Publications by Nasir Mahmood Abbasi
Wilcox-SCT-Newumap-DE Analysis
1. Load Libraries 2. Load Seurat Object 3. Set Up Identifiers for Clustering # Assign cluster identities to the Seurat object Idents(All_samples_Merged) <- "seurat_clusters" DimPlot(All_samples_Merged, reduction = "umap", group.by = "seurat_clusters",label = T, label.box = T, repel = T) + ggtitle("Harmony Integration - By Clusters") 4. Diffe...
24759 sym R (18064 sym/52 pcs) 15 img
fgsea- 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 library(dplyr) library(EnhancedVolcano) # Assuming you have a data fram...
18274 sym R (11774 sym/28 pcs) 12 img
L2_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L2_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L2_vs_C...
18706 sym R (17084 sym/48 pcs) 12 img
L3_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L3_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L3_vs_C...
18696 sym R (13864 sym/38 pcs) 12 img
L4_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L4_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L4_vs_C...
18696 sym R (13865 sym/38 pcs) 12 img
L5_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L5_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L5_vs_C...
18696 sym R (13863 sym/38 pcs) 12 img
L6_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L6_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L6_vs_C...
18695 sym R (13954 sym/38 pcs) 11 img
L7_vs_Control_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("Filtered_DE_Results_L7_with_MeanExpr.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_dir <- "L7_vs_C...
18696 sym R (13863 sym/38 pcs) 12 img
L5_vs_L6_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_L5_vs_L6_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18632 sym R (13817 sym/38 pcs) 12 img
L5_vs_L7_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_L5_vs_L7_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18632 sym R (14047 sym/41 pcs) 9 img