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nmabbasi

ABBASI

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Sézary Syndrome Cell Line derived from each patient DE comparison
used_patient_origin NEwumap Seem like if you dont use harmony clusters batch effect is evident in volcano plot
Sézary Syndrome Cell Line derived from each patient DE comparison
NewUMAP P1vsP2, P1vsP3, P2vsP3 ClusterBased
Differential Expression Analysis - Filtering and Visualization-NEWUMAP
Malignant_CD4Tcells_vs_Normal_CD4Tcells_RNA_Assay_Wilcox.csv Malignant_CD4Tcells_vs_Normal_CD4Tcells_SCT_SCTransformed_Wilcox.csv
Differential Expression Analysis - Filtering and Visualization-NEWUMAP
NewUMAP
Differential Expression Analysis using Harmony Integrated Clusters
NewUMAP
DE(Malignat_vs_Normal_CD4Tcells) of Harmony Integration
# Define malignant and normal cell lines malignant_cell_line <- c("L1", "L2", "L3", "L4", "L5", "L6", "L7") normal_cell_line <- c("PBMC", "PBMC_10x") DE Analysis Using SCT Assay (SCTransformed Counts) Next will be: L1 vs healthy DE Analysis Using RNA Assay (Log-Normalized Counts)
Inference and analysis of cell-cell communication using CellChat-SS_cellLines_HarmonyIntegration_PBMC10x
New Harmony UMAP
TCR Analysis using harmony integrated NewUMAP with PBMC10x
Hamony integrated UMAP 0.8 with PBMC10x
Different Resolution Tables on harmony integration
Final UMAP 0.5 theta
Different Resolution test on harmony integration
Final UMAP 0.5 theta Annotated again and removed nonCD4 T cells from Control
Different Resolution Tables on harmony integration
Tables for Resolution Azimuth annotation done again
Different Resolution test on harmony integration
I annotated the harmony integration again to remove artifacts
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 regress nCount, percent.mt and rb and apply SCT
didnt remove ILC, NK and CD14 Mono from cell lines and annotated after normalization
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells
28-Jan- Annotate after SCT
Identify the cluster 14(0.9) from harmony integration in normal CD4 Tcells
Different Resolution Tables on harmony integration on patient origin and orig.ident-theta-0.5,0.5
patient origin and orig.ident-theta-0.5,0.5 0.4-1.2 tables
Different Resolution test on harmony integration on patient origin and orig.ident-theta-at-0.5,0.5
patient origin and orig.ident-theta-at-0.5,0.5 Different Resolutions: 0.1-1.2
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)-GSEA
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)-GSEA
GSEA
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)
fgsea_result_kegg <- fgsea( pathways = kegg_list, stats = gene_list, nperm = 1000 # Number of permutations )
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells-Part2
Kegg included
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)
Harmony integrations of PBMC10x by cellLine -theta-default both
Harmony integrations of PBMC10x by Patient -theta-0.5 both
Harmony integrations of PBMC10x by Patient and orig.ident-theta-0.5 both
Harmony integrations of PBMC10x by cell_line-theta-0.5 both
on Cell lines
RPCA--Harmony Integration of PBMC10x with SCT on samples part2
RPCA-CCA-Harmony Integration of PBMC10x with SCT on samples part1.2
RPCA-Integration of PBMC10x with SCT on samples part1
1:20
Different evaluation test on harmony integration on patient origin and cell_line-theta-0.5 both
Including clustree Tables to check distribution
Different evaluation test on harmony integration on patient origin and cell_line-theta-0.5 both
Different Resolution umap 0.1 to 1.2
Harmony integrations of PBMC10x by patient origin and cell_line-theta-0.5 both
Finalized saved object to: ../0-R_Objects/CD4Tcells_harmony_integrated_0.5_theta_patientorigin_cell_line.Robj
Harmony integrations of PBMC10x by patient origin and cell_line-theta-0.5 both
Harmony integrations of PBMC10x by patient origin and cell_line-theta-1
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono regress nCount, percent.mt and rb and cell cycle apply SCT
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono regress nCount, percent.mt and rb and apply SCT
Harmony integrations of PBMC10x by cell_line-theta-0.5
1:15 dim theta 0.5 cell_line as batch
Merged All samples with PBMC_10x, Removed non CD4 T cells from Control, Apply_SCT_then_Harmony_theta-0.5
1:16 without regressing using SCTransform
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells
I removed B cells from L4 CD14 Mono and ILC and NK just one cell
Differential Expression Analysis - Filtering and Visualization
This analysis includes all genes in SCT logfc.threshold used = 0 min.pct=0 Then we used added column of mean expression and we used those columns for filtering. Here you have summary of genes before filtering and after filtering
