simplify file name descriptions

content/03.results.md

@@ -53,18 +53,18 @@ Based on these results, we elected to use `salmon alevin` and `alevin-fry` [@doi
 `scpca-nf` takes FASTQ files as input (Figure {@fig:fig2}A).
 Reads are aligned using the selective alignment option in `salmon alevin` to an index with transcripts corresponding to spliced cDNA and intronic regions, denoted by `alevin-fry` as a `splici` index.
 The output from `alevin-fry` includes a gene by cell count matrix for all barcodes identified, even those that may not contain true cells.
-This unfiltered counts matrix is stored in a `SingleCellExperiment` object [@doi:10.1038/s41592-019-0654-x] and output from the workflow to an R Data Serialization format (`.rds`) file with the suffix `_unfiltered.rds`.
-<!-- JAS: I tend to think we should remove the file names and extensions from the Results: they make sense to me in methods but seem a bit deep in the weeds for this section. Just the object types I think is probably sufficient as a description of the results.-->
+This unfiltered counts matrix is stored in a `SingleCellExperiment` object [@doi:10.1038/s41592-019-0654-x] and output from the workflow as a file with the suffix `_unfiltered.rds`.
+<!-- JAS: I tend to think we should remove the file names and extensions from the Results: they make sense to me in methods but seem a bit deep in the weeds for this section. I simplified them a bit, so I am happier now. Leaving this comment for later reviewers. -->
 
 `scpca-nf` performs filtering of empty droplets, removal of low-quality cells, normalization, dimensionality reduction, and cell type annotation (Figure {@fig:fig2}A).
-The unfiltered gene by cell counts matrices are filtered to remove any barcodes that are not likely to contain cells using `DropletUtils::emptyDropsCellRanger()`[@doi:10.1186/s13059-019-1662-y], and all cells that pass are saved in a `SingleCellExperiment` object and `.rds` file with the suffix `_filtered.rds`.
+The unfiltered gene by cell counts matrices are filtered to remove any barcodes that are not likely to contain cells using `DropletUtils::emptyDropsCellRanger()`[@doi:10.1186/s13059-019-1662-y], and all cells that pass are saved in a `SingleCellExperiment` object and a file with the suffix `_filtered.rds`.
 Low-quality cells are identified and removed with `miQC` [@doi:10.1371/journal.pcbi.1009290], which jointly models the proportion of mitochondrial reads and detected genes per cell and calculates a probability that each cell is compromised.
 The remaining cells' counts are normalized [@doi:10.1186/s13059-016-0947-7], and reduced-dimension representations are calculated using both principal component analysis (PCA) and UMAP.
 Finally, cell types are classified using two automated methods, `SingleR`[@doi:10.1038/s41590-018-0276-y] and `CellAssign`[@doi:10.1038/s41592-019-0529-1].
-The results from this analysis are stored in a processed `SingleCellExperiment` object saved to an `.rds` file with the suffix `_processed.rds`.
+The results from this analysis are stored in a processed `SingleCellExperiment` object saved to a file with the suffix `_processed.rds`.
 
 To make downloading from the Portal convenient for R and Python users, downloads are available as either `SingleCellExperiment` or `AnnData`[@doi:10.1101/2021.12.16.473007] objects.
-`scpca-nf` converts all `SingleCellExperiment` objects to `AnnData` objects, which are saved as Hierarchical Data Format (`.hdf5`) files (Figure {@fig:fig2}A).
+`scpca-nf` converts all `SingleCellExperiment` objects to `AnnData` objects, which are saved as `.hdf5` files (Figure {@fig:fig2}A).
 Downloads contain the unfiltered, filtered, and processed objects from `scpca-nf` to allow users to choose to perform their own filtering and normalization or to start their analysis from a processed object.
 
 All downloads from the Portal include a quality control (QC) report with a summary of processing information (e.g., `alevin-fry` version), library statistics (e.g., the total number of cells), and a collection of diagnostic plots for each library (Figure {@fig:fig2}B-G).
@@ -83,18 +83,18 @@ To process ADT libraries, the ADT FASTQ files were provided as input into `scpca
 Along with the FASTQ files, `scpca-nf` takes a tab-separated values (TSV) file with one row for each ADT – containing the name used for the ADT and associated barcode – required to build an ADT-specific index for quantifying ADT expression with `alevin-fry`.
 The output from `alevin-fry` is the unfiltered ADT by cell counts matrix.
 The ADT by cell counts matrix is read into R alongside the gene by cell counts matrix and saved as an alternative experiment (`altExp`) within the main `SingleCellExperiment` object containing the unfiltered RNA counts.
-This `SingleCellExperiment` object containing both RNA and ADT counts is output from the workflow to an `.rds` file with the suffix `_unfiltered.rds`.
+This `SingleCellExperiment` object containing both RNA and ADT counts is output from the workflow to a file with the suffix `_unfiltered.rds`.
 
