Trastulla, L. | Search bioRxiv

bioRxiv2020

Temporally-Divergent Regulatory Mechanisms Govern Neuronal Development and Diversification in the Neocortex

Mammalian neocortical neurons span one of the most diverse cell type spectra of any tissue. The regulatory strategies that neurons use during progressive development and maturation remain unclear. We present an integrated single-cell epigenomic and transcriptional analysis of individual classes of neurons from both mouse and marmoset neocortex, sampled during both early postmitotic stages of identity acquisition and later stages of neuronal plasticity and circuit integration. We find that in both species, the regulatory strategies controlling these early and late stages diverge: early postmitotic neurons use molecular regulatory programs with broader tissue distribution and greater evolutionary conservation, while programs active during later neuronal maturation implement more brain- and neuron-specific mechanisms showing greater evolutionary divergence. The data uncovers a temporally-regulated shift in regulatory choices, likely reflecting unique evolutionary constraints on distinct events of neuronal development in the neocortex.

neuroscience↗

bioRxiv2020

Integrative Network Fusion: a multi-omics approach in molecular profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes (0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes1.

bioinformatics↗