title	date	output
Arthritogenic SKG T cells have a transcriptional program of activation and a repertoire pruned by superantigen.	October 6, 2021	html_document toc keep_md true true

This repo contains the code for the analyses from "Arthritogenic SKG T cells have a transcriptional program of activation and a repertoire pruned by superantigen". All raw and processed data objects are currently being uploaded to GEO.

This document is divided into six sections. The input and output files and jupyter notebooks are listed and described first (1. Directory). The next sections describe the experiment and analysis for the bulk RNA sequencing data (2. Bulk RNA Sequencing Analysis) and for the single cell RNA sequencing data in three sections (3. Single Cell RNA Seq - Cell sub-type and T.4N_Nr4a1 Analysis, 4. Trajectory Analysis, and 5 TCR analysis). For each section, the jupyter notebooks that go along with each analysis are listed along with the section headers within the notebook to facilitate easily finding code for a particular figure/analysis. The last section (6. Other Software Versions) details software versions not provided in the previous sections.

1. Directory

Jupyter Notebooks

1_SKG_RA_bulk_RNA_seq_analysis.ipynb
2_SKG_RA_single_cell_preprocessing_mouse.ipynb
3_SKG_RA_single_cell_clustering.ipynb
4_SKG_RA_single_cell_sub_type_profiling.ipynb
5_SKG_RA_single_cell_T_4_Nr4a1_analysis.ipynb
6_SKG_RA_single_cell_trajectory_analysis.ipynb
7_SKG_RA_single_cell_TRA_clonotype.ipynb
8_SKG_RA_single_cell_TRBV.ipynb
9_SKG_RA_single_cell_MAST.ipynb

/adata_object (not included in Github repo - GEO upload in progress)

adata_only_T_cells.h5ad: scanpy anndata object with processed data
single_cell_scvelo_T_4_Nr4a1_cluster.h5ad: scvelo anndata object with trajectory analysis

/custom_reference_input_files

GFP_gene_info.txt: eGfp transcript info
GFP_sequence.txt: eGfp transcript sequence

/data

bulk_rna_seq_meta_data.csv: meta data for bulk RNA seq count matrix
bulk_RNAseq_data.csv: bulk RNA seq count matrix

sc_RNA_sample_sheet.csv: meta data for single cell RNA seq samples
non_T_barcodes.npy: numpy file with contaminating B cell barcodes to remove for scRNA-seq data
G1_S.csv: Mus musculus G1/S gene list from REACTOME
G2_M.csv: Mus musculus G2/M gene list from REACTOME
LN_protein_data.csv: Vb frequency for each subgroup
Vb11_combo_joint.csv: Vb11 frequency post-arthritis induction
Vb14_combo_joint.csv: Vb14 frequency post-arthritis induction
Vb3_combo_joint.csv: Vb3 frequency post-arthritis induction
Vb5_combo_joint.csv: Vb5 frequency post-arthritis induction
Vb6_combo_joint.csv: Vb6 frequency post-arthritis induction
Vb8_combo_joint.csv: Vb8 frequency post-arthritis induction

/results 

• /bulk_RNA_seq

Note: data_S1_diff_exp_Group1_Group2.csv is for Group 1 v Group 2 i.e. positive log2FoldChange is for genes UP in Group 1 versus Group 2
Column Annotations:
X - Gene name
baseMean—The average of the normalized count values, dividing by size factors, taken over all samples
log2FoldChange–The effect size estimate
lfcSE–The standard error estimate for the log2 fold change estimate
stat–The value of the test statistic for the gene or transcript
pvalue–P-value of the test for the gene or transcript
padj–Adjusted P-value for multiple testing for the gene or transcript

data_S1_diff_exp_WT_low_SKG_low.csv
data_S1_diff_exp_WT_low_SKG_low.csv
data_S1_diff_exp_WT_high_SKG_high.csv
data_S1_diff_exp_SKG_low_SKG_high.csv

data_S2_heatmap_gene_list_with_modules.csv: Ordered list of genes in heat map with module annotations
Gene_list_for_heatmap_2021_02_v1_orig.csv: List of genes to annotate in the heatmap
Gene_list_for_heatmap_2021_02_v1.csv: Same file as above with added column "module" to indicate module assignment

dotplot_FEA_pathways_for_gene_modules.csv: Curated list of enriched GO:BP or KEGG pathways for each gene module

fig_1_diff_exp_SKG_high_v_WT_High_ranked.rnk: Ranked list for differential expression of SKG High v WT High

