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## Figure Titles and Legends {.page_break_before}
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<!-- Figure 1 -->
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{#fig:fig1 tag="1" width="7in"}
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{#fig:fig1 tag="1" width="7in"}
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A. Barplots showing sample counts across four main cancer groupings in the ScPCA Portal with the total number of samples for each cancer type displayed to the right of each bar.
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Each bar is colored based on the number of samples with the indicated disease timing.
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<!-- Figure 2 -->
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{#fig:fig2 tag="2" width="7in"}
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{#fig:fig2 tag="2" width="7in"}
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A. Overview of `scpca-nf`, the primary workflow for processing single-cell and single-nuclei RNA-seq data for the ScPCA Portal.
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Mapping is first performed with `alevin-fry` to generate a gene-by-cell count matrix, which is read into `R` and converted into a `SingleCellExperiment` (`SCE`) object.
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<!-- Figure 3 -->
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{#fig:fig3 tag="3" width="7in"}
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{#fig:fig3 tag="3" width="7in"}
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A. Expanded view of the process for adding cell type annotations within `scpca-nf`, as introduced in Figure {@fig:fig2}A.
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Cell type annotation is performed on the `Processed SCE Object`.
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<!-- Figure 4 -->
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{#fig:fig4 tag="4" width="7in"}
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{#fig:fig4 tag="4" width="7in"}
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A. Dot plot showing expression of cell-type-specific marker genes across all libraries from brain and central nervous system (CNS) tumors, excluding multiplexed libraries.
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Expression is shown for each broad cell type annotation, where each broad cell type annotation is a collection of similar consensus cell type annotations.
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The percentage shown corresponds to the percentage of immune cells classified as the indicated consensus cell type.
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Only libraries comprised of at least 1\% immune cells, based on consensus cell type annotations, are shown.
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Specific consensus cell types for myeloid and lymphocyte immune cells are shown, with all other consensus immune cell types included in "other."
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Any myeloid or lymphocyte immune cell types with fewer than 1000 cells across all libraries are also include in "other".
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Any myeloid or lymphocyte immune cell types with fewer than 1000 cells across all libraries are also included in "other".
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D. Dot plot showing expression of cell-type-specific marker genes across all non-multiplexed libraries from brain and CNS tumors, considering only immune cells.
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Expression is shown for each consensus cell type annotation.
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<br><br>
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<!-- Figure 5 -->
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{#fig:fig5 tag="5" width="7in"}
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{#fig:fig5 tag="5" width="7in"}
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A. UMAP highlighting cell type annotations made with the OpenScPCA Project, collapsed into broad annotation groups, for all libraries in the neuroblastoma-only ScPCA Project `SCPCP000004` (N = 42).
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The UMAP was constructed from the merged `SCPCP000004` object such that all libraries contribute an equal weight, but no batch correction was performed.
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<!-- Figure 6 -->
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{#fig:fig6 tag="6" width="7in"}
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{#fig:fig6 tag="6" width="7in"}
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A. Scatter plots colored by point density of `DESeq2`-transformed and normalized bulk RNA-seq expression compared to pseudobulk expression from single-cell/nuclei RNA-seq.
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Samples with RNA-seq for both bulk and single-cell/nuclei modalities, excluding multiplexed samples, from ScPCA projects comprising brain and central nervous system tumors are shown, with the number of samples considered per project shown in parentheses.
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<br><br>
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<!-- Figure S1 -->
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{#fig:figS1 tag="S1" width="7in"}
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{#fig:figS1 tag="S1" width="7in"}
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Each panel compares metrics for six ScPCA libraries, including three single-cell and three single-nuclei suspensions, obtained from processing libraries with `salmon alevin` and `alevin-fry` or `CellRanger`.
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Results shown were generated with `CellRanger v6.1.2` using default parameters for single-cell libraries and use of the `--include_introns` flag to include intronic reads for single-nuclei libraries only.
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<br><br>
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<!-- Figure S2 -->
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{#fig:figS2 tag="S2" width="7in"}
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{#fig:figS2 tag="S2" width="7in"}
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A. Overview of the `scpca-nf` workflow for processing libraries with CITE-seq or antibody-derived tag (ADT) data.
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The workflow mirrors that shown in Figure {@fig:fig2}A with several differences accounting for the presence of ADT data.
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<br><br>
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<!-- Figure S3 -->
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{#fig:figS3 tag="S3" width="7in"}
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{#fig:figS3 tag="S3" width="7in"}
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A. Overview of the bulk RNA-Seq workflow.
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A set of FASTQ files from libraries sequenced with bulk RNA-seq are provided as input.
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<br><br>
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<!--Figure S4-->
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{#fig:figS4 tag="S4" width="7in"}
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{#fig:figS4 tag="S4" width="7in"}
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A. UMAP highlighting cells annotated as types of T cells with `SingleR`, `CellAssign`, `SCimilarity` as well as the associated consensus cell types for the library `SCPCL000049`.
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All other cells are shown in gray.
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<!-- Figure S5 -->
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{#fig:figS5 tag="S5" width="9in"}
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{#fig:figS5 tag="S5" width="9in"}
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Dot plots showing expression of cell-type-specific marker genes across all libraries from Leukemia (A), Sarcoma (B), and Other solid tumors (C) diagnosis groups.
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Expression is shown for each broad cell type annotation, where each broad cell type annotation is a collection of similar consensus cell type annotations.
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<br><br>
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<!-- Figure S6 -->
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{#fig:figS6 tag="S6" width="7in"}
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{#fig:figS6 tag="S6" width="7in"}
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Barplots of the percentage of cells annotated as each broad consensus cell type annotation across all libraries from Leukemia (A), Sarcoma (B), and Other solid tumors (C) diagnosis groups.
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Within each panel, libraries are shown grouped by diagnosis.
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<br><br>
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<!-- Figure S7 -->
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{#fig:figS7 tag="S7" width="7in"}
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{#fig:figS7 tag="S7" width="7in"}
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A. Scatter plots colored by point density of `DESeq2`-transformed and normalized bulk RNA-seq expression compared to pseudobulk expression from single-nuclei RNA-seq.
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Projects with RNA-seq for both bulk and single-cell/nuclei modalities that are not displayed in Figure {@fig:fig6}A are shown.
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