Two-stage linked component analysis for joint decomposition of multiple biologically related data sets

CellCover Captures Neural Stem Cell Progression in Mammalian Neocortical Development

Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. We aim to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. We show that this new method, CellCover, captures cell-class-specific signals in the developing mouse neocortex that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as temporal progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex appeared in gliogenic precursors of the rodent before the full program emerged in the primate lineage. We have assembled the public datasets we use in this report at NeMO analytics where the expression of individual genes {NeMO Individual Genes} and marker gene panels can be freely explored {NeMO: Telley 3 Sets Covering Panels}, {NeMO: Telley 12 Sets Covering Panels}, and {NeMO: Sorted Brain Cell Covering Panels}. CellCover is available in {CellCover R} and {CellCover Python}.

The use of prognostic models in allogeneic transplants: a perspective guide for clinicians and investigators

Allogeneic hematopoietic cell transplant (HCT) can cure many hematologic diseases, but it carries the potential risk of increased morbidity and mortality rates. Prognostic evaluation is a scientific entity at the core of care for potential recipients of HCT. It can improve the decision-making process of transplant vs no transplant, help choose the best transplant strategy and allows for future trials targeting patients' intolerances to transplant; hence, it ultimately improves transplant outcomes. Prognostic models are key for appropriate actuarial outcome estimates, which have frequently been shown to be better than physicians' subjective estimates. To make the most accurate prognostic evaluation for HCT, one should rely on >1 prognostic model. For relapse and relapse-related mortality risks, the refined disease risk index is currently the most informative model. It can be supplemented with disease-specific models that consider genetic mutations as predictors in addition to information on measurable residual disease. For nonrelapse mortality and HCT-related morbidity risks, the HCT-comorbidity index and Karnofsky performance status have proven to be the most reliable and most accepted by physicians. These can be supplemented with gait speed as a measure of frailty. Some other global prognostic models might add additional prognostic information. Physicians' educated perceptions can then put this information into context, taking into consideration conditioning regimen and donor choices. The future of transplant mandates (1) clinical investigators specifically trained in prognostication, (2) increased reliance on geriatric assessment, (3) the use of novel biomarkers such as genetic variants, and (4) the successful application of novel statistical methods such as machine learning.

Interpretive JIVE: Connections with CCA and an application to brain connectivity

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure.