Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data

Abstract

In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other. It has been shown to be useful in the analysis of high-dimensional genomic data, when two sets of assays are available on the same set of samples. In this paper, we propose two extensions to the sparse CCA methodology. (1) Sparse CCA is an unsupervised method; that is, it does not make use of outcome measurements that may be available for each observation (e.g., survival time or cancer subtype). We propose an extension to sparse CCA, which we call sparse supervised CCA, which results in the identification of linear combinations of the two sets of variables that are correlated with each other and associated with the outcome. (2) It is becoming increasingly common for researchers to collect data on more than two assays on the same set of samples; for instance, SNP, gene expression, and DNA copy number measurements may all be available. We develop sparse multiple CCA in order to extend the sparse CCA methodology to the case of more than two data sets. We demonstrate these new methods on simulated data and on a recently published and publicly available diffuse large B-cell lymphoma data set.

Multi-omics integration—a comparison of unsupervised clustering methodologies

“…This result is in line with the paper by Witten and Tibshirani [27], where a fused lasso penalty has been used to reduce samples noise before applying MCCA. Although not all the performances benefited from feature selection, the classification accuracy lost by noise addition has been generally recovered by applying this preprocessing step.…”

Multi-omics integration—a comparison of unsupervised clustering methodologies

“…This result is in line with the paper by Witten and Tibshirani [27], where a fused lasso penalty has been used to reduce samples noise before applying MCCA. Although not all the performances benefited from feature selection, the classification accuracy lost by noise addition has been generally recovered by applying this preprocessing step.…”

Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response

“…For example, the day 0 lymphocytes (LYM0) and granulocytes (GRA0) counts were strongly anti-correlated (R = −0.98). As per the literature [38], the expected negative correlation of the female gender (Gender_F) and red blood cell count (RBC0) (R = −0.63) was identified. TCR-derived parameters were also assessed.…”

Multi-view learning to unravel the different levels underlying hepatitis B vaccine response

“…For example, the lymphocytes (LYM0) and granulocytes (GRA0) counts are strongly anti-correlated (R = -0.98). As per the literature [38], the expected negative correlation of female gender (Gender_F) and red blood cell count (RBC0) (R=-0.63) was identified. TCR-derived parameters were also assessed.…”

Multi-omics integration—a comparison of unsupervised clustering methodologies

“…This result is in line with the paper by Witten and Tibshirani [27], where a fused lasso penalty has been used to reduce samples noise before applying MCCA. Although not all the performances benefited from feature selection, the classification accuracy lost by noise addition has been generally recovered by applying this preprocessing step.…”

Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response

“…For example, the day 0 lymphocytes (LYM0) and granulocytes (GRA0) counts were strongly anti-correlated (R = −0.98). As per the literature [38], the expected negative correlation of the female gender (Gender_F) and red blood cell count (RBC0) (R = −0.63) was identified. TCR-derived parameters were also assessed.…”

Multi-view learning to unravel the different levels underlying hepatitis B vaccine response

“…For example, the lymphocytes (LYM0) and granulocytes (GRA0) counts are strongly anti-correlated (R = -0.98). As per the literature [38], the expected negative correlation of female gender (Gender_F) and red blood cell count (RBC0) (R=-0.63) was identified. TCR-derived parameters were also assessed.…”

Multi-omics integration—a comparison of unsupervised clustering methodologies

“…This result is in line with the paper by Witten and Tibshirani [27], where a fused lasso penalty has been used to reduce samples noise before applying MCCA. Although not all the performances benefited from feature selection, the classification accuracy lost by noise addition has been generally recovered by applying this preprocessing step.…”

Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response

“…For example, the day 0 lymphocytes (LYM0) and granulocytes (GRA0) counts were strongly anti-correlated (R = −0.98). As per the literature [38], the expected negative correlation of the female gender (Gender_F) and red blood cell count (RBC0) (R = −0.63) was identified. TCR-derived parameters were also assessed.…”

Multi-view learning to unravel the different levels underlying hepatitis B vaccine response

“…For example, the lymphocytes (LYM0) and granulocytes (GRA0) counts are strongly anti-correlated (R = -0.98). As per the literature [38], the expected negative correlation of female gender (Gender_F) and red blood cell count (RBC0) (R=-0.63) was identified. TCR-derived parameters were also assessed.…”