Principal component analysis (PCA) and GIS were used to distill the relationships across all variables, in order to obtain a vulnerability index. Aggregates of variables were also investigated to map the spatial
we sought to identify technical factors which may confound our morphological phenotypes and remove these sources of variance from our downstream association tests. We performed a variance component analysis using well-
Principal component analysis (PCA) of the isotopic data measured on the Late Neolithic and Eneolithic copper artefacts, both axes and other objects. Each object is marked with the sample label reported in the Supplementary Table 1. Full size image Figure 3 2D projections of the 3D isotopic spac...
Principal component analysis (PCA) using DEGs sepa- rated the samples into febrile and non-febrile groups on PC1, which was associated with 21.64% of the variation, while PC2 (7.52%) partially separated U from A and the febrile infections into those who were initially asympto- matic (FA) ...
41 The PDUI or PSI values of the selected AS or APA events were curated to perform principal-component analysis (PCA). Principal components (PCs) 1 and 2 were extracted and used as the signature score. As previously reported, the formula was employed to define the AS or APA score: AS ...
(A) Loading of principal component analysis (PCA) of the CIBERSORT-derived immune cell infiltrate composition across 10,469 tumors stratified as antitumor (E; effector) or pro-tumor (S; suppressor). Immune type nomenclature is listed in Supplementary Table 2. (B) The cellular composition (...
Principal component analysis (PCA) using DEGs sepa- rated the samples into febrile and non-febrile groups on PC1, which was associated with 21.64% of the variation, while PC2 (7.52%) partially separated U from A and the febrile infections into those who were initially asympto- matic (FA) ...
Additionally, principal component analysis (PCA) was performed using the same functional annotations as used in the hierarchical clustering, with the exception that for CAZymes and proteases the number of genes associated with each fam- ily (e.g AA1, A01) was used instead of those of each ...
PCA was performed using the 3000 most highly variable genes. The first 50 principal components (PCs) were used to perform dimensionality reduction by UMAP [30, 31] and to construct a shared nearest neighbor graph (SNN; FindNeighbors()), which was used to cluster the dataset (FindClusters())...
AUMAP plot showing clustering of 2349 T cells by SCASL based on AS profiles. The principal component analysis (PCA) is performed with a dimensionality reduction number of 30. Source data are provided as a Source Data file. Source data are provided as a Source Data file.BThe heatmap on...