DOI: 10.5061/dryad.4b8gthtkr
title: Raw differential gene expression data, data S1, from: Molecular cascades and cell type-specific signatures in ASD revealed by single cell genomics

description: Genomic profiling in post-mortem brain from autistic individuals has
 consistently revealed convergent molecular changes. What drives these
 changes and how they relate to genetic susceptibility in this complex
 condition is not understood. We performed deep single nuclear RNA
 sequencing (snRNAseq) to examine cell composition and transcriptomics,
 identifying dysregulation of cell type-specific gene regulatory networks
 (GRNs) in autism, which we corroborated using snATAC-seq and spatial
 transcriptomics. Transcriptomic changes were primarily cell type-specific,
 involving multiple cell types, most prominently interhemispheric and
 callosal-projecting neurons, interneurons within superficial laminae, and
 distinct glial reactive states involving oligodendrocytes, microglia, and
 astrocytes. Autism-associated GRN drivers and their targets were enriched
 in rare and common genetic risk variants, connecting autism genetic
 susceptibility and cellular and circuit alterations in the human brain.
 This data is the raw differential gene expression comparing ASD versus CTL
 subjects for each cell cluster. 
description: Please see Manuscript for detailed information.
 In Brief: we generated Pseudobulk expression ASD vs CTL analysis
 by cell type. We generated pseudobulk counts for each sample by adding
 counts from the same cell type. Then pseudobulk counts are normalized by
 variance stabilizing transformation method. To identify genes
 differentially expressed in ASD compared to control in each cell type, we
 examined covariates with top 5 PCs from normalized pseudo-bulk expression
 matrix. We identified the following covariates consistently correlated
 with top 5PCs for each cell type: age, PMI, BrainRegion, SeqBatch,
 Mito_perc, and ngenes. We then randomly selected subjects 500 times and
 calculated average beta to regress out effects of these covariates. Then
 we used limma-voom to identify differentially expressed genes for each
 cluster.
description: # Raw differential gene expression data, data S1, from: Molecular cascades
 and cell type-specific signatures in ASD revealed by single cell genomics
 Raw Differential Gene
 Expression data, Data S1, from "Molecular cascades and cell
 type-specific signatures in ASD revealed by single cell genomics" ##
 Description of the data and file structure Excell document with raw
 differential gene expression ASD vs. CTL per cell-type cluster. The first
 column is a drop-down selection to select which cell-type to view the
 differential gene expression results. The second (ASDvCTL) and third
 (CTLvASD) columns are the LOGFC value for each differential gene for a
 given cell type, the fourth (ASDvCTL) and fifth (CTLvASD) column are the
 p-values for each gene for a given cell type, the sixth (ASDvCTL) and
 seventh (CTLvASD) columns are the FDR-values for each gene for a given
 cell type, and the last column is the gene name. ## Sharing/Access
 information Links to other publicly accessible locations of the raw and
 process single-cell data: *
 ##
 Code/Software Code for the analysis:
 -