Publications by Nasir Mahmood Abbasi
Sézary Syndrome Cell Line Analysis_NewUMAP_Wilcox_RNA_Assay
1. load libraries #Differential Expression Analysis 2. load seurat object #Differential Expression Analysis # 3. Pairwise Comparisons library(Seurat) library(dplyr) library(tibble) library(EnhancedVolcano) # Extract normalized expression values for RNA assay expression_data_RNA <- GetAssayData(All_samples_Merged, assay = "RNA", slot = "data") # ...
8005 sym R (7061 sym/25 pcs) 5 img
adding Mean Expression_P1_vs_P3 - Filtering and Visualization_Patients
1. Load Libraries 2. load seurat object #Load Seurat Object L7 load("../../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj") All_samples_Merged An object of class Seurat 62900 features across 49305 samples within 6 assays Active assay: SCT (26176 features, 3000 variable featu...
5730 sym R (4101 sym/15 pcs)
adding Mean Expression_P1_vs_P2 - Filtering and Visualization_Patients
1. Load Libraries 2. load seurat object #Load Seurat Object L7 load("../../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj") All_samples_Merged An object of class Seurat 62900 features across 49305 samples within 6 assays Active assay: SCT (26176 features, 3000 variable featu...
5706 sym R (3980 sym/15 pcs)
adding Mean Expression_P2_vs_P3 - Filtering and Visualization_Patients
1. Load Libraries 2. load seurat object #Load Seurat Object L7 load("../../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj") All_samples_Merged An object of class Seurat 62900 features across 49305 samples within 6 assays Active assay: SCT (26176 features, 3000 variable featu...
5730 sym R (4102 sym/15 pcs)
Gene Enrichment Analysis (P1_vs_P3)_on_Filtered_meanExp
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_P1_vs_P3_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malignant_CD4Tcell...
23802 sym R (18260 sym/45 pcs) 17 img
Gene Enrichment Analysis (P2_vs_P3)_on_Filtered_meanExp
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_P2_vs_P3_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malignant_CD4Tcell...
23826 sym R (17770 sym/39 pcs) 11 img
Gene Enrichment Analysis (P1_vs_P2)_on_Filtered_meanExp
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_P1_vs_P3_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malignant_CD4Tcell...
23802 sym R (18260 sym/45 pcs) 17 img
fgsea-L5 vs Control(Normal CD4 Tcells)
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("2-All_genes_celllines_vs_normal_updated_with_mean_expression/Updated_DE_Results_L5_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4T...
17608 sym R (12225 sym/29 pcs) 12 img
fgsea-L6 vs Control(Normal CD4 Tcells)
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("2-All_genes_celllines_vs_normal_updated_with_mean_expression/Updated_DE_Results_L6_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4T...
17608 sym R (12225 sym/29 pcs) 12 img
fgsea-L7 vs Control(Normal CD4 Tcells)
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("2-All_genes_celllines_vs_normal_updated_with_mean_expression/Updated_DE_Results_L7_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4T...
17608 sym R (12225 sym/29 pcs) 12 img