Differential Expression Analysis - Filtering and Visualization
Its with previous file All genes min.pct = 0 logfc.threshold = 0
Merged All samples with PBMC_10x, Removed non CD4 T cells from Control, Apply_SCT_then_Harmony_theta-0.5
1:15 Didnt regress anything in SCTransform
Differential Expression Analysis - Filtering and Visualization
Its with previous file All genes min.pct = 0 logfc.threshold = 0
Differential Expression Analysis - Filtering and Visualization
Its with previous file 14000 genes min.pct=default logfc.threshold=default
Differential Expression Analysis using Harmony Integrated Clusters
heatmap is based on pvalue and avg_log2FC
Differential Expression Analysis using Harmony Integrated Clusters
celllines vs CD4Tcells normal clusters min.pct=0 logfc.threshold=0 MAST with batch
Harmony integrations of PBMC10x by cell_line-theta-0.5_removing non CD4Tcells and B cells from L4_also_ILC_NK_CD14_Mono
ILC,NK, CD14 MOno are from L4 where we have B cells so i removed them
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and regress nCountRNA and apply SCT_finalized
Didnt removed NK and ILC
Seurat Integration of PBMC10x-HPC-rpca
CD4Tcells
Seurat Integration of PBMC10x-Rserver-rpca-part1
Its done after removal of nonCD4T cells from PBMC and B cells also from L4
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono didnt regress nCount and nFeature and apply SCT
I havent regress nCount and nFeature to see UMAP
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono and regress nCountRNA and nFeatureRNA and apply SCT
1:16 I also removed CD14 Mono Regress nCount and nFeature
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC and NK just one Cell and regress batch and apply SCT
Not a good idea to regress using SCTransform
Harmony integrations of PBMC10x by cell_line-theta-0.5
After removing nonCD4 T cells
use Annotated Robj including PBMC10x to remove ILC and NK-just one Cell
Removed ILC and NK from l2 as it was forming seperate cluster
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and apply SCT
1;16
Merged All samples with PBMC_10x and removed non CD4 T cells from Control apply SCT
1:16
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and B cells from L4
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and B cells from L4
Merged All samples with PBMC_10x and removed non CD4 T cells from Control apply SCT
1:22 we got 1:16 by PCA test so I will use that
CD4Tcells in PBMC(Ready to Normalize)
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells
Pre-Analysis using FeaturePlot
ADT normalization- Margin = 2 and Visualized by dittoseq heatmap
Margin 2
ADT normalization- Margin = 1 and Visualized by dittoseq heatmap
Margin 1
Enrichment after FC Scanner Analysis-part2
FC Scanner files for Analysis-part2
Sézary Syndrome silhouette Score Test-Part2
silhouette Score Test
Enrichment after FC Scanner Analysis
DE with FC Scanner
Tables of celllines based on clusters harmony0.9
Tables of celltypes based on cell_lines
Tables of celltypes based on clusters harmony0.9
Cell_lines vs PBMC
Patients vs PBMC-Tcells
P1,P2,P3
TCR Analysis using harmony integrated UMAP
TCR Analysis using harmony integrated UMAP
Cell Line derived from each patient DE comparison-part2
Top10 categories
Significant genes for strings
0.05 2 -2
Sézary Syndrome Cell Line derived from each patient DE comparison
Cell lines derived from patient
SCpubr
Sézary Syndrome SCpubr Visualization
https://enblacar.github.io/SCpubr-book-v1/04-FeaturePlots.html
Sézary Syndrome Cell Line Top5 gene markers
Top5 and Top10
Sézary Syndrome top10 Clusters
Differential Expression Analysis of SS vs PBMC10X+PBMC
based on CSV file
DE_Harmony_integrated_0.9_SS vs PBMC10X+PBMC_Tcells
Differential Expression Analysis
1vs2 6vs16
InferCNV Analysis
Percentage of cells CNVs
Harmony_Intergrations_and_visualization_of_PBMC-10x.
Its juts to visualize cluster, clustree and tables
Harmony integrations of PBMC10x by sample_group-theta-0.5
Theta-0.5
Harmony integrations of PBMC10x by cell_line_group-theta-0.5
Theta-0.5
Harmony integrations of PBMC10x by cell_line-theta-0.5
Theta-0.5
Harmony integrations of PBMC10x by cell_line-theta-0
Theta-0
Harmony integrations of PBMC10x by cell_line-theta-Default
Theta-Default
Sézary Syndrome Cell Line Analysis-DE-Integrated
DE integrated
Sézary Syndrome Cell Line Analysis-DE-PBMC10X
DE Merged with PBMC10x
Differential Expression Analysis of SS vs PBMC10X+PBMC
based it on clusters after harmony integration which I did
Harmony integrations of PBMC10x-part4
final version to Discuss
Multiple Harmony integrations of PBMC10x-part3
on HPC we use sample group, cell line group and cell line
Harmony integrations of PBMC10x bu sample group, cell line group and cell line
Multiple Harmony integrations of PBMC10x
1:22 Different methods of harmony are tried.
