Comprehensive Microarray Data Analysis Pipeline

All microarray data was subjected to QC and ERCC spike-in assessments, and any failing samples were omitted from the analysis. Biological outliers were identified by comparing samples from related structures using hierarchical clustering and inter-array correlation measures. Data for samples passing QC were normalized in three steps: 1) “within-batch” normalization to the 75th percentile expression values; 2) “cross-batch” bias reduction using ComBat⁵⁷ ; and 3) "cross-brain" normalization as in step 1. Differential expression assessments were done using template vector correlation, where 1="in group" and 0="not in group", or by measuring the fold change, defined as mean expression in category divided by mean expression elsewhere. False discovery rates were estimated using permutation tests (Suppl. Methods). WGCNA was performed on all neocortical samples using the standard method^{36 ,58} , and on germinal layers by defining a consensus module in the 15 and 16pcw brains⁵⁹ , only including genes differentially expressed across these layers (5494 genes; ANOVA p<0.01, Benjamini-Hochberg adjusted). Gene list characterizations were made using a combination of module eigengene / representative gene expression, gene ontology enrichment using DAVID⁶⁰ , and enrichment for known brain-related categories (i.e.,^{61 ,62} ) using userListEnrichment⁶³ . Module C31 is depicted using VisANT⁶⁴ : the top 250 gene-gene connections based on topological overlap are shown, with histone genes removed for clarity. Rostral-caudal areal gradient genes were identified as follows: first, the center of each neocortical region was identified at 21pcw in Euclidean coordinates; second, the rostral/caudal region position was estimated as an angle along the lateral face of the brain centered at the temporal/frontal lobe juncture (ordering lobes roughly as frontal, parietal, occipital, temporal; Fig. 5c); third, for each brain gene expression in each layer was (Pearson) correlated with this region position; and finally, genes with R>0.5 in all four brains were identified. A similar strategy was used to identify unbiased areal gradient genes (Suppl. Methods). Enrichment of haCNSs was determined using hypergeometric tests. Samples in all plots are ordered in an anatomically relevant manner. Unless otherwise noted, all p-values are Bonferroni corrected for multiple comparisons.