Fig. 2.

AneuFinder – automated copy number analysis of single-cell sequencing data. a Samples are homogenised, single-cell sorted and sequenced. b Aligned sequencing reads are counted in non-overlapping bins of variable size based on mappability. c A Hidden Markov Model with multiple hidden states is applied to the binned read counts in order to predict copy number state of every single bin. Emission distributions are modelled as negative binomial distributions (NB (r,p,x)). d The model parameters are estimated using the Baum Welch algorithm and every binned read count is assigned to the copy number state that maximises the posterior probability. e Quality of each single-cell library is assessed based on the following measures: spikiness, loglikelihood of the model determined by the Baum-Welch algorithm, number of separate copy number segments and Bhattacharyya distance. Libraries are clustered based on these measures: the highest scoring cluster is selected for further analysis. f The extent of aneuploidy is measured as the divergence of a given chromosome from the normal euploid state. At the cell population level, heterogeneity is measured as the number of cells with a distinct copy number profile within the population. g Example of a genome-wide copy number profile of a population of T-ALL cells. Each row represents a single cell with chromosomes plotted as columns. Copy number states are depicted in different colours. Cells are clustered based on the similarity of their copy number profile