genes are not differentially expressed | Bulk differential expression [1] | Fails under massive global shifts in one direction |
| Median-of-ratios (DESeq2) | Median ratio against a pseudo-reference sample | Most genes are not differentially expressed | Bulk differential expression | Same global-shift caveat as TMM |
| TPM/RPKM | Gene length and library size | Used within a sample for relative abundance | Visualizing within-sample expression | Not a between-sample normalization for differential testing |
| Spike-in normalization | Added external RNA molecules | Spike-ins represent cell/RNA input faithfully | Absolute-scale questions, global-shift designs | Spike-in handling artifacts, requires careful protocol |
| GC-content and lane-aware methods (EDASeq) | Within-lane GC bias correction plus between-lane scaling | GC bias is sample-specific and correctable | Datasets with GC artifacts | Requires modeling, adds complexity [2] |

Two of these deserve special attention because they are the actual standards in bulk [RNA-seq differential expression](/knowledge/bioinformatics/rna-seq-differential-expression-deseq2-edger). TMM, the Trimmed Mean of M-values implemented in edgeR, and the median-of-ratios method in DESeq2 both estimate scale factors from the bulk of the distribution rather than from a handful of genes. They assume most genes are unchanged, an assumption that is statistically far more robust than assuming a pre-specified list of genes is unchanged, because the assumption is averaged over thousands of transcripts instead of resting on five or ten. A recent Parkinson's disease classification workflow, for example, used TMM normalization followed by voom transformation and limma-based differential expression as its [preprocessing](/blog/data-analysis/what-is-data-preprocessing) backbone [1].

## Where the Housekeeping Habit Comes From, and Why It Does Not Transfer

Housekeeping normalization is a qPCR reflex, and understanding the qPCR context explains both its appeal and its limits.

### The qPCR tradition

In quantitative PCR, expression of a target gene is expressed relative to one or more reference genes using the delta-delta Ct method. The method requires reference genes whose Ct values do not change across your conditions. Decades of practice produced an informal canon of "stable" genes, and an equally established discipline of validating them. Three software algorithms dominate this validation literature: geNorm, which measures how co-regulated candidate genes are and derives an optimal pair, NormFinder, which estimates intra- and intergroup variance, and BestKeeper, which compares candidate stability against a composite index [3].

The validation studies themselves are the best evidence against casual use. A study of reference genes for whole blood expression analysis in neonates and adults tested eight classic candidates, including ACTB, B2M, GAPDH, GUSB, HPRT, PPIB, RPLP0, and RPL13. Every candidate except RPLP0 showed significant intergroup expression variance and higher expression with older age, producing a small but systematic bias. The study concluded that usable normalization factors exist, such as ACTB with PPIB for comparing neonatal samples across gestational ages, but only after systematic validation in the specific sample type [3].

### The RNA-seq reality

Sequencing changes the problem in three ways that break the qPCR logic.

**First, the signal-to-noise structure is different.** In qPCR, a reference gene's Ct is measured with high precision from a single amplification reaction. In RNA-seq, a gene's count is a sample from a negative binomial-like distribution, and counts of even moderately expressed genes carry enough sampling noise that a scale factor built from five genes is far noisier than one built from the whole transcriptome. Your normalization constant inherits the Poisson-level noise of those few genes, which then propagates into every gene's normalized value.

**Second, the candidate gene list is drawn from the wrong evidence base.** Reference gene studies are usually conducted by qPCR in specific tissues. Genome-wide surveys of transcript composition show that expression patterns are strongly tissue-specific and that even highly expressed, top-ranked transcripts dominate different fractions of the transcriptome in different tissues [4]. A gene that is stable across ten liver samples can vary sharply between liver and brain, or between developing and adult tissue. A silkworm study screening the entire developmental cycle from transcriptomic data found that although nine candidate reference genes looked promising by [coefficient of variation](/blog/research-skills/how-to-calculate-coefficient-of-variation), no single gene was universally stable across all stages, and the optimal pair identified by geNorm differed from naive expectations. Normalizing with the traditional actin control alone performed measurably worse than the validated dual-reference pair [5].

