Dataset Information

SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.

ABSTRACT: Human pluripotent stem cells have ushered in an exciting new era for disease modeling, drug discovery, and cell therapy development. Continued progress toward realizing the potential of human pluripotent stem cells will be facilitated by robust data sets and complementary resources that are easily accessed and interrogated by the stem cell community. In this context, we present SISTEMA, a quality-controlled curated gene expression database, built on a valuable catalog of human pluripotent stem cell lines, and their derivatives for which transcriptomic analyses have been generated using a single experimental pipeline. SISTEMA functions as a one-step resource that will assist the stem cell community to easily evaluate the expression level for genes of interest, while comparing them across different hPSC lines, cell types, pathological conditions, or after pharmacological treatments.

SUBMITTER: Jarrige M

PROVIDER: S-EPMC8271161 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.

Jarrige Margot M Polvèche Hélène H Carteron Alexandre A Janczarski Stéphane S Peschanski Marc M Auboeuf Didier D Martinat Cécile C

iScience 20210624 7

Human pluripotent stem cells have ushered in an exciting new era for disease modeling, drug discovery, and cell therapy development. Continued progress toward realizing the potential of human pluripotent stem cells will be facilitated by robust data sets and complementary resources that are easily accessed and interrogated by the stem cell community. In this context, we present SISTEMA, a quality-controlled curated gene expression database, built on a valuable catalog of human pluripotent stem c ...[more]

PMID: 34278269

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Project description:BackgroundMulti-dimensional scaling (MDS) is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data.ResultsWe developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS) is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data.ConclusionOur new method reduces the computational complexity from O(N3) to O(N) when the dimension of the feature space is far less than the number of genes N, and it successfully reconstructs the low dimensional representation as does the classical MDS. Its performance depends on the grouping method and the minimal number of the intersection points between groups. Feasible methods for grouping methods are suggested; each group must contain both neighboring and far apart data points. Our method can represent high dimensional large data set in a low dimensional space not only efficiently but also effectively.

Dataset Information

SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.

Publications

SISTEMA: A large and standardized collection of transcriptome data sets for human pluripotent stem cell research.

Similar Datasets

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