PsychENCODE Integrative Analysis




Integrative Analysis

An integration of data across the capstone projects to build a model taking QTLs as inputs and providing both phenotype predictions as well as functional modules involved.

Integrative Analysis for each brain phenotype

  1. Integrative model parameters for all phenotypes:

  2. Multi-level functional enrichment analysis (DSPN-mod) and Weighted Gene Co-Expression Analysis (WGCNA) modules:

  3. Transcription Factor - Target Gene - Enhancer linkages:

    1. Gene regulatory network 1 (GRN 1):

    2. Gene regulatory network 2 (GRN 2):

  4. HiC-derived Enhancer - Gene linkages:

  5. Schizophrenia-associated genes:

  6. Matlab code and formatted data for the DSPN:




Derived Data Types

Gene expression matrix, enhancer lists, eQTL and cQTL maps, DEX genes, gene co-expression modules, PCA/RCA-based clustering of RNA-seq data and epigenetic data, decomposition and deconvolution of cell-type-specific RNA-seq.

Differentially Expressed (DEX) and Spliced Genes/Transcripts and Gene/Isoform Co-expression modules

This resource provides sets of genes that exhibit significantly different expression levels between different groups of samples.

  1. Disorder DEX Genes and Transcripts, and Differentially Spliced Genes of PsychENCODE samples (from Disorder Analysis Paper):

  2. Gene and Isoform Co-Expression Modules calculated using Weighted Gene Co-Expression Analysis (WGCNA) on the PEC RNA-seq samples (from the Cross-Disorder Analysis; included as supplementary table S5 in Gandal et al 2018; see Cross-disorder_README for details on annotations):

Bulk RNA-seq Decomposition and Deconvolution with Single-cell Data

  1. Brain Cell-type Marker Genes and Single-cell Expression Data (in units of TPM), from PEC (Developmental), Darmanis et al. 2015 and Lake et al. 2016

  2. Brain Cell-type Marker Genes and Single-cell Expression Data (in units of UMI), from PEC (Adult) and Lake et al. 2018

  3. External references: Darmanis et al. 2015, Proc. Nat. Acad. Sci. U.S.A. 112(23), Pgs. 7285-90; Lake et al. 2016, Science 352(6293), Pgs. 1586-90; Lake et al. 2018, Nat. Biotechnol. 36(1), Pgs. 70-80

  4. Cell Fractions Derived from Deconvolution:

  5. Decomposition through Non-negative Matrix Factorization (NMF):




Pipeline-Processing Results

RNA-seq quantifications, ChIP-seq signals and peaks, Brain Transcriptionally Active Regions (TARs), Imputed Genotypes (secured),and Phenotypes.

Access to all files tagged as "private" is login-secured. The raw data used in these publications are available to the research community as described under Access Instructions. Note that Synapse files will be made accessible in December 2018.



Raw Data

Alignment files for the various experiments, chip arrays for the SNP genotyping assays and phenotype metadata for the different studies under the consortium; external links are provided for the data sources on Synapse, and the GTEx consortium and Roadmap Epigenomics Consortium web portals.

Access to all files tagged as "private" is login-secured. The raw data used in these publications are available to the research community as described under Access Instructions. Note that Synapse files will be made accessible in December 2018.

List of all datasets used in the integrative analysis

List of datasets including ssource study, disease status of samples, source tissue(s), downstream analyses conducted using the data and the number of datasets: RAW-01_PEC_Table_of_Datasets xlsx28KB