Publications by Nasir Mahmood Abbasi
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"...
7700 sym R (31285 sym/62 pcs) 17 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/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"...
4789 sym Python (2749 sym/16 pcs) 15 img
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 regress nCount, percent.mt and rb and apply SCT
1. load libraries Loading required package: SeuratObject Loading required package: sp Attaching package: 'SeuratObject' The following objects are masked from 'package:base': intersect, t ── Installed datasets ──────────────────────────────── SeuratData v0.2.2.9001 ── ✔ ...
19663 sym R (27644 sym/170 pcs) 23 img
use Annotated Robj including PBMC10x to remove NonCD4Tcells from Control and keep just CD4Tcells
##. In this script I will remove Non T cells from PBMC 1. load libraries 2. Load Seurat Object Cell type Distribution to check clusters Cell type Distribution barplot 3. filter cells just keep CD4 T cells Cell type Distribution to check clusters 4. filter B cells from L4 Cell type Distribution to check clusters 5. Save the Seurat object as...
9976 sym
Identify the cluster 14(0.9) from harmony integration in normal CD4 Tcells
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("CD4Tcells_harmony_integrated_0.5_theta_patientorigin_orig_ident.Robj") 3. Identify the cluster FeaturePlot(All_samples_Merged, features = c("CLEC4E", "IL1B", "LILRA5")) VlnPlot(All_samples_Merged, features = c("CLEC4E", "IL1B...
3876 sym Python (967 sym/3 pcs) 4 img
Different Resolution Tables on harmony integration on patient origin and orig.ident-theta-0.5,0.5
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("CD4Tcells_harmony_integrated_0.5_theta_patientorigin_orig_ident.Robj") 3. Harmony Visualization DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = TRUE, ...
7410 sym R (31085 sym/61 pcs) 16 img
Different Resolution test on harmony integration on patient origin and orig.ident-theta-at-0.5,0.5
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("CD4Tcells_harmony_integrated_0.5_theta_patientorigin_orig_ident.Robj") 3. Harmony Visualization DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = TRUE, ...
4709 sym Python (2719 sym/16 pcs) 15 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("0-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malign...
9461 sym R (6105 sym/11 pcs) 6 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("0-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malign...
17149 sym R (11756 sym/20 pcs) 8 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("0-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", header = T) 3. Create the EnhancedVolcano plot EnhancedVolcano(Malignant_CD4Tcells_vs_Normal_CD4Tcells, lab = Malign...
23500 sym R (17836 sym/45 pcs) 19 img