Author: Lucia Grenga; Fabrice Gallais; Olivier Pible; Jean-Charles Gaillard; Duarte Gouveia; Hélène Batina; Niza Bazaline; Sylvie Ruat; Karen Culotta; Guylaine Miotello; Stéphanie Debroas; Marie-Anne Roncato; Gérard Steinmetz; Charlotte Foissard; Anne Desplan; Béatrice Alpha-Bazin; Christine Almunia; Fabienne Gas; Laurent Bellanger; Jean Armengaud
Title: Shotgun proteomics of SARS-CoV-2 infected cells and its application to the optimisation of whole viral particle antigen production for vaccines Document date: 2020_4_17
ID: kd02dehk_19
Snippet: Principal component analysis was done as previously described [18] . Co-expression cluster analysis was obtained using the Bioconductor R package coseq v1.5.2 [19] . The protein abundance matrix was used as an input in coseqR. Log CLR-transformation was applied to the matrix to normalize the abundance of proteins and the K-means algorithm was chosen to detect the co-expressed clusters across the different time points. The K-mean algorithm was rep.....
Document: Principal component analysis was done as previously described [18] . Co-expression cluster analysis was obtained using the Bioconductor R package coseq v1.5.2 [19] . The protein abundance matrix was used as an input in coseqR. Log CLR-transformation was applied to the matrix to normalize the abundance of proteins and the K-means algorithm was chosen to detect the co-expressed clusters across the different time points. The K-mean algorithm was repeated 20 times in order to determine the optimal number of clusters. The resulting number of clusters in each run was recorded, and the most parsimonious cluster partition was selected using the slope heuristics approach. Both the PCA and the coseq analysis were performed after removal of proteins with spectral counts lower than three author/funder. All rights reserved. No reuse allowed without permission.
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