Citation

If you use our work, please cite:

Daugelaite K, Lacour P, Winkler I, Koch M, Schneider A, Schneider N, Tolkachov A, Nguyen XP, Vilkaite A, Rehnitz J, Odom DT, Goncalves A. (2023)
Superovulation and ageing perturb oocyte-granulosa cell transcriptomes and communication
bioRxiv (2023)
doi: https://doi.org/10.1101/2023.10.30.563978

From raw counts to R objects

create_seurat_age_ov.R - for natural and superovulated, young and old oocytes and granulosa cells Smart-Seq2 data (E-MTAB-13479)
create_seurat_totalrna.R - for natural and superovulated oocytes total-RNA seq data (E-MTAB-13474)
create_seurat_ivf_mouse.R - for IVF-derived mouse embryos (morula or blastocyst) and corresponding granulosa cells, Smart-seq2 data (E-MTAB-13480)

These scripts create the Seurat objects used by the other scripts from the raw count tables.

Scnorm.R - normalizes count data using the SCnorm method to take into account gene length (used for cell communication and classifier scripts).

Differential gene expression and overrepresentation

dge.R - differential expression analysis using DESeq2 for aging and superovulation dataset
ora.R - over-representation analysis of genes found by DESeq2

dge_SNvS.R - differential expression analysis using DESeq2 between S and SN granulosa cells (as identified by cell-to-cell communication analysis and transcription factor activities)

Total-RNA analysis (repolyadenylation, deadenylation, and degradation)

totalrna_vs_smartseq.R - compares the expression of known genes between natural and superovulated oocytes in a polyA-biased technology (Smart-Seq2) and a non-biased one (total RNA)

Cell-to-cell communication

cell_communication.R - computes ligand-receptor interaction score based on gene expression level and CellChatDB annotation

SCENIC and AUCell

scenic.R - runs SCENIC analysis on oocytes and granulosa cells from the aging and superovulation dataset
scenic_post.R - tests for significant differentially active pathways between conditions
tf_scenic_pathway.R - computes the overlap between the TFs targets and the pathways, plots the results in a heatmap

aucell.R - computes pathway activity scores

Granulosa cells classifier

data_preparation_genes.R - selects genes that will be used in the gene classifier (based on differentially expressed genes (DEG) between S and SN granulosa cells)
data_preparation_tfs.R - selects genes that will be used in the TF classifier (based on SCENIC results)
auc_classifier.R - trains different granulosa classifiers using TF activity scores
genes_classifier.R - trains different granulosa classifiers using DEG
gc_scenic_scoring_classifier.R - computes TF activity scores of new samples using the same regulons as the ones in the training dataset (results from the SCENIC analysis)
classifier_combined.R - predicts the class of new granulosa cells using the two classifiers

Embryo development and copy number variation

pseudotime_embryos.R - creates a reference developmental trajectory and calculates a developmental pseudotime for each embryo to assess link between granulosa cell classification and developmental transcriptional trajectory CNV_prep.R - prepares embryo data for inferCNV run
CNV_runner.R - runs inferCNV on embryo data

Validation experiments (HCR and qPCR)

hcr_analysis_and_plots.R - validation of Esr2 expression in natural and superovulated young granulosa cells using HCR fluorescence
qPCR_analysis_and_plots.R - qPCR quantification of genes used in the granulosa cells classifier

Shannon entropy

Shannon_entropy.R - computes differential Shannon entropy for the aging and superovulation dataset

Human data analysis

human_dge_gsea.R - computes differential gene expression on human granulosa cells (E-MTAB-13496) and compares the enriched pathways identified using fgsea to the ones found in mouse

Interaction between aging and superovulation

pca_projection.R - uses a PCA projection approach to summarize the non-linearity between aging and superovulation effects

Old scripts used in previous versions of the manuscript

pseudotime_oc_gc.R - performs pseudotime analysis based on highly variable genes or pathways of interest (e.g. meiosis)