Table 1 Challenges associated with implementation of the proposed integrative approach for precision medicine
From: An integrative approach for building personalized gene regulatory networks for precision medicine
Challenge | Solution | References | |
|---|---|---|---|
Technical challenges | Implementation of directionality and causality | eQTL, context-dependent eQTL and co-expression QTL information Time-series data and pseudotime combined with RNA velocity Experimental validation using CRISPR perturbations coupled to scRNA-seq read-out (for example, CRISP-seq, CROP-seq, and PERTURB-seq) | |
Dropouts | Gene expression and cross-omics imputation | ||
Amplification bias | Unique molecular identifiers (UMIs) | [66] | |
Combining single-cell data with a bulk reference network | Anchor points Computational methods need to be developed | [120] | |
Practical challenges | Time and cost involved in collecting scRNA-seq data | Droplet-based approaches in combination with approaches that enable super-loading and pooling of samples (for example, cell hashing or demuxlet) Split-pool barcoding approaches (for example, SPLiT-seq and combinatorial indexing) Large throughput sequencers that enable reduction in sequencing cost | |
Large-scale availability of datasets with both genotype and scRNA-seq data | Collaborative efforts (for example, single-cell eQTLGen consortium and Human Cell Atlas) | ||
Cost involved in genotyping each individual | Genotype arrays in combination with imputation-based approaches enable mapping of clinically relevant genetic variants with high coverage for less than €100 per individual | ||
Public perception, health regulations | General Data Protection Regulation implemented in the EU in 2018 Genetic counselors to help with interpreting genetic results | [113] |