• /single_cell_RNA_seq
  • /correlations

gene_correlations_for_hvg_mouse_all_cells_spearman.csv
gene_correlations_for_hvg_mouse_Nr4a1_hi_cluster_spearman.csv

• /differential_expression

data_S4_scRNAseq_diff_genes_by_cluster.csv

data_S5_diff_genes_Nr4a1_high_cluster_versus_other_cells.csv

data_S5_diff_genes_SKG_High_v_WT_High_Nr4a1_cluster.csv

data_S6_diff_exp_Tnfrsf9_pos_Egr2_pos_Nr4a1_hi_cluster.csv

• /ranked_lists

SKG_High_v_WT_High_Nr4a1_high_cluster.rnk

Nr4a1_high_cluster_Egr_high_v_Tnf_high_genes.rnk

Nr4a1_cluster_stage_1_v_stage_4.rnk

• /trajectory

data_S7_top_300_heatmap_gene_list.txt

• /TCR

data_S8_gini_coefficients.csv
TCR_data.pickle
data_S9_TRBV_paired_test_nr4a1_cluster.csv
select_TRBV_sample_frequencies_Nr4a1_high_cluster.csv
select_TRBV_sample_frequencies_all_cells.csv
volcano_plot_gene_list_to_label.csv
TRBV3_MAST_SKG_high_v_SKG_low_cell_types.csv
TRBV19_MAST_SKG_high_v_SKG_low_cell_types.csv

/scripts

run_cellranger_GEX.sh
run_cellranger_TCR.sh
run_velocyto.sh

2. Bulk RNA Sequencing Analysis

Sequencing (3 batches run on 3 lanes of HiSeq 2500)

Batch 1 (H94843)
2a_SKGNur_CD4Naive_GFPlo
2b_SKGNur_CD4Naive_GFPhi
3a_SKGNur_CD4Naive_GFPlo
4a_WTNur_CD4Naive_GFPlo
4b_WTNur_CD4Naive_GFPhi

Batch 2 (H95020)

5a_WTNur_CD4Naive_GFPlo
5b_WTNur_CD4Naive_GFPhi
6a_WTNur_CD4Naive_GFPlo
6b_WTNur_CD4Naive_GFPhi

Batch 3 (H96272)
1a_SKGNur_CD4Naive_GFPlo
1b_SKGNur_CD4Naive_GFPhi
3b_SKGNur_CD4Naive_GFPhi

Results 

Note: #PE Sequencing Reads are reads after filtering for QC metrics

Sample	#PE Sequencing Reads
1a	41,942,126
1b	43,098,857
2a	34,456,547
2b	36,633,847
3a	37,967,524
3b	56,713,716
4a	37,885,107
4b	37,201,324
5a	32,793,434
5b	33,106,184
6a	35,015,201
6b	34,316,756

Analysis

1_SKG_RA_bulk_RNA_seq_analysis.ipynb

Software versions:

python: pandas v.1.1.3 numpy v.1.19.2 rpy2 v.2.9.4 matplotlib v.3.3.1

R: VennDiagram v.1.6.20 pheatmap v.1.0.12 hash v.2.2.6.1 ggplot2 v.3.3.2 DESeq2 v.1.22.2

Sections:

• Data Processing
• PCA Analysis
• Differential Expression
• Heatmap
• GO Plot
• Module Distribution
• Volcano plot SKG High v WT High
• Ranked list for GSEA
• Other

3. Single Cell RNA Seq - Cell sub-type and T.4N_Nr4a1 Analysis

Sequencing (8 wells of 5'10x-VDJ sequenced on NovaSeq 6000)

GEX

Sample	PE Sequencing Reads
1	745,890,946
2	727,971,779
3	784,588,458
4	747,029,488
5	840,784,64
6	831,011,967
7	892,260,945
8	715,376,870

TCR

Sample	#PE Sequencing Reads
1	209,571,333
2	438,975,429
3	403,195,137
4	366,362,663
5	419,581,007
6	278,298,845
7	510,594,226
8	404,954,306

Alignment

GEX

/scripts/run_cellranger_GEX.sh
Used cellranger v.3.0.1 and mm10_withGFP transcriptome.

mm10_withGFP transcriptome creation:

