Update output.md

Clarify output files:
- add gene_lengths description
- clarify that scaled output files contain estimated counts from abundances

Author: pmoris

docs/output.md

@@ -682,9 +682,10 @@ The principal output files are the same between Salmon and Kallsto:
   - `<pseudo_aligner>.merged.gene_counts.tsv`: Matrix of gene-level raw counts across all samples.
   - `<pseudo_aligner>.gene_tpm.tsv`: Matrix of gene-level TPM values across all samples.
   - `<pseudo_aligner>.gene_counts.rds`: RDS object that can be loaded in R that contains a [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) container with the TPM (`abundance`), estimated counts (`counts`) and transcript length (`length`) in the assays slot for genes.
-  - `<pseudo_aligner>.merged.gene_counts_scaled.tsv`: Matrix of gene-level library size-scaled counts across all samples.
+  - `<pseudo_aligner>.merged.gene_lengths.tsv`: Matrix of average within-sample transcript lengths for each gene across all samples, which can be used as an offset for gene-level analysis.
+  - `<pseudo_aligner>.merged.gene_counts_scaled.tsv`: Matrix of gene-level library size-scaled estimated counts from abundances across all samples.
   - `<pseudo_aligner>.merged.gene_counts_scaled.rds`: RDS object that can be loaded in R that contains a [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) container with the TPM (`abundance`), estimated library size-scaled counts (`counts`) and transcript length (`length`) in the assays slot for genes.
-  - `<pseudo_aligner>.merged.gene_counts_length_scaled.tsv`: Matrix of gene-level length-scaled counts across all samples.
+  - `<pseudo_aligner>.merged.gene_counts_length_scaled.tsv`: Matrix of gene-level length-scaled estimated counts from abundances across all samples.
   - `<pseudo_aligner>.merged.gene_counts_length_scaled.rds`: RDS object that can be loaded in R that contains a [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) container with the TPM (`abundance`), estimated length-scaled counts (`counts`) and transcript length (`length`) in the assays slot for genes.
   - `<pseudo_aligner>.merged.transcript_counts.tsv`: Matrix of isoform-level raw counts across all samples.
   - `<pseudo_aligner>.merged.transcript_tpm.tsv`: Matrix of isoform-level TPM values across all samples.
@@ -727,7 +728,7 @@ The [tximport](https://bioconductor.org/packages/release/bioc/html/tximport.html
 
 According to the [`txtimport` documentation](https://bioconductor.org/packages/release/bioc/vignettes/tximport/inst/doc/tximport.html#Downstream_DGE_in_Bioconductor) you can do one of the following:
 
-- Use the original (bias-uncorrected) counts _with an offset_: import all the salmon `quant.sf` files with `tximport` and then use `DESeq2` with `dds <- DESeqDataSetFromTximport(txi, sampleTable, ~condition)` to automatically construct an object with gene-level offsets in-built, which can be used by DESeq2 to automatically account for effective gene length effects across conditions. It's also possible to use the `.gene_lengths.tsv` matrices output by this workflow to make your own object the same way- which is what [the nf-core differentialabundance workflow does](https://nf-co.re/differentialabundance/1.5.0/docs/usage#outputs-from-nf-corernaseq-and-other-tximport-processed-results). See the [DESeq2 module](https://github.com/nf-core/modules/blob/c8f7f481bf2ccd8f9c7f2d499d94e74d4b9e23ab/modules/nf-core/deseq2/differential/templates/deseq_de.R#L323) for the implementation details.
+- Use the original (bias-uncorrected) counts _with an offset_: import all the salmon `quant.sf` files with `tximport` and then use `DESeq2` with `dds <- DESeqDataSetFromTximport(txi, sampleTable, ~condition)` to automatically construct an object with gene-level offsets in-built, which can be used by DESeq2 to automatically account for effective gene length effects across conditions. It's also possible to combine the `.merged.gene_counts.tsv` and `.gene_lengths.tsv` matrices output by this workflow to make your own object the same way - which is what [the nf-core differentialabundance workflow does](https://nf-co.re/differentialabundance/1.5.0/docs/usage#outputs-from-nf-corernaseq-and-other-tximport-processed-results). See the [DESeq2 module](https://github.com/nf-core/modules/blob/c8f7f481bf2ccd8f9c7f2d499d94e74d4b9e23ab/modules/nf-core/deseq2/differential/templates/deseq_de.R#L323) for the implementation details.
 - Use bias-corrected counts _without an offset_: load and use `salmon.merged.gene_counts_length_scaled.tsv` or `salmon.merged.gene_counts_scaled.tsv` directly as you would with a regular gene-level counts matrix. These files were created using the `tximport` argument `countsFromAbundance="scaledTPM"` or `"lengthScaledTPM"` to scale counts to library size or to library size and average transcript length respectively.
 - Use the original (bias-uncorrected) counts _without an offset_: load and use the `txi$counts` matrix (or `salmon.merged.gene_counts.tsv`) with `DESeq2`. This is generally **not** recommended, since it does not correct for potential differential isoform usage (the offset). However, if you have 3’ tagged RNA-seq data, then this _is_ the most suitable method, because the counts do not exhibit any length bias.