Publications by Nasir Mahmood Abbasi
Harmony integrations of PBMC10x by patient origin and cell_line-theta-0.5 both
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-R_Objects/CD4Tcells_SCTnormalized_done_on_HPC_inluding_Patient_origin.robj") # Visualize before Harmony integration DimPlot(All_samples_Merged, reduction = "umap", group.by = "Patient_origin",...
14091 sym Python (19842 sym/42 pcs) 16 img
Different evaluation test on harmony integration on patient origin and cell_line-theta-0.5 both
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-R_Objects/CD4Tcells_harmony_integrated_0.5_theta_patientorigin_cell_line.Robj") 3. Harmony Visualization DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", ...
4721 sym Python (2733 sym/16 pcs) 15 img
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono regress nCount, percent.mt and rb and cell cycle 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 ── ✔ ...
22644 sym R (33542 sym/166 pcs) 27 img
Harmony integrations of PBMC10x by patient origin and cell_line-theta-1
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-R_Objects/CD4Tcells_SCTnormalized_done_on_HPC_inluding_Patient_origin.robj") # Visualize before Harmony integration DimPlot(All_samples_Merged, reduction = "umap", group.by = "Patient_origin",...
19661 sym Python (24364 sym/43 pcs) 15 img
Harmony integrations of PBMC10x by patient origin and cell_line-theta-0.5 both
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-R_Objects/CD4Tcells_SCTnormalized_done_on_HPC_inluding_Patient_origin.robj") # Visualize before Harmony integration DimPlot(All_samples_Merged, reduction = "umap", group.by = "Patient_origin",...
19673 sym Python (23771 sym/43 pcs) 15 img
Merged All samples with PBMC_10x and removed non CD4 T cells from Control and B cells from L4 and ILC, NK, CD14 Mono 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 ── ✔ ...
22535 sym R (32348 sym/165 pcs) 27 img
Harmony integrations of PBMC10x by cell_line-theta-0.5
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control after filtration load("../0-R_Objects/CD4Tcells_SCTnormalized_done_on_HPC_inluding_Patient_origin.robj") # Visualize before Harmony integration DimPlot(All_samples_Merged, reduction = "umap", group.by = "Patient_origin",...
19601 sym Python (22878 sym/43 pcs) 15 img
Merged All samples with PBMC_10x, Removed non CD4 T cells from Control, Apply_SCT_then_Harmony_theta-0.5
1. load libraries 2. Load Seurat Object #Load Seurat Object merged from cell lines and a control(PBMC) after filtration load("../0-R_Objects/SS_CD4_Tcells_Azimuth_Annotated_PBMC10x_final_for_SCT_and_Integration.robj") All_samples_Merged <- filtered_seurat rm(filtered_seurat) Summarizing Seurat Object # Display basic metadata summary head(All_...
21452 sym R (30571 sym/97 pcs) 28 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 #Load Seurat Object merged from cell lines and a control(PBMC) after filtration load("../0-R_Objects/All_Samples_Merged_with_10x_Azitmuth_Annotated.robj") All_samples_Merged An object of class Seurat 36752 features across 59355 samples within 5 assay...
15388 sym R (32318 sym/41 pcs) 5 img
Differential Expression Analysis - Filtering and Visualization
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-imp_Robj/1-MAST_with_batch_as_Covariate_with_meanExpression.csv", row.names = 1) markers_wilcox <- read.csv("0-imp_Robj/2-Wilcox_min.pct_logfcT-0_with_meanExpression.csv", row.names = 1) markers_...
10382 sym 6 img