Publications by Kennedi Todd
Myeloid Cells Recluster
Setup Load libraries library(ComplexUpset) # upset() library(dplyr) library(ggrepel) library(ggtree) library(gtools) # smartbind() library(parallel) #detectCores() library(Seurat) # Idents() library(tibble) # rownnames_to_column() library(UpSetR) # upset() options(mc.cores = detectCores() - 1) Read object mouse.annotated <- readRDS("../../r...
1589 sym R (41268 sym/99 pcs) 38 img
Gene Enrichment Analysis: Lymphocyte Subpopulations
GEA with p_val_adj of 0.05 Setup # libraries library(gprofiler2) # read data isoform.df <- read.table("../../results/recluster/lymphocytes/DEGs/lymphocytes_DEGs_E4_vs_E3.tsv",sep="\t",header=TRUE) age.df <- read.table("../../results/recluster/lymphocytes/DEGs/lymphocytes_DEGs_14_vs_2_months.tsv",sep="\t",header=TRUE) sex.df <- read.table("../.....
117 sym R (650 sym/3 pcs)
Lymphocytes Recluster
Setup library(ComplexUpset) # upset() library(dplyr) library(ggrepel) library(ggtree) # BuildClusterTree() library(gtools) library(parallel) #detectCores() library(Seurat) # Idents() library(tibble) # rownnames_to_column() library(UpSetR) # fromList() options(mc.cores = detectCores() - 1) mouse.annotated <- readRDS("../../rObjects/mouse_annot...
1201 sym R (29407 sym/84 pcs) 31 img
APP Results
Notes This only compares SVs that made the VCF file and that were converted to BED. CNVs, translocations, aneuploidy, and complex SVs are not present in the VCF. This compares mainly insertion, deletions, and duplications. Intersect means a filter was applied. If file A intersects file B then file B is used as a filter. So, unique features in A...
1088 sym R (1850 sym/16 pcs)
Gene Enrichment Analysis: Myeloid-like and Vascular Cells
GEA with p_val_adj of 0.1 Setup # libraries library(gprofiler2) # read data isoform.df <- read.table("../../results/recluster/myeloid_like_and_vascular/DEGs/myeloid_like_and_vascular_E4_vs_E3_DEGs.tsv",sep="\t",header=TRUE) age.df <- read.table("../../results/recluster/myeloid_like_and_vascular/DEGs/myeloid_like_and_vascular_14_vs_2_months_DEGs...
1308 sym R (1065 sym/11 pcs)
Myeloid-like and Vascular Cells Recluster
Setup Load libraries library(ComplexUpset) # upset() library(dplyr) library(ggrepel) library(ggtree) library(gtools) # smartbind() library(parallel) #detectCores() library(Seurat) # Idents() library(tibble) # rownnames_to_column() library(UpSetR) # upset() options(mc.cores = detectCores() - 1) Read object mouse.annotated <- readRDS("../../r...
1368 sym R (33756 sym/98 pcs) 38 img
Mouse scRNAseq CellBender Annotation
Setup Working directory knitr::opts_knit$set( root.dir = "/research/labs/neurology/fryer/m214960/Ferreira_Da_Mesquita/scripts/R") Libraries # load packages library(ComplexUpset) # upset() library(dplyr) library(ggrepel) # geom_text_repel() library(ggtree) # BuildClusterTree() library(gtools) library(gridExtra) library(parallel) # detectCores...
4735 sym R (55100 sym/195 pcs) 84 img
Mouse scRNAseq Processing
Setup Working directory knitr::opts_knit$set( root.dir = "/research/labs/neurology/fryer/m214960/Ferreira_Da_Mesquita/scripts/R") Load libraries # load libraries library(cowplot) # plot_grid() library(dplyr) # ungroup() library(ggplot2) # ggplot() library(grid) # grid.arrange() library(gridExtra) # grid.arrange() library(parallel) # dete...
15038 sym R (34978 sym/167 pcs) 41 img
Gene Enrichment Analysis: Myeloid Cell Subpopulations
Setup # libraries library(gprofiler2) # read data isoform.df <- read.table("../../results/recluster/myeloid_cells/myeloid_cells_E4_vs_E3_DEGs.tsv",sep="\t",header=TRUE) age.df <- read.table("../../results/recluster/myeloid_cells/myeloid_cells_14_vs_2_months_DEGs.tsv",sep="\t",header=TRUE) sex.df <- read.table("../../results/recluster/myeloid_cel...
167 sym R (732 sym/5 pcs)
Gene Enrichment Analysis: Sex Up-regulated
Setup # libraries library(gprofiler2) # read data isoform.df <- read.table("../../results/DEGs/E4_vs_E3_DEGs.tsv",sep="\t",header=TRUE) age.df <- read.table("../../results/DEGs/14_vs_2_months_DEGs.tsv",sep="\t",header=TRUE) sex.df <- read.table("../../results/DEGs/female_vs_male_DEGs.tsv",sep="\t",header=TRUE) # filter by pval isoform.df <- iso...
646 sym R (801 sym/9 pcs)