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nmabbasi

NASIR MAHMOOD ABBASI

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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
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
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
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
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 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
TCR Analysis-Part2
New UMAP
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"
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_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_1-50
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
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
ADT Normalization
Resolution Test
0.3-2
Resolution Test
Document
Escape Visualization
Document
Analysis of TCR-SS