Fig. 3: Loss of heterozygosity analysis workflow.

a In our LOH analysis, the allele fraction of known heterozygous SNPs is used to infer underlying copy number changes at the DNA level. We compare germline and tumor scenarios, focusing on 5 SNPs to illustrate key principles. The germline example displays a copy number of 2 and B allele frequencies at 0.5 across all SNPs. In contrast, the tumor displays partial p-arm deletion (white) and chromosome duplication (blue). The copy number state varies from 1 in the deleted p-arm region to 3 in the duplication region. B-allele frequencies at each SNP vary from 0, 0.3, and 0.6. b Integrative Genomics Viewer example of aligned spatial transcriptomic sequencing reads. The top coverage track represents the total reads aligned, with an indication of a SNP highlighted in red. The reference transcript at the bottom is in the 5’ to 3’ orientation, as indicated by arrows. c The SNP density plot shows the location and relative quantity of unique SNPs across the glioma dataset. The chromosomes are listed on the y-axis, with an ideogram below density measurements. d Analysis of i length of defined segments, ii number of unique SNPs per segment, iii segment mode peak values, and iv segment sequential sum of log10(K) values. e The analysis workflow of LOH identification begins with a spatial sample analyzed with the 10X Genomics spaceranger software. A sample BAM is split into cluster-specific BAMs. Read coverage is calculated at predetermined heterozygous SNP positions, filtered by strict criteria. Bayes factor K values are calculated at each SNP. A hidden Markov model independently evaluates each chromosome for each cluster and assigns regions with state determinations. Metrics across segments are evaluated, and a final assignment of heterozygous, LOH, or undefined is determined. SNP allele fractions are plotted in different panels for each cluster, and points are color-coded by state.