Transcript To Gene Aggregation

transcript-to-gene-aggregation

Summary

Aggregate transcript-level quantification estimates from tools like Salmon, kallisto, or RSEM to gene-level counts using a transcript-to-gene mapping table. This step produces un-normalized gene-level count matrices suitable for downstream differential expression analysis with DESeq2.

When to use

When you have transcript-level quantification files (e.g., Salmon quant.sf.gz, kallisto abundance.h5, or RSEM .results) and need to construct a gene-level count matrix for DESeq2 differential expression testing. Apply this skill before constructing a DESeqDataSet, especially when your research question targets gene-level rather than transcript-level effects.

When NOT to use

• Input is already a gene-level count matrix from htseq-count, featureCounts, or a prior aggregation step.
• Your research question specifically targets isoform-level or transcript-level differential usage (use transcript-level quantification directly instead).
• Quantification was performed with a tool not supported by tximport (e.g., proprietary formats without documented import methods).

Inputs

• transcript-level quantification files (Salmon quant.sf.gz, kallisto abundance.h5, RSEM .results, or equivalents)
• tx2gene mapping table (two-column data frame: transcript ID → gene ID)
• sample metadata table with condition/group assignments

Outputs

• tximport list object (containing $abundance, $counts, $length, $countsFromAbundance)
• gene-level count matrix (integer estimated counts)
• DESeqDataSet object (after DESeqDataSetFromTximport)

How to apply

Load transcript abundance quantifications using tximport() with the appropriate quantifier type (e.g., type='salmon') and supply a transcript-to-gene mapping table (tx2gene) that links each transcript ID to its parent gene ID. The tx2gene table is typically a two-column data frame with transcript identifiers in the first column and corresponding gene identifiers in the second. tximport() will aggregate transcript-level estimated counts to the gene level and return a list object containing gene-level counts, effective lengths, and offsets. These un-normalized estimated counts are then passed directly to DESeqDataSetFromTximport() along with sample metadata and a design formula to construct the DESeqDataSet for statistical testing. Verify that the resulting count matrix contains integer-like estimated counts without library-size scaling or normalization applied.

Related tools

• tximport (Core tool: ingests transcript-level quantification files and aggregates counts to gene level using tx2gene mapping) — https://bioconductor.org/packages/tximport
• DESeq2 (Downstream consumer: accepts tximport output via DESeqDataSetFromTximport() to construct DESeqDataSet for differential expression analysis) — https://github.com/thelovelab/DESeq2
• Salmon (Upstream quantifier: produces transcript-level abundance estimates (quant.sf.gz files) that serve as input to tximport)
• kallisto (Upstream quantifier: produces transcript-level abundance estimates (abundance.h5) that serve as input to tximport)
• RSEM (Upstream quantifier: produces transcript-level abundance estimates (.results files) that serve as input to tximport)
• tximportData (Reference data package: provides example Salmon quantification files, sample metadata, and tx2gene mapping tables for learning and testing) — https://bioconductor.org/packages/tximportData
• tximeta (Alternative: imports quantification data and automatically retrieves transcript-to-gene mappings and metadata from remote databases)

Examples

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

Evaluation signals

• The resulting tximport list object contains integer-like estimated gene counts in the $counts element, without library-size normalization or log transformation applied.
• Spot-check the tx2gene mapping: verify that all transcript IDs in the quantification files are present in the mapping table and that gene IDs are valid and non-duplicated within the mapping.
• Compare gene-level totals: sum of aggregated transcript counts for each gene should be approximately equal to the transcript-level estimates for that gene (within rounding and abundance-weighting tolerances).
• DESeqDataSet construction succeeds and the assay slot contains un-normalized integer counts; row counts should match the number of unique genes in the tx2gene mapping.
• Pre-filtering or independent filtering on the DESeqDataSet (e.g., genes with rowSums < threshold) works as expected, confirming that counts are properly structured as a numeric matrix.

Limitations

• tximport requires an exact, curated tx2gene mapping; missing or misnamed transcript IDs will result in silent loss of quantification data for those transcripts.
• Quantification uncertainty (e.g., from multi-mapping reads in Salmon) is already embedded in the estimated counts; tximport does not increase or decrease that uncertainty.
• Gene-level aggregation is irreversible; transcript-level isoform quantification information is lost after aggregation, making post-hoc transcript-level inference impossible.
• The tx2gene mapping is typically species-, genome-build-, and transcriptome-version-specific; using an incorrect or mismatched mapping will yield meaningless results.

Evidence

• [other] tximport ingests transcript-level quantification files and produces a list object that contains un-normalized counts aggregated to the gene level: "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] tximport processes transcript-level quantification files using type parameter and tx2gene mapping: "Import transcript abundance quantifications using tximport() with type="salmon" and the tx2gene mapping to aggregate to gene level, producing a list object containing estimated gene counts, lengths,"
• [other] Supported quantification tools for tximport: "If you have performed transcript quantification (with Salmon, kallisto, RSEM, etc.) you could import the data with tximport, which produces a list, and then you can use"
• [other] DESeqDataSetFromTximport constructor accepts tximport output: "Construct a DESeqDataSet from the tximport output using DESeqDataSetFromTximport(), specifying the sample metadata and design formula (~condition)"
• [other] Verification of un-normalized count values: "Verify that the resulting count matrix contains un-normalized estimated counts by examining the assay data and confirming values are integer counts without library-size scaling or normalization"