• Input files: /custom_reference_input_files

#Concatenate GFP sequence and GFP gene description to files from refdata-cellranger-mm10-3.0.0
cat GFP_sequence.txt>>genome.fa
cat GFP_gene_info.txt>>genes.gtf
#Create mm10_withGFP reference
cellranger mkref --genome=mm10_withGFP --fasta=genome.fa --genes=genes.gtf

TCR

/scripts/run_cellranger_TCR.sh

Analysis

2_SKG_RA_single_cell_preprocessing_mouse.ipynb

Software versions: scanpy v.1.4.3

Preprocessing to create scanpy objects for each 10x lane cellranger output

3_SKG_RA_single_cell_clustering.ipynb

Software versions: scanpy v.1.4.3

Data normalization and clustering

4_SKG_RA_single_cell_sub_type_profiling.ipynb

Software versions: scanpy v.1.5.1

Sections:

• Create cell sub types and run diff exp
• UMAPs by subgroup
• Density UMAPs by subgroup
• Stacked Violin Plots and Matrix Plots
• Dot Plot for Cell Type Markers
• Distribution over cell sub-types by subgroup
• Scoring for bulk RNA Seq modules
• Calculate Cell Cycle
• Save adata object

5_SKG_RA_single_cell_T_4_Nr4a1_analysis.ipynb

Software versions: scanpy v.1.7.1

Sections:

• UMAP colored by T.4 Nr4a1 cluster
• Differential Expression for T.4 Nr4a1 cluster
• Volcano Plot for T.4N Nr4a1 cluster v other cells
• UMAP for SKG High v WT High in T.4N Nr4a1 cluster
• Differential expression for SKG High v WT High in T.4N Nr4a1 high cluster
• Volcano Plot for SKG High v WT High in T.4N Nr4a1 cluster
• UMAPs for gene markers
• Correlation Heatmaps
• Volcano Plot Egr2 High v Tnfrsf9 High
• Cell Cycle Analysis

4. Trajectory Analysis

Analysis Pipeline

Create loom files from cellranger BAM files using velocyto (/scripts/run_velocyto.sh). Use loom files as input for scvelo.

Analysis  

6_SKG_RA_single_cell_trajectory_analysis.ipynb

Software versions: velocyto v.0.17.17 loompy v.2.0.17 scvelo v.0.2.1 scanpy v.1.5.1

Sections:

• UMAPs for Egr2 and Tnfrsf9 expression
• Merge data and run scvelo in dynamical mode
• UMAP overlays
• Visualize latent time distributions
• Separate stages of latent time distribution with Gaussian Mixture Model
• Heatmap of expression top genes for modelling latent time
• Differential expression between Stage 1 and Stage 4
• Visualize smoothed gene expression over latent time
• Run PAGA
• Save adata object

5. TCR Analysis

Analysis

7_SKG_RA_single_cell_TRA_clonotype.ipynb

Software versions: scanpy v.1.5.1

Sections:

• Load TCR Data
• Add TRAV data to adata
• Add TRBV data to adata
• Add Clonotype data to adata
• Gini Coefficient Analysis
• Barplot paired TCR coverage
• Filter cells based on TRAV
• Calculate double TRA frequency
• Filter cells based on TRAV (cont.)
• Plot TRAV abundance by subgroup
• TRAV diff between SKG High and SKG Low
• Analysis for T.4 Nr4a1 hi cluster

8_SKG_RA_single_cell_TRBV.ipynb

Software versions: scanpy v.1.5.1

Sections:

• Filter based on TRBVs and dual TRAs
• Plot TRBV abundance by subgroup
• TRBV diff between SKG High and SKG Low
• Barplots for TRBV frequencies by subgroup
• Analysis for Protein Data
• Analysis for T.4N Nr4a1 cluster

9_SKG_RA_single_cell_MAST.ipynb

Software versions: python: rpy2 v.3.3.2 anndata2ri v.1.0.4

R: MAST v.1.12.0

Sections:

• Remove Dual TRA cells
• Subset adata to SKG TRBV3 cells
• Set up adata object for MAST
• Run MAST for TRBV3
• Save results
• Volcano plot for TRBV3
• Setup TRBV19 adata object for MAST
• Run MAST for TRBV19
• Volcano plot for TRBV19

6. Other Software Versions

Python 3

R v.3.5.1

Jupiter Notebook version info:
jupyter-notebook : 6.0.3
ipykernel : 5.1.4
jupyter lab : 1.2.6