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content/03.results.md

@@ -91,9 +91,7 @@ Any cells removed after filtering empty droplets based on the unfiltered RNA cou
 The workflow calculates QC statistics for ADT counts using `DropletUtils::cleanTagCounts()` that are stored alongside the ADT by cell counts matrix in the filtered `SingleCellExperiment` object. 
 The `SingleCellExperiment` object containing the filtered RNA and ADT counts matrix and associated ADT QC statistics is saved to an `.rds` file with the suffix `_filtered.rds`.
 
-Like the RNA gene expression data, the ADT by cell counts matrix is normalized.
-<!-- TODO: Cite/briefly explain ADT normalization -->
-<!-- TODO: check if the meaning was retained in next sentence? -->
+The ADT by cell counts matrix is normalized by first determining the ambient profile and then using that profile to calculate median size factors with `scuttle::computeMedianFactors()` [@url:https://bioconductor.org/books/3.16/OSCA.advanced/integrating-with-protein-abundance.html#cite-seq-median-norm]. 
 We skip normalization for cells with low-quality ADT expression, as indicated by `DropletUtils::cleanTagCounts()`.
 Although `scpca-nf` normalizes ADT counts, the workflow does not perform any dimensionality reduction of ADT data; only the RNA counts data is used as input for dimensionality reduction.
 The normalized ADT data is saved as an `altExp` within the processed `SingleCellExperiment` containing the normalized RNA data and is output to a `.rds` file with the suffix `_processed.rds`.
@@ -117,8 +115,7 @@ As with ADT data, `scpca-nf` does not filter any cells based on HTO expression,
 `scpca-nf` does not perform any additional filtering or processing of the HTO by cell counts matrix, so the same filtered matrix is saved to the processed `.rds` file with the `_processed.rds` suffix.
 
 Although `scpca-nf` quantifies the HTO data and includes an HTO by cell counts matrix in all objects, `scpca-nf` does not demultiplex the samples into one sample per library. 
-<!-- TODO: Likely missing citations in next sentence -->
-Instead, `scpca-nf` applies multiple demultiplexing methods, including demultiplexing with `DropletUtils::hashedDrops()`, demultiplexing with `Seurat::HTODemux()`, and genetic demultiplexing when bulk RNA-seq data is available.
+Instead, `scpca-nf` applies multiple demultiplexing methods, including demultiplexing with `DropletUtils::hashedDrops()`, demultiplexing with `Seurat::HTODemux()` [@doi:10.1186/s13059-018-1603-1], and genetic demultiplexing when bulk RNA-seq data is available.
 `scpca-nf` uses the genetic demultiplexing method described in Weber et al. [@doi:10.1093/gigascience/giab062], which uses bulk RNA-seq as a reference for the expected genotypes found in each single-cell RNA-seq sample.
 The results from all available demultiplexing methods are saved in the filtered and processed `SingleCellExperiment` objects.
 
@@ -130,8 +127,7 @@ No additional plots are produced, but a table summarizing the results from all t
 
 Multiple libraries were collected for some samples, with the additional libraries being used for bulk RNA-seq and/or spatial transcriptomics. 
 Both of these additional sequencing methods are supported by `scpca-nf`.
-<!-- TODO: Missing citations --> 
-`scpca-nf` takes FASTQ from bulk RNA-seq as input, trims reads using `fastp`, and then aligns reads with `salmon` (Supplemental Figure 3A). 
+`scpca-nf` takes FASTQ from bulk RNA-seq as input, trims reads using `fastp` [@doi:10.1093/bioinformatics/bty560], and then aligns reads with `salmon` (Supplemental Figure 3A) [@doi:10.1038/nmeth.4197]. 
 The output is a single TSV file with the gene by sample counts matrix for all samples in a given ScPCA project.
 This gene by sample matrix is only included with project downloads on the Portal.