Publications by Nasir Mahmood Abbasi
P1_vs_P2_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_P1_vs_P2_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18775 sym R (13415 sym/29 pcs) 12 img
P1_vs_P3_Enrichment
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 library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18632 sym R (13818 sym/38 pcs) 12 img
P2_vs_P3_Enrichment
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 library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18632 sym R (13981 sym/40 pcs) 10 img
L3_vs_L4_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_L3_vs_L4_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18630 sym R (14136 sym/40 pcs) 8 img
L1_vs_L2_Enrichment
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_L1_vs_L2_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot library(ggplot2) library(EnhancedVolcano) library(dplyr) # Define the output directory output_di...
18636 sym R (13819 sym/38 pcs) 12 img
after_filtering_on_MeanExpression
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes markers <- read.csv("../1-MAST_with_SCT_batch_patient_cellline_as_Covariate_with_meanExpression.csv", header = T) 3. Summarize Markers summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 <- sum(markers$p_val_a...
6894 sym R (4318 sym/11 pcs) 2 img
Differential Expression Analysis of Malignant CD4Tcells vs Control(Normal CD4 Tcells)-GSEA-after_filtering
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes markers <- read.csv("../1-MAST_with_SCT_batch_patient_cellline_as_Covariate_with_meanExpression.csv", header = T) 3. Summarize Markers summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 <- sum(markers$p_val_a...
6894 sym R (4318 sym/11 pcs) 2 img
P1 vs P2
1. load libraries 2. Perform DE analysis using Malignant_CD4Tcells_vs_Normal_CD4Tcells genes Malignant_CD4Tcells_vs_Normal_CD4Tcells <- read.csv("comparison_P1_vs_P2_with_mean_expression_filtered.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malignant_CD4Tcell...
23801 sym R (17376 sym/41 pcs) 16 img
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)