**Third, "housekeeping" is not a biological guarantee, it is a label.** The head and neck squamous cell carcinoma literature provides a striking example. A multi-platform search for stable internal control genes, spanning microarrays, published qPCR data, and TCGA RNA-seq, narrowed 18 discovery candidates down to five validated genes: RPL30, RPL27, PSMC5, MTCH1, and OAZ1. RPL30 and RPL27 were the most stable and abundantly expressed across tissues [6]. Notice what is missing from that validated list: GAPDH and ACTB, the two genes most commonly used by default. The candidates that survive validation are rarely the ones people reach for first, and the set is tissue-specific by construction.

## The Four Failure Modes, Explained

The risk in normalizing rna-seq data with housekeeping genes concentrates into four distinct, diagnosable failure modes.

### Failure mode 1: The genes are differentially expressed

This is the fatal one. If any selected housekeeping gene genuinely changes between your conditions, the resulting scale factor encodes a biological signal and subtracts it from everything else. HPRT1, a canonical housekeeping gene, turned out to be an independent prognostic factor in oral squamous cell carcinoma, with knockdown suppressing proliferation and migration [7]. A gene like that is not a ruler, it is a variable. Normalizing an OSCC dataset to HPRT1 would partially remove the very signal that predicts survival.

Developmental and stress-response experiments make this failure mode nearly certain. Drought-stressed mulberry seedlings showed coordinated transcriptional responses across ABA, gibberellin, and brassinosteroid signaling pathways within days of stress [8]. Embryonic development reshapes expression in waves, with lens-associated [structural genes](/blog/guides/structural-genes) in seabass embryos shifting dramatically between mid-gastrula and late somitogenesis stages, and principal components separating samples almost entirely by developmental stage [9]. Any of the classical metabolic housekeeping genes can be caught in these waves. The stress response does not politely exclude GAPDH.

### Failure mode 2: Composition bias is not a housekeeping problem

The second failure mode is subtler. Suppose your housekeeping genes really are invariant. Scaling to them still leaves you exposed to composition effects. If one condition contains a handful of transcripts that soak up 40 percent of the library while the other contains none, then the reads available for every other gene, including your perfectly stable housekeeping genes, change relative to absolute molecular abundance. Scaling so that the housekeeping genes match across samples would then distort the rest of the transcriptome in exactly the wrong direction. This is the same reason raw total-count normalization misleads, and it is why robust methods trim or take medians across the whole distribution rather than trusting any fixed subset.

### Failure mode 3: Small-gene-set noise

Even with perfectly stable, well-expressed housekeeping genes, a scale factor computed from a handful of transcripts has high variance. With a genome-wide method, the scale factor is an average over thousands of genes, so per-gene sampling noise cancels. With five genes, it does not. For lowly expressed housekeeping candidates the problem worsens, because relative count noise scales inversely with count depth. The practical consequence shows up in downstream plots: normalized values that scatter more than the raw data, differential expression hits that flip when you swap one housekeeping gene for another, and poor replicate clustering.

### Failure mode 4: Uncorrected technical biases ride along

Housekeeping scaling is a single multiplicative adjustment per sample. It does nothing for gene-specific technical biases. Strong sample-specific GC-content effects on read counts are well documented and can substantially bias differential expression results, which is why within-lane GC-content normalization followed by between-lane scaling was developed and implemented in the EDASeq package, with demonstrated improvements in fold-change accuracy and error control [2]. A housekeeping-based scale factor also does not address the problem that library size effects can persist even after standard genome-wide correction. A systematic analysis of more than 100 publicly available GEO and TCGA datasets with raw counts found that a substantial proportion of genes remain correlated with library size after state-of-the-art normalization, motivating the gecco method, which applies a gene-specific exponent correction rather than a uniform scale factor [10]. If genome-wide methods leave residual library size signal, a five-gene scale factor leaves more.

