Table 2 Different methods were used in the hackathons and are also available as reproducible vignettes

Pre-processing

Normalization & data transformation

Data distribution checks (Fig. 2: 1, Fig. 2: 4)

High Variable Genes selection (Fig. 2: 5)

Variance Stabilization Normalization [16] (Fig. 2: 8)

Arcsinh transformation (Fig. 2: 9).

Inverse transformation (Fig. 2: 9)

Selection of patients (Fig. 2: 9)

Summaries of DNA measurements (input data provided in hackathon)

Managing differences in scale

Data integration

LIGER [17] (Fig. 2: 2) (sc)

ComBat (Fig. 2: 4) (bulk)

Projection methods MFA, sGCCA [18] (Fig. 2: 4^a) (bulk)

UMAP/tSNE (Fig. 2: 2) (sc)

Multiblock PCA [19]

Weighting matrices based on their similarities: STATIS, MFA (Fig. 2: 8) (bulk^a)

Scale MIBI-TOF to the range of CyTOF values (Fig. 2: 9)

LIGER [17] (Fig. 2: 13) (sc)

Projection method sGCCA [18] (Fig. 2: 11) (bulk)

Multi Omics Supervised Integrative Clustering with weights (Fig. 2: 14) (bulk)

Overlap

Cell overlap

(features not matching)

Dimension reduction and projection methods:

LIGER [17] (Fig. 2: 13) (sc)

sGCCA [18] (Fig. 2: 11) (bulk)

Partial feature overlap

(cells not matching)

Imputation:

Direct inversion with latent variables

Optimal transport to predict protein expression (Fig. 2: 10)

K-nearest neighbor averaging (Fig. 2: 9)

No imputation:

Biological Network Interaction^a

Partial cell overlap

(features not matching)

Multiblock PCA [19] (Fig. 2: 8^a)

No cell overlap

(complete feature overlap)

Transfer cell type label with Random Forest (Fig. 2: 7)

LIGER [17] (Fig. 2: 13)

No cell overlap

(partial feature overlap)

Topic modeling to predict cell spatial co-location or spatial expression (Fig. 2: 9, partial feature overlap)

No overlap

RLQ^a [20]

Generic approaches

Classification & feature selection

Backward selection with SVM (Fig. 2: 1)

Self-training ENet (Fig. 2: 4)

Balanced error rate (Fig. 2: 1) Fig. 2: 4)

Recursive Feature Elimination (Fig. 2: 5)

(all bulk)

Multi Omics Supervised Integrative Clustering (Fig. 2: 14) (bulk)

Lasso penalization in regression-type models (bulk)

Cell type prediction

Projection with LIGER [17] (Fig. 2: 2)

SVM (Fig. 2: 1, Fig. 2: 5)

ssEnet (Fig. 2: 4)

(all bulk)

Spatial analysis

Hidden Markov random field

Voronoi tesselation (Fig. 2: 1) (bulk)

Spatial autocorrelation with Moran’s Index (Fig. 2: 7, Fig. 2: 10)

Selection of spatial discriminative features:

Moran’s Index, NN correlation, Cell type, interaction composition, L function (Fig. 2: 10)

(all bulk)

Inclusion of additional information

Survival prediction: Cox regression based on spatial features (Fig. 2: 10)

Include annotated hypersensitive sites index to anchor new/unseen data from DNase-seq, (sc)ATAC-seq, scNMT-seq, for de novo peak calling (bulk^a)