Publications by Nadine Bestard
Cell and gene QC (jpriller)
Set-up The object has 32285 genes and 23007 cells before filtering Add cell QC metrics to the sce First we need to sort the gene names and gene symbols, because the default ensembl notation is not very handy. And then save the mitochondrial genes as such. Then we can use the scater package to add the quality per cell. This computes for each cell...
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Normalisation (jpriller)
Set-up Normalisation by deconvolution In order to correct for systematic differences in sequencing coverage between libraries we will normalise the dataset. This involves dividing all counts for each cell by a cell-specific scaling factor, often called a “size factor” (Anders and Huber 2010). The assumption here is that any cell-specific bia...
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feature selection and dimensioal reduction ( jpriller )
Set-up The workflow and explanations bellow are from OSCA library(SingleCellExperiment) library(scran) # For feature selcetion library(scater) # for pca library(ggplot2) # modify plots library(here) #reproducible paths age <- "old" sce <- readRDS(here("processed", age, "sce_norm_01.RDS")) Feature selection Motivation We often use scRNA-seq data...
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First annotation (jpriller)
Clustering at k60 ## Scale for 'colour' is already present. Adding another scale for 'colour', ## which will replace the existing scale. Annotation We make a first rough annotation using known markers for different celltypes. Click to expand the Neurons marker plots ## Scale for 'colour' is already present. Adding another scale for 'colour',...
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Cluster QC (jpriller)
set-up Introduction As in [publication] we will perform a cluster QC to remove clusters of poorer quality. This will be assessed by the number of UMI counts, the mitochondrial percentage, doublet analysis, ribosomal genes and the number of mice that contribute to each cluster. Moreover we will keep in mind our experimental groups in order to ens...
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Differential expression between KO-WT cell types (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...
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Differential abundance of cells between KO-WT clusters (jpriller)
set-up Dimensional reduction Proportion KO-WT In order to visualise the proportions from KO and WT for each cluster, we do not take in consideration the microglia, as these cells are only present in the control. We then normalise per number of cells per cluster. ## Genotype cluster Proportion ## 1 KO Gran & Mono 91.1...
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Cell cycle (jpriller)
set-up Visualise cyclins The cyclins control progression through the cell cycle and have well-characterized patterns of expression across cell cycle phases. cyclin A: activates DNA replication in S phase cyclin B: assembly of mitotic spindle to preparate for mitosis Cyclin D: move from G0 to G1 and S Cyclin E: prepares for DNAreplication just be...
1989 sym R (2566 sym/2 pcs)
index_2
testworkflowR Home workflowr Summary Checks Past versions Last updated: 2022-01-21 Checks: 1 1 Knit directory: testworkflowR/ This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions t...
1903 sym R (229 sym/1 pcs)
why is not the same as before?
testworkflowR Home About License workflowr Summary Checks Past versions Last updated: 2022-01-21 Checks: 1 1 Knit directory: testworkflowR/ This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The...
2155 sym R (134 sym/1 pcs) 1 tbl