## A Practical Workflow for Normalizing Bulk RNA-seq Data

![Six-step flow from raw counts through filtering, TMM or DESeq2 scaling, bias correction, QC and interpretation.](/article-images/illustrations/rna-seq-normalization-housekeeping-genes-normalization-workflow.webp)

This is the workflow that replaces housekeeping scaling in routine analysis, with the reasoning attached at each step. The commands use R and Bioconductor, the standard environment for this work.

### Step 1: Start from raw counts

Obtain gene-level raw counts, not pre-normalized values. Alignment- or pseudoalignment-based pipelines producing a count matrix per sample are the input that normalization methods expect. If you download TCGA or GEO data, fetch the raw or upper-quartile-unnormalized counts when available, because methods like TMM and median-of-ratios need to see the full count distribution to estimate their factors.

### Step 2: Filter low-count genes

Remove genes with very low counts across all samples, for example genes that fail to reach a minimal count threshold in the smallest group. This increases [statistical power](/blog/guides/statistical-power-what-it-is-and-why-it-matters-in-research), and it matters more than most beginners expect, because low-count genes dominate the multiple-testing burden with nothing to show for it.

### Step 3: Choose a genome-wide scale factor method

In edgeR:

```r
library(edgeR)
y <- DGEList(counts = counts, group = group)
keep <- filterByExpr(y, group = group)
y <- y[keep, , keep.lib.sizes = FALSE]
y <- calcNormFactors(y, method = "TMM")
```

In DESeq2:

```r
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = counts,
                              colData = coldata,
                              design = ~ condition)
dds <- DESeq(dds)
```

DESeq2 applies median-of-ratios size factors internally. Both methods assume that most genes are not differentially expressed between conditions. If your experiment involves a treatment known to produce a global transcriptional shift, such as a severe stress or a broad transcription-factor perturbation, this assumption needs explicit checking, and spike-in-based approaches or compositional methods become the better conversation.

### Step 4: Check and correct gene-specific biases where needed

GC-content bias is sample-specific and strong enough to distort fold-change estimates and inflate false positives when ignored [2]. The EDASeq package provides exploratory plots to detect the bias and within-lane normalization to correct it:

```r
library(EDASeq)
plotBias(data, plot = "GC")
dataNorm <- withinLaneNormalization(data, which = "gc", offset = TRUE)
dataNorm <- betweenLaneNormalization(dataNorm, which = "median", offset = TRUE)
```

For residual library size effects, which persist after standard correction in a substantial fraction of genes across large public datasets, gecco offers a gene-specific correction framework that can be evaluated on your dataset, particularly in large-scale projects where subtle depth correlation matters for interpretation [10].

### Step 5: Run standard QC on the normalized data

Normalization is a hypothesis about your data, so test it. Three checks cover most cases:

- **PCA or MDS on log-transformed normalized values.** Replicates should cluster by biological condition. If they cluster by library size, batch, or sequencing lane, normalization failed and you have a bioinformatics troubleshooting session ahead of you.
- **MA plots per sample.** Most points should sit near zero log fold change against a reference sample, with no funnel-shaped artifacts.
- **Residual library size correlation.** Correlate normalized expression of each gene with library size. Genome-wide methods leave some residual signal in large datasets [10], so the question is not "is there any" but "is it widespread enough to worry about."

### Step 6: Interpret in context

Differential expression tables are the beginning of interpretation, not the end. Tools like GeneTonic integrate normalized expression values, differential expression results, and enrichment analyses into linked interactive views so that a reviewer can drill from a pathway hit down to individual genes and their expression context [11]. Interpretation steps are also where housekeeping-gene intuitions can still add value, as sanity checks rather than as normalizers, which is covered below.

## Where Housekeeping Genes Still Have Legitimate Roles

The honest position is not that housekeeping genes are useless. It is that their use case in RNA-seq is narrower than habit suggests.

