Skip to main content

Table 4 Approaches for data integration, highlighting their promises and challenges

From: Eleven grand challenges in single-cell data science

 IntegrationExample MT combinationExample AMsPromisesChallenges
1SNonescDNA-seqClustering/unsupervisedDiscover new subclones, cell types, or cell statesTechnical noise ↓; data sparsity ↓
+SWithin 1 MT, within 1 exp, across >1 smpsscRNA-seqDifferential analyses, time series, spatial samplingIdentify effects across sample groups, time, and spaceBatch effects ↓; validate cell type assignments ↓
+X+SWithin 1 MT, across >1 exp, across >1 smpsmerFISHMap cells to stable reference (cell atlas)Accelerate analyses, increase sample size, generalize observationsStandards across experimental centers
+M1CAcross >1 MTs, within 1 exp, within 1 cellscM&T-seq (scRNA-seq + methylome)MOFA, DIABLO, MINTHolistic view of cell state; quantify dependency of MTsScaling cell throughput; MT combinations limited; dependency of MTs ↓
+M+CAcross >1 MTs, within 1 exp, across >1 cells, within 1 cell popscDNA-seq + scRNA-seqCardelino, Clonealign, MATCHERUse existing datasets (faster than +M1C); flexible experimental designValidate cell/data matching; test assumptions for integrating data
+allAcross >1 MTs, across >1 exps, across >1 smps, within cellsHypothetical (any combination)Hypothetical (map cells to multi-omic HCA, single-cell TCGA)Holistic view of biological systemsAll from approaches +X+S, +M1C, and +M+C
  1. The labeling corresponds to Fig. 6. For each approach, one (combination of) measurement type(s) that is available is given, but more exist and several are discussed in the text. As example analysis methods, actual tool names are given where few tools exist to date; otherwise, broader categories or imaginable methodologies are described
  2. Abbreviations: “↓” same challenge also applies to all approaches below, AM analysis method, exp(s) experiment(s), HCA human cell atlas, MT measurement type, smps samples, TCGA The Cancer Genome Atlas