Harmony Integration of PBMC10x-part2
1:22 0.5
Harmony Integration of PBMC10x-Part1
1:22 0.5
RPCA-CCA-Harmony Integration of PBMC10x with NormalizeData-VST on samples part3
Its on Rserver 1:20 1.2
Harmony Integration of PBMC10x with SCT on samples part2
Merged All samples with PBMC_10x and apply SCT on 1:22
Object is saved as All_Samples_Merged_with_10x_Azitmuth_Annotated_SCT_HPC_without_harmony_integration.robj
Harmony Integration of PBMC10x with SCT on samples
old method of normalizing the SCT clusters
Merged All samples with PBMC_10x
1:12 PC
Merged All samples with PBMC_10x
First regressed in SCT for cell_line and then we used harmony
Merged All samples with PBMC_10x
HPC 1:22
Merged All samples with PBMC_10x_on_HPC
SCT normalization of merged samples and Harmony Integration
PBMC10X included
Merged All samples with PBMC_10x and SCT analysis on annotated Object
Did analysis on Rstudio server
Merging all our cell lines and controls(PBMC-PBMC10x) into single seurat object-Robj
Merging all our cell lines and controls(PBMC-PBMC10x) into single seurat object-Robj
PBMC_10x
New Reference
Cytogenetic Analysis
Comparison of inferCNV with Cytogenetics data
Sézary Syndrome Cell Line DE Analysis
Phylogeny_based on inferCNV
Phylogeny_based on RNA Expression-euclidean_pearson
Differential Expression Analysis at 0.9
WNN analysis of CITE-seq, RNA + ADT-Part2
Inference and analysis of cell-cell communication using CellChat-SS_cellLines
SS Object is USED
TCR Analysis-Part2
New UMAP
WNN analysis of CITE-seq, RNA + ADT-PART5
res:0.4
WNN analysis of CITE-seq, RNA + ADT_part4
WNN analysis of CITE-seq, RNA + ADT_part3
Res=0.9 dims.list = list(1:20, 1:18), modality.weight.name = "RNA.weight"
WNN analysis of CITE-seq, RNA + ADT part2
dims.list = list(1:20, 1:18), modality.weight.name = "RNA.weight"
WNN analysis of CITE-seq, RNA + ADT
Inference and analysis of cell-cell communication using CellChat-Melange
Melange
UMAP of T cells without other PBMC cells using clusters and PC-1:20
UMAP of T cells without other PBMC cells using clusters and PC-1:20
L1_Merged_first_HPC_PC-1:21-6-old_script
L1_Merged_first_HPC_PC-1:21-6-old_script
L1_Merged_first_HPC_PC-1:50-5
L1_Merged_first_HPC_PC-1:50-5
L1_Merged_first_HPC_PC-1:50-4
L1_Merged_first_HPC_PC-1:50-4
L1_Merged_first_HPC_PC-1:50-3
L1_Merged_first_HPC_PC-1:50-3
UMAP of T cells-PC-1:50-2
Old Script used
Just T cells Analysis_PC-1:50
UMAP of T cells without other PBMC cells using clusters and PC-1:50
L7
Cell Line L7 Analysis
L6
Cell Line L6 Analysis
L5
Cell Line L5 Analysis
L3
Cell Line L3 Analysis
L4
Cell Line L4 Analysis
L4_notebook
Cell Line L4 Analysis
L3_Notebook
Cell Line L3 Analysis
L2_Notebook
Cell Line L2 Analysis
L2
Cell Line L2 Analysis
L1_notebook
Cell Line L1 Analysis
L1
Cell Line L1 Analysis
cell-cell communication using CellChat
Inference and analysis of cell-cell communication using CellChat
Document-Harmony-Integration
1:13 0.1-1.2
Document-Harmony
Integration by Harmony_on_logNormalization
1:17 0.5
Integration by Harmony_on_SCTransform_DATA
Same parameters Without findNeigbors and FindClusters
1-Harmony Integration_on_SCTransform
Its done on SCTransform data with 1:13 PCA 0.5 Res
Integration by Harmony_by_K_1-12
1:12
Integration by Harmony_by_K_1-30
0.5
Integration by Harmony_by_K_1-50
1:50 0.5
Integration by Harmony_by_K
1;20 0.5
Integration by CCA_by_K_1-12
used K code 1:20 log Norm 1:12 integration
Integration by CCA_by_K_1-20
used K code 1:20 log Norm 1:20 integration
Integration by CCA_by_K
used K code 1:20 log Norm 1:50 integration
Harmony Integration_Test3
Harmony Integration_Test2
Harmony Integration_Test1
PCA TEST and Harmony
CCA_harmony_0.5
dims: 1:15
CCA-Harmony_new
rpca-Harmony_new
Integration-Harmony
Integration-CCA
rpca-Integration
Corrected-UMAP
integration by Different Methods-rpca-CCA-Harmony
ADT Normalization
UMAP-0.7 with Annotation
Resolution Test
0.3-2
Resolution Test
Document
Escape Visualization
Document
Analysis of TCR-SS