**Targeted, validated contexts.** When a study is restricted to a small panel of genes measured in a defined tissue and condition set, and the reference genes have been validated in that exact context with geNorm, NormFinder, or BestKeeper, a reference-based scaling can be defensible. The head and neck cancer work is a model of how this should be done: multi-platform discovery, four stability algorithms, and qPCR validation before the minimal set of RPL30, RPL27, PSMC5, MTCH1, and OAZ1 was proposed [6]. Note the investment required. That is what it costs to make a housekeeping claim credible, and the result is tissue-specific.

**Quality control and sanity checks.** Housekeeping genes make good canaries. If your genome-wide normalization is correct, genes that are broadly stable across samples should show near-zero log fold changes between conditions. If GAPDH and RPLP0 show up with large apparent fold changes after TMM normalization, that is not automatically a normalization failure, it may be biology, but it deserves a look. Using them this way, as diagnostics rather than as the denominator, is the correct transfer of qPCR experience into sequencing analysis.

**Bridging platforms.** In mixed qPCR and RNA-seq studies, validated reference genes serve to connect targeted qPCR validation of a few genes to the genome-wide sequencing results. The reference genes calibrate the qPCR side, while the RNA-seq side uses its own genome-wide normalization. The two arms do not need to share a normalization scheme.

**Annotation-aware interpretation.** Knowing which transcripts dominate the library in your tissue is itself informative. Analyses of GTEx data show that top-ranked protein-coding transcripts occupy a large fraction of the transcriptome and that some dominant transcripts are noncoding splice isoforms, which affects how expression budgets are interpreted [4]. A normalization strategy that ignores which transcripts are eating the library is missing part of the picture.

## Comparative and Experimental Relevance

The stakes differ by experimental design, and worth stating plainly.

**Grouped differential expression designs** (treated versus control, tumor versus normal) are the standard case, and genome-wide robust methods are appropriate. The primary risk of housekeeping normalization here is silent fold-change distortion when the selected genes happen to be condition-responsive, which the validation literature suggests is common rather than exceptional [3][7].

**Time-course and developmental designs** amplify every failure mode. Stage-specific transcriptional variation is the norm, whether in insect development [5], embryogenesis [9], or stress-response time courses [8]. The silkworm finding that no single reference gene was stable across all developmental stages, and that the optimal pair changed by stage, generalizes: in dynamic systems, the stability of any pre-selected gene list should be treated as an empirical question with a likely negative answer.

**Cross-tissue and disease studies** face the tissue-specificity problem directly. GTEx-based analyses demonstrate that transcript composition, including which isoforms dominate, differs by tissue [4], so a reference set validated in blood cannot be exported to liver without revalidation. Large-scale reanalyses of public data add a further wrinkle, since residual library size effects persist across many datasets even after mainstream normalization [10], meaning downstream claims should be robust to the normalization choice rather than dependent on it.

**Machine learning on RNA-seq features**, an increasingly common downstream use, inherits whatever distortion the normalization introduced. Gene selection algorithms evaluated across TCGA-derived scenarios depend on pre-processing steps including filtering, normalization, and transformation before selection even begins [12]. A biased scale factor changes which features appear informative, and the classifier's apparent performance can encode the normalization artifact rather than biology.

## Misconceptions Students Most Often Hold

**"GAPDH is housekeeping, so it is safe everywhere."** The validated reference gene literature repeatedly excludes GAPDH from the stable set, and genes as canonical as HPRT1 can be condition-responsive and even clinically predictive [6][7]. "Housekeeping" describes intent, not measured invariance.

**"Normalization with a few stable genes is more conservative than statistical methods."** It is the opposite. Genome-wide robust methods average the assumption over thousands of genes, which makes them harder to corrupt by any one gene's behavior. A five-gene scale factor concentrates all the risk in five draws from a noisy distribution.

**"TPM normalizes my data for differential expression."** TPM accounts for gene length and library size within a sample, which makes it useful for comparing relative abundance across genes, but it is not the sample-level scaling that differential expression models require. Comparing TPMs across samples for DE testing is a well-known source of spurious results.

