Integrate DESeq2, edgeR, and limma-voom with deep learning ensemble models to identify differentially expressed genes with improved sensitivity and statistical rigor across complex RNA-seq experiments.
Enginoma combines proven statistical frameworks with deep learning to deliver robust gene expression profiling across diverse experimental designs.
Our AI ensemble integrates DESeq2, edgeR, and limma-voom, applying the most appropriate method based on your dataset characteristics, sequencing depth, and experimental design.
Our deep learning models are trained on validated RNA-seq datasets to improve detection sensitivity for lowly expressed genes and complex differential expression patterns.
Dedicated bioinformatics team provides consultation on experimental design, data interpretation, and downstream functional analysis to support your research goals.
Every analysis is grounded in established statistical methods, enhanced by AI models trained on validated benchmarks.
DESeq2 models count data using negative binomial distributions with shrinkage estimators for dispersion and fold-change estimation, providing robust differential expression analysis for RNA-seq experiments with biological replicates.
edgeR applies empirical Bayes methods to moderate gene-wise dispersion estimates, making it particularly effective for experiments with limited replicates and complex group structures.
The voom method transforms RNA-seq read counts to log-counts-per-million and estimates mean-variance relationships to generate precision weights, enabling limma's established linear model framework for RNA-seq analysis.
AI ensemble models trained on validated RNA-seq benchmarks learn expression pattern characteristics to complement statistical inference, improving detection sensitivity across diverse gene expression profiles.
A structured, quality-controlled pipeline from data preprocessing through differential expression to downstream functional annotation.
Quality assessment of raw sequencing reads, adapter trimming, and alignment to reference genome or transcriptome.
Quantification of gene-level or transcript-level read counts using established tools such as featureCounts or STAR.
Library size normalization, TMM, or DESeq2 median-of-ratios methods to ensure accurate comparison across samples.
Multi-method differential expression analysis with DESeq2, edgeR, and limma-voom, combined with deep learning model scoring.
Every analysis includes standardized outputs with full documentation for your research and publication needs.
Complete differential expression tables with log2 fold changes, p-values, and FDR-corrected q-values across all comparisons. Multiple testing correction applied using the Benjamini-Hochberg procedure.
Publication-quality plots including volcano plots, MA plots, heatmaps of top differentially expressed genes, and PCA or MDS plots for sample-level quality assessment.
Downstream functional analysis including GO term enrichment, KEGG pathway analysis, Reactome pathway mapping, and Gene Set Enrichment Analysis (GSEA) to identify biologically relevant processes and pathways.
Full documentation of the analysis pipeline including QC metrics, normalization details, software versions, statistical parameters, and methodological notes suitable for Methods sections in publications.
We support a wide range of input formats from all major sequencing platforms to accommodate diverse study designs.
FASTQ and FASTA files from Illumina, Ion Torrent, PacBio Sequel, and Oxford Nanopore platforms. Both single-end and paired-end formats are supported.
Pre-aligned BAM/SAM files from any standard aligner including STAR, HISAT2, TopHat, and BWA-MEM for direct gene expression quantification.
Pre-computed gene count matrices, TPM (Transcripts Per Million), and FPKM (Fragments Per Kilobase Million) tables ready for differential expression analysis.
Our analysis framework is grounded in peer-reviewed literature from the bioinformatics and RNA-seq communities.
Li D, Zand MS, Dye TD, Goniewicz ML, Rahman I, Xie Z. An evaluation of RNA-seq differential analysis methods. PLoS One. 2022;17(9):e0264246. PMID: 36112652
View on PubMed →Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141. PMID: 34557778
View on PubMed →Nguyen HCT, Baik B, Yoon S, Park T, Nam D. Benchmarking integration of single-cell differential expression. Nature Communications. 2023;14(1):1570. PMID: 36944632
View on PubMed →Xue J, et al. Comparative study on differential expression analysis methods for single-cell RNA sequencing data with small biological replicates. PLoS One. 2024;19(3):e0299358. PMID: 38536877
View on PubMed →Our deep learning models integrate multiple statistical frameworks including DESeq2, edgeR, and limma-voom, and learn from validated datasets to improve detection sensitivity. This ensemble approach helps maintain statistical rigor while identifying differentially expressed genes across complex RNA-seq experiments.
We accept raw sequencing data (FASTQ, FASTA) and pre-processed data including count matrices, TPM/FPKM tables, and aligned BAM files from all major sequencing platforms including Illumina, Ion Torrent, PacBio, and Oxford Nanopore.
The Enginoma platform supports complex designs including paired samples, time series, nested designs, and multi-factor experiments. The system applies appropriate statistical models and provides confidence intervals for all reported genes.
Standard differential expression analysis timelines vary based on project complexity and data volume. Complex multi-condition studies may require additional processing time. We also offer prioritized services for time-sensitive projects.
Yes, differential expression analyses can include pathway enrichment analysis (KEGG, Reactome, GO), gene set enrichment analysis (GSEA), network analysis, and functional annotation. Custom downstream analyses are available upon request.
Contact us today to discuss your differential expression analysis needs and discover how our AI-powered platform can support your study.
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