library(readr)
library(knitr)
library(DT)
library(dplyr)

Overview

Quality Control

Cells with mitochondrial counts above 5% and feature count below 200 were filtered out.

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Basic Stats

Summary Statistics

Table of Cell Counts Per Sample

Table of Cell Counts Per Cluster

Table of Cell Counts Per Cluster Filtered

Initial UMAP Clustering

UMAP clusters with no QC or batch correction.

UMAPS

UMAP Colored by Transcript
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UMAP Colored by Sample
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UMAP Colored by Epithelium
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UMAP Colored by Spleen
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UMAP Colored by Pooled JLN
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UMAP Colored by Pooled Spleen
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Heatmap

Heatmap of Gene expression per cluster

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UMAP Clustering after QC

This section generates UMAP clustering after filtering out cells with greater than 5% mitochondrial gene counts and less than 200 features.

UMAP Colored by Transcript
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UMAP Colored by Sample
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UMAP Colored by Epithelium
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UMAP Colored by Spleen
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UMAP Colored by Pooled JLN
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UMAP Colored by Pooled Spleen
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Heatmap

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UMAP Clustering after Batch Correction

This section generates UMAP clustering after filtering out cells with greater than 5% mitochondrial gene counts and less than 200 features. Batch correction is implemented with Harmony before clustering.

UMAP Colored by Transcript
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UMAP Colored by Sample
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UMAP Colored by Epithelium
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UMAP Colored by Spleen
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UMAP Colored by Pooled JLN
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UMAP Colored by Pooled Spleen
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Heatmap

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UMAP Clustering with CSP and scRNAseq data merged

This UMAP is the clustering after merging the CSP and scRNAseq data using a weighted nearest neighbor algorithm.

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Feature Plots for scRNAseq data

Feature plots for the single cell RNAseq data, used to identify T-cell clusters.

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Feature Plots for CSP data

Feature plots for the cell surface protien data, used to identify TCR-Beta positive clusters.

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Differential Gene Expression

Below are the volcano plots and gene tables generated from DESEQ2 output using the merged scRNAseq and CSP data.

Volcano Plots

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Epithelium VS GreenSpleen

Epithelium VS JLN

Epithelium VS RedSpleen

GreenSpleen VS JLN

RedSpleen VS GreenSpleen

## Warning in instance$preRenderHook(instance): It seems your data is too big for
## client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html

RedSpleen VS JLN

TODO

Volcano plots of FOXP3 + CD4 T cells

Polish report and remove necessary figures

List of Genes

Put list of genes in the dataset here

Put list of CSP receptors here

TCR

What clonotypes are enriched?

What clonotypes are enriched across samples?

Appendix

Put code here