**"If the housekeeping genes look stable on a plot, the normalization is valid."** Stability observed across your samples is necessary but not sufficient. You also need the scale factor to be precise, which fails for small gene sets, and you need gene-specific technical biases such as GC effects handled separately [2].

**"Differential expression results will tell me if normalization is wrong."** Most of the time they will not. A normalization bias that is uniform across a condition shifts fold changes coherently and can look like a clean, believable result. Only explicit diagnostics, PCA, MA plots, and residual library size correlation [10], expose it.

## Quick Review

- Housekeeping-gene normalization scales each sample so a chosen set of presumed invariant genes matches across samples, transferring an assumption from qPCR into a setting where it usually fails.
- Validation studies with geNorm, NormFinder, and BestKeeper show that classic candidates, including ACTB, GAPDH, and HPRT1, often vary between groups, tissues, developmental stages, and disease states [3][6][7][5].
- If a selected gene is differentially expressed, the resulting scale factor subtracts real biology from every gene in the dataset, multiplicatively and silently.
- A scale factor from a handful of genes carries far more sampling noise than genome-wide methods like TMM in edgeR or median-of-ratios in DESeq2 [1].
- Housekeeping scaling does nothing for gene-specific GC-content bias, which is sample-specific and correctable with within-lane methods in EDASeq [2].
- Residual library size effects survive even mainstream genome-wide normalization in a substantial proportion of genes across large public datasets, and a small-gene scale factor does not improve on this [10].
- Legitimate modern roles for housekeeping genes are QC diagnostics and platform bridging, plus reference-based scaling only in small targeted panels after context-specific validation.

## Common Mistakes and Limitations

**Using an unvalidated default list.** The single most common mistake in this area is reaching for GAPDH, ACTB, or 18S rRNA because a 2005 paper used them. Validation is context-specific, and the genes that pass validation in one tissue often fail in another [6][5]. If you have not validated stability in your tissue and condition set, you have no evidence.

**Normalizing to genes inside the comparison of interest.** Any gene included in the scale factor cannot be interpreted afterward. If your housekeeping gene is induced by your treatment, you have converted a biological finding into a technical adjustment and distributed the distortion across the table.

**Applying qPCR delta-delta Ct logic to genome-wide counts.** The delta-delta Ct method compares a target to a reference within a sample. RNA-seq differential expression compares expression between samples across genes, with count-level noise models. The statistical structures differ, and mixing them produces results that neither framework can support.

**Trusting one QC plot.** A clean PCA is reassuring but insufficient. MA plots, residual library size correlation, and examination of known stable genes together give a defensible picture [10]. A single passing check is not evidence of correct normalization, it is the absence of one specific failure.

**Ignoring global-shift designs.** TMM and median-of-ratios assume most genes are unchanged. Experiments with expected global shifts, such as severe stress or broad transcriptional reprogramming, violate this and need spike-in-aware or composition-aware strategies. The workflow must match the biology, not the other way around.

**Reanalyzing pre-normalized public data with a second method.** Applying TMM to values that were already TPM-normalized stacks two incompatible transformations. Normalize raw counts with one coherent pipeline, or document exactly what the deposited values represent before deciding what they can support.

**Expecting small sample sizes to rescue the situation.** With five samples per group, the noise in a housekeeping-derived scale factor is proportionally larger, and the genome-wide assumption of "most genes unchanged" is harder to verify. Small studies should lean on the most robust normalization available, not on the least.

One general limitation applies to everything above: specific datasets, designs, and biological systems vary, and a qualified bioinformatician or the analyst responsible for a given study should review the actual data before a normalization strategy is finalized.

## Frequently Asked Questions

### What does rna-seq normalization with housekeeping genes mean?

It means adjusting each sample's gene counts so that the read counts of a small set of genes presumed to be constantly expressed come out equal across samples, then applying the resulting scale factor to every gene. It is a qPCR-style reference-gene approach transplanted into sequencing data, and its validity rests entirely on the presumption that the selected genes do not actually change between your conditions.

