Data and code for Fitzgerald et al: MDD seeded co-expression networks
Bibliographic record
Abstract
Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder" Generated data Data About All_GTEx_DLPFC_networks.RData Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex my_big_negative_GTEx_DLPFC_list.RData "All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05 my_big_positive_GTEx_DLPFC_list.RData "All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05 Generated code File About Related figure Chang_bootstrap.R Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states Fig 4G CMC_QC.R Quality control for the common mind consortium data for validation of coexpression networks Supp Cont_vs_MDD_modscores.R Generating module scores in snRNA-seq data Fig 4E FADS1_clustering.R Clustering of snRNA-seq data using genes coexpressed with FADS1 Fig 5 gene_analysis.sh Annotation of GWAS summary statistics using Hi-C data Fig 2A gene_set_analysis.sh GWAS enrichment analysis using MAGMA Fig 6C GTEx_coexp_networks.R Generating seeded coexpression networks for MDD risk genes in the GTEx dataset Fig 2B GTEx_QC_1.R Filtering of the GTEx dataset NA GTEx_QC_2.R Normalisation and regression of technical covariates from the GTEx data NA Labonte_et_al_QC.R Quality control, filtering and regression of technical covariates from the Labonte et al dataset Fig 4F Milo_analysis.R Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei Fig 5H Nagy_et_al_astro_subsetting.R Subsetting astrocytes from the full Nagy et al snRNA-seq dataset NA Network_analysis.R To generate and analyse a graph of coexpression networks Fig 3E NicheNet.R For a NicheNet analysis to infer patterns of cell-cell communication Fig 6F Vizium_analysis.R Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states Fig 5F
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".