Publications by Nasir Mahmood Abbasi
Sézary Syndrome Cell Line derived from each patient DE comparison
In this notebook we will be using clusters for each patient to comapre and we will be using MAST to see if that could improve results or we similar results as Wilcox. 1. load libraries #Differential Expression Analysis 2. load seurat object #Load Seurat Object L7 load("../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_S...
22470 sym R (140289 sym/75 pcs) 26 img
Sézary Syndrome Cell Line derived from each patient DE comparison
1. load libraries #Differential Expression Analysis 2. load seurat object All_samples_Merged An object of class Seurat 62900 features across 49305 samples within 6 assays Active assay: SCT (26176 features, 3000 variable features) 3 layers present: counts, data, scale.data 5 other assays present: RNA, ADT, prediction.score.celltype.l1, predict...
21594 sym R (18236 sym/72 pcs) 26 img
Sézary Syndrome Cell Line derived from each patient DE comparison
1. load libraries #Differential Expression Analysis 2. load seurat object All_samples_Merged An object of class Seurat 62900 features across 49305 samples within 6 assays Active assay: SCT (26176 features, 3000 variable features) 3 layers present: counts, data, scale.data 5 other assays present: RNA, ADT, prediction.score.celltype.l1, predict...
21594 sym R (18236 sym/72 pcs) 26 img
Differential Expression Analysis - Filtering and Visualization-NEWUMAP
1. Load Libraries 2. Load Data 2.1 Load CSV Files with Mean Expression 3. Summarize Results 3.1 Summary Function summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 <- sum(markers$p_val_adj == 1) num_significant <- sum(markers$p_val_adj < 0.05) num_upregulated <- sum(markers$avg_log2FC > 1) num_...
3683 sym 2 img
Differential Expression Analysis using Harmony Integrated Clusters
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...
11906 sym R (102387 sym/33 pcs) 5 img
Differential Expression Analysis - Filtering and Visualization-NEWUMAP
1. Load Libraries 2. Load Data 2.1 Load CSV Files with Mean Expression # Load the CSV files with mean expression data markers_mast_batch <- read.csv("0-robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", row.names = 1) markers_wilcox <- read.csv("0-robj/2-Wilcox_min.pct_logfcT-0_with_meanExpression.csv", row.names = 1) markers_mast_no_...
10355 sym 6 img
DE(Malignat_vs_Normal_CD4Tcells) of Harmony Integration
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj") 3. Visulization of Harmony integrated Object DefaultAssay(All_samples_Merged) <- "SCT" DimPlot(All_samp...
14070 sym R (8506 sym/14 pcs) 5 img
Inference and analysis of cell-cell communication using CellChat-SS_cellLines_HarmonyIntegration_PBMC10x
1. load libraries 2. Load Seurat Object #Load Seurat Object Integrated Object load("../0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized.Robj_cleaned_mt.robj") Loading required package: SeuratObject Loading required package: sp ‘SeuratObject’ was built under R 4.4.1 but the current version i...
22636 sym R (117607 sym/107 pcs) 48 img
Different Resolution test on harmony integration
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-robj/3-Harmony_Integrated_All_samples_Merged_CD4Tcells_final.Robj") 3. Harmony Visualization DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = TRUE...
4645 sym Python (2721 sym/16 pcs) 15 img
Different Resolution Tables on harmony integration
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("0-robj/CD4_T_cells_Harmony_integrated_0.5_Theta_Patient_origin_and_orig_ident_Annotated_again.robj") 3. Harmony Visualization DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line"...
7620 sym R (29594 sym/62 pcs) 17 img