Single-cell ChIP-seq analysis is challenging due to data sparsity. We present SIMPA (https://github.com/salbrec/SIMPA), a single-cell ChIP-seq data imputation method leveraging predictive information within bulk ENCODE data to impute missing protein- DNA interacting regions of target histone marks or transcription factors. Machine learning models trained for each single cell, each target, and each genomic region enable drastic improvement in cell types clustering and genes identification.The discovery of protein-DNA interactions of histone marks and transcription factors is of high importance in biomedical studies because of their impact on the regulation of core cellular processes such as chromatin structure organization and gene expression. These interactions are measured by chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq). Public data from the ENCODE portal, which provides a large collection of experimental bulk ChIP-seq data, has been used for comprehensive investigations revealing insights into epigenomic processes impacting chromatin 3Dstructure, open chromatin state, and gene expression to name just a few (ENCODE project consortium, 2012). Recently developed protocols for single-cell ChIP-seq (scChIP-seq) are

The living cell operates thanks to an intricate network of protein interactions. Proteins activate, transport, degrade, stabilise and participate in the production of other proteins. As a result, a reliable and systematically generated protein wiring diagram is crucial for a deeper understanding of cellular functions. Unfortunately, current human protein networks are noisy and incomplete. Also, they suffer from both study and technical biases: heavily studied proteins (e.g. those of pharmaceutical interest) are known to be involved in more interactions than proteins described in only a few publications. Here, we use the experimental evidence supporting the interaction between proteins, in conjunction with the so-called disparity filter, to construct a reliable and unbiased proteome-scale human interactome. The application of a global filter, i.e. only considering interactions with multiple pieces of evidence, would result in an excessively pruned network. In contrast, the disparity filter preserves interactions supported by a statistically significant number of studies and does not overlook small-scale protein associations. The resulting disparity-filtered protein network covers 67% of the human proteome and retains most of the network's weight and connectivity properties.