# Deseqdataset Construction From Quantification

> Use when you have transcript-level quantification files (quant.sf, kallisto abundance.h5, or RSEM output) from one or more RNA-seq samples and need to construct a count matrix for differential expression analysis.

- Skill: `holobiomicslab/deseqdataset-construction-from-quantification` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/deseqdataset-construction-from-quantification`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/deseqdataset-construction-from-quantification/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/deseqdataset-construction-from-quantification

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# DESeqDataSet Construction from Quantification

## Summary

Convert transcript-level quantification files (from Salmon, kallisto, or RSEM) into a DESeqDataSet object suitable for differential expression testing. This skill bridges quantification tools and DESeq2 by aggregating transcript abundance to gene level and wrapping the result in a metadata-aware R object.

## When to use

You have transcript-level quantification files (quant.sf, kallisto abundance.h5, or RSEM output) from one or more RNA-seq samples and need to construct a count matrix for differential expression analysis. Use this when you want un-normalized estimated gene-level counts paired with sample metadata and a statistical design formula.

## When NOT to use

- You already have a gene-level count matrix from HTSeq, featureCounts, or similar; use DESeqDataSetFromMatrix() instead.
- You have quantification data imported via tximeta (which produces a SummarizedExperiment); construct DESeqDataSet directly from the SummarizedExperiment with design formula.
- Your input files are alignment BAM files rather than quantification files; use featureCounts or HTSeq to produce counts first.

## Inputs

- transcript-level quantification files (Salmon quant.sf.gz, kallisto abundance.h5, or RSEM .genes.results)
- tx2gene mapping table (data frame with columns: transcript ID, gene ID)
- sample metadata table (data frame with sample names as row names, columns for condition/covariates)

## Outputs

- DESeqDataSet object with count matrix, sample metadata (colData), and design formula
- assay matrix containing un-normalized estimated gene-level counts

## How to apply

First, load quantification file paths and sample metadata (with condition labels and run identifiers), then construct or load a tx2gene mapping table linking transcript IDs to gene IDs. Pass quantification files and the mapping to tximport() with type='salmon' (or 'kallisto'/'rsem') to aggregate transcript abundance estimates to gene level, producing a list containing estimated counts, lengths, and offsets. Finally, construct a DESeqDataSet using DESeqDataSetFromTximport(), specifying the tximport list, sample metadata table (colData), and design formula (e.g., ~condition). Verify the result by examining the assay matrix to confirm values are un-normalized integer estimates without library-size scaling.

## Related tools

- **tximport** (Imports transcript abundance quantifications and aggregates to gene level using tx2gene mapping)
- **DESeq2** (Provides DESeqDataSetFromTximport() constructor and differential expression testing framework) — https://github.com/thelovelab/DESeq2
- **Salmon** (Upstream quantification tool producing transcript-level abundance estimates (quant.sf files))
- **kallisto** (Alternative upstream quantification tool producing transcript abundance estimates)
- **RSEM** (Alternative upstream quantification tool producing transcript abundance estimates)
- **tximportData** (Provides example Salmon quantification files and tx2gene mapping for tutorial workflows)

## Examples

```
txi <- tximport(files, type="salmon", tx2gene=tx2gene); ddsTxi <- DESeqDataSetFromTximport(txi, colData = samples, design = ~ condition)
```

## Evaluation signals

- The resulting DESeqDataSet contains a count assay with integer (or quasi-integer) values without library-size normalization or log transformation.
- Row names of the count matrix match gene IDs from the tx2gene mapping; dimensions are (genes × samples).
- colData(dds) contains all sample metadata columns and matches the order of quantification files.
- The design formula is correctly stored and includes condition or covariate variables present in colData.
- Calling assay(dds) reveals un-scaled estimated counts aggregated across all transcripts per gene.

## Limitations

- tximport produces estimated counts based on transcript-level abundance; these are not raw read counts and should not be treated as such downstream.
- The tx2gene mapping must be accurate and complete; missing or duplicate mappings will result in missing genes or gene aggregation errors.
- Design formula must reference columns in the sample metadata table; misnamed variables will cause construction or analysis to fail.
- Pre-filtering of low-count genes is recommended after DESeqDataSet construction to reduce memory use and improve statistical power, but is not performed automatically.

## Evidence

- [other] tximport ingests transcript-level quantification files and produces a list object that contains un-normalized counts aggregated to the gene level, which serves as input to DESeqDataSetFromTximport for constructing a DESeqDataSet.: "tximport ingests transcript-level quantification files and produces a list object that contains un-normalized counts aggregated to the gene level, which serves as input to DESeqDataSetFromTximport"
- [other] you could import the data with *tximport*, which produces a list, and then you can use `DESeqDataSetFromTximport()`: "you could import the data with *tximport*, which produces a list, and then you can use `DESeqDataSetFromTximport()`"
- [other] If you have performed transcript quantification (with *Salmon*, *kallisto*, *RSEM*, etc.) you could import the data with *tximport*: "If you have performed transcript quantification (with *Salmon*, *kallisto*, *RSEM*, etc.) you could import the data with *tximport*"
- [other] ddsTxi <- DESeqDataSetFromTximport(txi, colData = samples, design = ~ condition): "ddsTxi <- DESeqDataSetFromTximport(txi, colData = samples, design = ~ condition)"
- [other] txi <- tximport(files, type="salmon", tx2gene=tx2gene): "txi <- tximport(files, type="salmon", tx2gene=tx2gene)"