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The complexity, dynamics, and scale of data acquired by modern biotechnology increasingly favor model-free computational methods that make minimal assumptions about underlying biological mechanisms. For example, single-cell transcriptome and proteome data have a throughput several orders more than bulk methods. Many model-free statistical methods for pattern discovery such as mutual information and chi-squared tests, however, require discrete data. Most discretization methods minimize squared errors for each variable independently, not necessarily retaining joint patterns. To address this issue, we present a joint grid discretization algorithm that preserves clusters in the original data. We evaluated this algorithm on simulated data to show its advantage over other methods in maintaining clusters as measured by the adjusted Rand index. We also show it promotes global functional patterns over independent patterns. On single-cell proteome and transcriptome of leukemia and healthy blood, joint grid discretization captured known protein-to-RNA regulatory relationships, while revealing previously unknown interactions. As such, the joint grid discretization is applicable as a data transformation step in associative, functional, and causal inference of molecular interactions fundamental to systems biology. The developed software is publicly available at https://cran.r-project.org/package=GridOnClusters 

Aerosol‐cloud‐precipitation interactions represent one of the most significant uncertainties in climate simulation and projection. In particular, the impact of aerosols on precipitation is highly uncertain due to limited and conflicting observational evidence. A major challenge is to distinguish the effects of different types of aerosols on precipitation associated with deep convective clouds, which produces most of the precipitation in East Asia. Here, we use 9‐yr observations from multiple satellite‐borne sensors and find that the occurrent frequency of heavy rain increases while that of light rain decreases with the increase of aerosol optical depth (AOD) for dust and polluted continental aerosol types. For average hourly precipitation amount, elevated smoke tends to suppress deep convective precipitation, while dust and polluted continental aerosols enhance precipitation mainly through the invigoration of deep convection. The invigoration effect is more significant for clouds with higher cloud base temperature (CBT), while no significant invigoration is observed when CBT is <12°C. A great contrast is found for the response of average hourly precipitation amount to aerosols over ocean and land. While the prevailing continental aerosol types other than smoke increase precipitation, the marine aerosols first enhance and then inhibit precipitation with the increase of AOD. Moreover, our analysis indicates that the above‐mentioned enhancement and inhibition effects on precipitation are mainly caused by aerosols themselves, rather than by the covariation of meteorological factors. These observed relationships between different aerosol types and precipitation frequency and amount provide valuable constraints on the model forecasting of precipitation.