 `scpca-nf` does not filter any cells based on ADT expression or remove cells with low-quality ADT expression.
 Any cells removed after filtering empty droplets based on the unfiltered RNA counts matrix are also removed from the ADT counts matrix.
 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`.
+The `SingleCellExperiment` object containing the filtered RNA and ADT counts matrix and associated ADT QC statistics is saved to a file with the suffix `_filtered.rds`.
 
 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()` [@doi:10.18129/B9.bioc.scuttle; @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 an `.rds` file with the suffix `_processed.rds`.
-All `.rds` files containing `SingleCellExperiment` objects and associated `altExp` objects are converted to `AnnData` objects and exported as separate RNA (`_rna.hdf5`) and ADT (`_adt.hdf5`) `AnnData` objects.
+The normalized ADT data is saved as an `altExp` within the processed `SingleCellExperiment` containing the normalized RNA data and is output to a file with the suffix `_processed.rds`.
+All files containing `SingleCellExperiment` objects and associated `altExp` objects are converted to `AnnData` objects and exported as separate RNA (`_rna.hdf5`) and ADT (`_adt.hdf5`) `AnnData` objects.
 
 If a library contains associated ADT data, the QC report output by `scpca-nf` will include an additional section with a summary of ADT-related statistics, such as how many cells express each ADT, and ADT-specific diagnostic plots (Figure {@fig:figS2}B-D).
 As mentioned above, `scpca-nf` uses `DropletUtils::cleanTagCounts()` to calculate QC statistics for each cell using ADT expression but does not filter any cells from the object.
@@ -108,10 +108,10 @@ The top four ADTs with the most variable expression are also identified and visu
 To process multiplexed libraries, the HTO FASTQ files are input to `scpca-nf` and quantified using `salmon alevin` and `alevin-fry` (Figure {@fig:figS2}C).
 Along with the FASTQ files, `scpca-nf` requires two TSV files to process multiplexed data: one to build an HTO-specific index for quantifying HTO expression with `alevin-fry`, and a second to indicate which HTO was used for which sample when multiplexing the library.
 The unfiltered HTO by cell counts matrix output from `alevin-fry` is saved as an alternative experiment (`altExp`) within the main `SingleCellExperiment` containing the unfiltered RNA counts.
-This `SingleCellExperiment` object containing both RNA and HTO counts is output from the workflow to an `.rds` file with the suffix `_unfiltered.rds`.
+This `SingleCellExperiment` object containing both RNA and HTO counts is output from the workflow to a file with the suffix `_unfiltered.rds`.
 
-As with ADT data, `scpca-nf` does not filter any cells based on HTO expression, and any cells removed after filtering empty droplets based on the unfiltered RNA counts matrix are also removed from the HTO counts matrix with the remainder saved to an `.rds` file with the `_filtered.rds` suffix.
-`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.
+As with ADT data, `scpca-nf` does not filter any cells based on HTO expression, and any cells removed after filtering empty droplets based on the unfiltered RNA counts matrix are also removed from the HTO counts matrix with the remainder saved to a file with the `_filtered.rds` suffix.
+`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 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.
 Instead, `scpca-nf` applies multiple demultiplexing methods, including demultiplexing with `DropletUtils::hashedDrops()` [@doi:10.18129/B9.bioc.DropletUtils], demultiplexing with `Seurat::HTODemux()` [@doi:10.1186/s13059-018-1603-1], and genetic demultiplexing when bulk RNA-seq data is available.
@@ -145,7 +145,7 @@ When downloading data for an entire project, users can choose between receiving
 Users also have the option to choose their desired format and receive the data as `SingleCellExperiment` (`.rds`) or `AnnData` (`.hdf5`) objects.
 
 For downloads with samples as individual files, the download folder will include a sub-folder for each sample in the project (Figure {@fig:fig3}A).
-Each sample folder contains all three object types (unfiltered, filtered, and processed) as either `SingleCellExperiment` (`.rds`) or `AnnData` (`.hdf5`) objects and the QC and cell type summary report for all libraries from the given sample.
+Each sample folder contains all three object types (unfiltered, filtered, and processed) in the requested file format and the QC and cell type summary report for all libraries from the given sample.
 The objects house the summarized gene expression data and associated metadata for the library indicated in the filename.
 
 All project downloads include a metadata file, `single_cell_metadata.tsv`, containing relevant metadata for all samples, and a `README.md` with information about the contents of each download, contact and citation information, and terms of use for data downloaded from the Portal (Figure {@fig:fig3}A-B).