Publications by Nasir Mahmood Abbasi
Wilcox Analysis RNA Top Markers
1. load libraries 2. load seurat object #Load Seurat Object load("/home/nabbasi/isilon/To_Transfer_between_computers/23-Harmony_Integration/0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj") Layers(All_samples_Merged@assays$RNA) [1] "data" "counts" 3. clusters vs the rest De...
7996 sym R (4712 sym/29 pcs) 8 img
NK Proliferating vs Control_filtred_on_mean
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("3-RNA_NK_Prolif_vs_Control_Filtered_by_MeanExp.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Volcano Plots library(dplyr) library(EnhancedVolcano) # Assuming you have a data frame named Malignant_CD4Tcells_vs_Normal_CD4Tcells...
18352 sym R (13777 sym/30 pcs) 8 img
HSPC vs Control_filtred_on_mean
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("3-RNA_HSPC_vs_Control_Filtered_by_MeanExp.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Volcano Plots library(dplyr) library(EnhancedVolcano) # Assuming you have a data frame named Malignant_CD4Tcells_vs_Normal_CD4Tcells # Fi...
18280 sym R (13468 sym/27 pcs) 7 img
P1 vs P2 Enrichment Final after Pseudobulk
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("Psedobulk_Deseq2_filtered_on_mean_P1_vs_P2.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval...
31451 sym R (25025 sym/61 pcs) 11 img
P2_vs_P3_Enrichment
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("Psedobulk_Deseq2_filtered_on_mean_p2_vs_P3.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0, na.rm = TR...
31905 sym R (26194 sym/63 pcs) 11 img
P1_vs_P3_Enrichment
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("Psedobulk_Deseq2_filtered_on_mean_P1_vs_P3.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval...
31441 sym R (25903 sym/62 pcs) 12 img
Pseudo_bulk_Enrichment_Final
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("2-Pseudobulk_Deseq2_LRT_filtered_on_mean.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 ...
31347 sym R (25618 sym/63 pcs) 13 img
Pseudo_bulk_Enrichment_Final
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("2-Pseudobulk_Deseq2_LRT_filtered_on_mean.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 ...
31523 sym R (25780 sym/65 pcs) 11 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-Pseudobulk_DEseq2_LRT_DE_with_libra.csv", header = T) 3. Create the EnhancedVolcano plot library(dplyr) library(EnhancedVolcano) # Assuming you have a data frame named Malignant_CD4Tcells_vs_Normal...
18204 sym R (13144 sym/38 pcs) 12 img
Pseudo_bulk_Enrichment_Final
1. load libraries 2. Load the filtered list on mean expression # Load the DE results from CSV df <- read.csv("2-Pseudobulk_Deseq2_LRT_filtered_on_mean.csv", stringsAsFactors = FALSE) DE_results_df <- df 3. Summarize Markers markers <- DE_results_df summarize_markers <- function(markers) { num_pval0 <- sum(markers$p_val_adj == 0) num_pval1 ...
26631 sym R (21873 sym/57 pcs) 14 img