R Analysis for fire mice scRNAseq

Overall

cellrangerQC

OLD

Done:

• The first cell and gene quality control is in QC

• The normalisation by deconvolution is in normalise

• Feature selection and dimensional reduction in feature_selection_dimred

• Clustering at different resolutions, clustering_01

• First rough annotation in annotation_01

• Cluster QC in clusterQC_k5, now includes doublets detection

• Stricter cell and gene QC; dimensional reduction and feature selection with the cleaned data

• Clustering with different resolutions clustering_02

• Annotation annotation_02

• Differential gene expression between WT and KO for each celltype DE_WT_KO_celltype and each cluster DE_WT_KO_k20

• Differential abundance of cells between clusters between WT and KO:

  • Basic DA_between WT_KO
  • Using miloR, with all the celltypes DA_miloR
  • Using miloR, with oligodendroglia DA_oligo_miloR

• Cell cycle in cell_cycle

• Analyse Oligos separate in oligos, with DA in DA_oligo_miloR

Interactive Shinyapp with the analysis until this stage.

YOUNG

• The first cell and gene quality control is in QC_young

• The normalisation by deconvolution is in normalise_young

• Feature selection and dimensional reduction in feature_selection_dimred_young

• Batch correction in batchcorrect_young

• Clustering at different resolutions, clustering_01

• First rough annotation in annotation_01

• ClusterQC

• Stricter cell and gene QC; dimensional reduction and feature selection with the cleaned dataQC_norm_featrueselect_dimred_02

• Clustering with different resolutions clustering_02

• Annotation annotation_02 - the marker plot is missing

• Differential gene expression (DE) between WT and KO and between WT and HETs for each cluster DE_WT_KO_HET_clusters, DE_WT_HET_microglia and DE_WT_HET_BAMs

• Differential abundance of cells between clusters between WT KO and HET DA_miloR

• Trajectory analysis Oligodendroglia here

• Interactive Shinyapp

To Do/ In Progress

• Remove labels from Milo plots

• Trajectory analysis Astros?

• DE using MAST?

Very OLD

Done:

• The first cell and gene quality control is in QC and QC_filtered

• The normalisation by deconvolution in normalise

• Feature selection and dimensional reduction in feature_selection_dimred

• Clustering at different resolutions, clustering_01

• First rough annotation annotation_01

Interactive Shinyapp with the analysis until this stage.

• Cluster QC in clusterQC

• Stricter cell and gene QC

• dimensional reduction and feature selection with the cleaned data

• Clustering with different resolutions clustering_02

• Annotation annotation_02

• Differential abundance of cells between clusters between WT and KO DA_miloR

• Differential gene expression (DE) between WT and KO for:

  • Each cluster DE_WT_KO_clusters
  • Each celltype
  • Between the KO specific oligo clusters and other oligos Olig2 and Olig3
  • The KO specific OPC cluster and other OPCs here.