Publications by Nadine Bestard
Integration embryos adults
Setup # libraries library(Seurat) # for scrnaseq analyisis ## Attaching SeuratObject library(here) # for reproducible paths ## here() starts at C:/Users/nbestard/OneDrive/Laura Load objects srt_old <- readRDS(here("Data", "HCA", "srt_opcs.RDS")) srt_dev <- readRDS(here("Data", "Embryos", "hca_harmony_opcs.rds")) The seurat adult object conta...
2076 sym R (20676 sym/36 pcs) 4 img
Differential expression
In this Document I aim to analyse the differences between the clusters. I will focus on the clusters that have been spotted of interest from sex_age_tissue_diff.Rmd Setup # libraries library(Seurat) # for scrnaseq analyisis ## Attaching SeuratObject library(here) # for reproducible paths ## here() starts at C:/Users/nbestard/OneDrive/Luise_HCA ...
4524 sym R (14968 sym/59 pcs) 36 img
Sex-Age-Tissue differences
Set-up Bellow we load the HCA object that has been filtered for bad quality samples (filter_bad_oligos_and_samples.R), for clusters that were formed by a single individual(filter_bad_clusters.Rmd) and annotated. General Cluster Plot the proportions for caseNo General parameters Some general distributions about the data. We started with equal n...
1290 sym R (7654 sym/9 pcs) 8 img
Annotation
Set-up Bellow we load the HCA object that has been filtered for bad quality samples (filter_bad_oligos_and_samples.R), and for clusters that were formed by a single individual(filter_bad_clusters.Rmd). General Cluster Annotation Click to expand the Neurons marker plots Click to expand the Stromal marker plots Click to expand the Astrocytes...
705 sym R (3554 sym/2 pcs) 17 img
Differential expression HCA-Dev
Setup Load objects Filter for markers only present in adult From Seurat Positive values of logFC indicate that the gene is more highly expressed in the first group. Therefore, as ident 1 is embryo and ident 2 is adult, we want negative values, more expressed in second group. There are 1720 up regulated genes in adult compared with the foetal on...
2209 sym R (3669 sym/3 pcs) 12 img
Differential expression WTvsKO (jpriller)
set-up Use of pseudo-bulk samples We perform the DE analysis separately for each label to identify cell type-specific transcriptional effects of KO condition. The actual DE testing is performed on “pseudo-bulk” expression profiles (Tung et al. 2017), generated by summing counts together for all cells with the same combination of label and s...
2330 sym
Differential expression WTvsKO (jpriller)
set-up Use of pseudo-bulk samples We perform the DE analysis separately for each label to identify cell type-specific transcriptional effects of KO condition. The actual DE testing is performed on “pseudo-bulk” expression profiles (Tung et al. 2017), generated by summing counts together for all cells with the same combination of label and s...
2330 sym
annotation 2 (jpriller)
Annotation We make a first rough annotation using known markers for different celltypes. ## Scale for 'colour' is already present. Adding another scale for 'colour', ## which will replace the existing scale. Click to expand the Neurons marker plots ## Scale for 'colour' is already present. Adding another scale for 'colour', ## which will rep...
2394 sym R (5732 sym/19 pcs) 34 img
clustering 2 (jpriller)
Set-up The workflow and explanations bellow are from OSCA Motivation Clustering is an unsupervised learning procedure that is used in scRNA-seq data analysis to empirically define groups of cells with similar expression profiles. It is worth stressing the distinction between clusters and cell types. The former is an empirical construct while the...
1574 sym R (958 sym/7 pcs) 8 img
First clustering (jpriller)
Set-up The workflow and explanations bellow are from OSCA Motivation Clustering is an unsupervised learning procedure that is used in scRNA-seq data analysis to empirically define groups of cells with similar expression profiles. It is worth stressing the distinction between clusters and cell types. The former is an empirical construct while the...
1304 sym R (958 sym/7 pcs) 8 img