Publications by Nasir Mahmood Abbasi
L4 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L4_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23816 sym R (18420 sym/43 pcs) 17 img
L5 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L5_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23824 sym R (22354 sym/51 pcs) 17 img
L6 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L6_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23814 sym R (18331 sym/42 pcs) 16 img
L7 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L7_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23824 sym R (22354 sym/51 pcs) 17 img
L1 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L1_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23822 sym R (22346 sym/50 pcs) 16 img
L2 vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("../../2-DE_on_Harmony_Integration/Filtered_DE_Results_L2_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab...
23816 sym R (18839 sym/43 pcs) 17 img
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)-GSEA
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("1-MAST_with_SCT_batch_patient_cellline_as_Covariate_with_meanExpression.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, la...
23882 sym R (18118 sym/42 pcs) 16 img
Differential Expression Analysis of Malignant CD4Tcells 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("1-MAST_with_SCT_batch_patient_cellline_as_Covariate_with_meanExpression.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, la...
17357 sym R (11967 sym/30 pcs) 12 img
Cell lines vs Normal Control-DE-NewUMAP
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 features...
11116 sym R (368044 sym/45 pcs) 16 img
Filtering and Visualization(cell lines vs Control)
1. Load Libraries 2. Load Data 2.1 Load CSV Files with Mean Expression # Define cell lines cell_lines <- c("L1", "L2", "L3", "L4", "L5", "L6", "L7") # Define filtering criteria mean_expr_threshold <- 0.2 p_val_threshold <- 0.05 logfc_threshold <- 1 # Summary Function summarize_markers <- function(markers, cell_line) { num_pval0 <- sum(marker...
9588 sym R (10306 sym/8 pcs) 21 img