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A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data.

There is still a significant gap between our understanding of neural circuits and the behaviours they compute—i.e. the computations performed by these neural networks (Carandini 2012 Nat. Neurosci. 15 , 507–509. ( doi:10.1038/nn.3043 )). Cellular decision-making processes, learning, behaviour and memory formation—all that have been only associated with animals with neural systems—have also been observed in many unicellular aneural organisms, namely Physarum , Paramecium and Stentor (Tang & Marshall2018 Curr. Biol. 28 , R1180–R1184. ( doi:10.1016/j.cub.2018.09.015 )). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeseli (Jennings 1902 Am. J. Physiol. Legacy Content 8 , 23–60. ( doi:10.1152/ajplegacy.1902.8.1.23 )) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidance reactions. Although we were able to observe all responses described in the literature and in a previous study (Dexter et al . 2019), they do not conform to any particular learning model. We then investigated whether models inferred from machine learning approaches, including decision tree, random forest and feed-forward artificial neural networks could infer and predict the behaviour of S. roeseli . Our results showed that an artificial neural network with multiple ‘computational’ neurons is inefficient at modelling the single-celled ciliate's avoidance reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

A fundamental step of tumour single cell mRNA analysis is separating cancer and non-cancer cells. We show that the common approach to separation, using shifts in average expression, can lead to erroneous biological conclusions. By contrast, allelic imbalances representing copy number changes directly detect the cancer genotype and accurately separate cancer from non-cancer cells. Our findings provide a definitive approach to identifying cancer cells from single cell mRNA sequencing data.

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