Scenarios of future Indian electricity demand accounting for space cooling and electric vehicle adoption.

India is expected to witness rapid growth in electricity use over the next two decades. Here, we introduce a custom regression model to project electricity consumption in India over the coming decades, which includes a bottom-up estimate of electricity consumption for two major growth drivers, air conditioning, and vehicle electrification. The model projections are available at a customizable level of spatial aggregation at an hourly temporal resolution, which makes them useful as inputs to long-term electricity infrastructure planning studies. The approach is used to develop electricity consumption data sets spanning various technology adoption and growth scenarios up to the year 2050 in five-year increments. The aim of the data is to provide a range of scenarios for India's demand growth given new technology adoption. With long-term hourly demand projections serving as an essential input for electricity infrastructure modeling, this data publication enables further work on energy efficiency, generation, and transmission expansion planning for a fast-growing and increasingly important region from a global climate mitigation perspective.

Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases.

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We find that immunoStates significantly reduces biological and technical biases. Importantly, we find that different methods have virtually no or minimal effect once the basis matrix is chosen. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.