### Is GAPDH a safe housekeeping gene for RNA-seq normalization?

No, not by default. GAPDH is condition- and tissue-responsive in many validated datasets, and formal reference gene studies frequently exclude it from their recommended normalization factors [6]. If you want to use it, demonstrate with your own data, across your own conditions, that its expression is stable, and even then prefer genome-wide normalization for differential expression.

### When can normalizing RNA-seq data with housekeeping genes be justified?

In small targeted panels measured in a specific tissue and condition set where the reference genes were validated in that exact context using tools such as geNorm, NormFinder, or BestKeeper [6][5]. It can also be justified for platform bridging between qPCR and sequencing arms of a study. It is not justified as a general substitute for genome-wide normalization in bulk RNA-seq differential expression.

### What should I use instead of housekeeping genes for RNA-seq normalization?

Use raw counts, filter low-count genes, then apply TMM in edgeR or median-of-ratios in DESeq2, or voom normalization with limma [1]. Check for GC-content bias and correct it with within-lane methods in EDASeq where present [2]. For large-scale analyses where residual library size correlation matters, consider gene-specific correction approaches such as gecco [10].

### How do I know whether my RNA-seq normalization worked?

Run explicit diagnostics: PCA or MDS on log-normalized values should cluster replicates by biology rather than by library size or batch, MA plots against a reference sample should center near zero, and correlations between normalized expression and library size should be minimal across genes [10]. Housekeeping genes can serve as one of these checks, confirming that broadly stable transcripts show near-zero fold changes, but they cannot serve as the normalization itself.

### Does the housekeeping gene problem apply to single-cell RNA-seq?

It applies more strongly. Single-cell data suffer from dropout and extreme sparsity, so counts for any individual gene are noisy or absent in many cells, making a housekeeping-derived scale factor even less reliable than in bulk data. Single-cell workflows use their own normalization frameworks, and reference-gene-style scaling is not part of standard practice there.

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## Sources

1. [Trans-vatabnet: transcriptomic signatures for Parkinson's disease detection using deep learning.](https://pubmed.ncbi.nlm.nih.gov/42873480/)
2. [GC-content normalization for RNA-Seq data.](https://pubmed.ncbi.nlm.nih.gov/22177264/)
3. [Validation of reference genes for whole blood gene expression analysis in cord blood of preterm and full-term neonates and peripheral blood of healthy adults.](https://pubmed.ncbi.nlm.nih.gov/34193041/)
4. [Top-ranked expressed gene transcripts of human protein-coding genes investigated with GTEx dataset.](https://pubmed.ncbi.nlm.nih.gov/33004865/)
5. [Selection and evaluation of dual reference genes in silkworm (Bombyx mori) based on transcriptome data.](https://pubmed.ncbi.nlm.nih.gov/42313805/)
6. [A minimal set of internal control genes for gene expression studies in head and neck squamous cell carcinoma.](https://pubmed.ncbi.nlm.nih.gov/30128175/)
7. [Prognostic Value of Necroptosis-Related Genes Signature in Oral Squamous Cell Carcinoma.](https://pubmed.ncbi.nlm.nih.gov/37760507/)
8. [Comprehensive RNA-Seq analysis revealed molecular pathways and genes associated with drought tolerance in Morus alba cv. Yunsang-2.](https://pubmed.ncbi.nlm.nih.gov/42458250/)
9. [Stage-Specific Expression of Lens-Associated Structural Genes During Early Embryogenesis in European Seabass (Dicentrarchus labrax).](https://pubmed.ncbi.nlm.nih.gov/42195049/)
10. [Gene-specific exponent-corrected normalization for library size in bulk RNA-seq.](https://pubmed.ncbi.nlm.nih.gov/42465367/)
11. [GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.](https://pubmed.ncbi.nlm.nih.gov/34949163/)
12. [A research on machine learning based gene selection algorithms for RNA-seq data.](https://pubmed.ncbi.nlm.nih.gov/42878693/)