Fold change level of molecules involved in antigen processing and presentation molecules in cluster 1 (A) and cluster 4 (B). Fold change level of molecules involved in leukocyte recruitment in cluster 1 (C) and cluster 4 (D). These results show how cluster 1 genes for the first 2 steps of the cancer-immunity cycle are up-regulated while those of cluster 4 are mostly downregulated. These pathways and others for other clusters and steps of the cancer-immunity pathway are in S5 File. The color scale ranges from the downregulated expression in green (-1 fold), to the non-differential expression in grey, to the up-regulated expression in red (+1 fold).</p
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
<p>Cluster 1 for highly down-regulated genes throughout the treatment (A) included the highest numbe...
<p>A: the effect estimates of Model 3 were subjected to a hierarchical cluster analysis. Genes are d...
<p>Colored keys represent the fold changes (log<sub>2</sub> transformed counts) of gene expression b...
<p>(A) All the data were median centred and clustered using a hierarchical clustering. A cluster ima...
<p>A) Heat map indicates result of un-supervised hierarchical clustering for 743 gene promoters with...
<p>Co-expressed gene cluster time trends of baseline log<sub>2</sub> fold change in LCPM of DE genes...
<p>MetaCore Pathway analysis was conducted on the differential gene expression of Cluster-1 at 1 wee...
Relates to Fig. 4. Genes most consistently associated with COO-unclassified DLBCL are related to a ...
<p>The heat map shows a two-color representation of the regulatory relationship between modulators (...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Using the Impact Analysis method in iPathway (Advaita Corporation), we conducted unbiased pathway an...
Unsupervised clustering analysis combining gene expression levels of CK20, CD44, E-CAD and Survivin ...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
<p>Cluster 1 for highly down-regulated genes throughout the treatment (A) included the highest numbe...
<p>A: the effect estimates of Model 3 were subjected to a hierarchical cluster analysis. Genes are d...
<p>Colored keys represent the fold changes (log<sub>2</sub> transformed counts) of gene expression b...
<p>(A) All the data were median centred and clustered using a hierarchical clustering. A cluster ima...
<p>A) Heat map indicates result of un-supervised hierarchical clustering for 743 gene promoters with...
<p>Co-expressed gene cluster time trends of baseline log<sub>2</sub> fold change in LCPM of DE genes...
<p>MetaCore Pathway analysis was conducted on the differential gene expression of Cluster-1 at 1 wee...
Relates to Fig. 4. Genes most consistently associated with COO-unclassified DLBCL are related to a ...
<p>The heat map shows a two-color representation of the regulatory relationship between modulators (...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Using the Impact Analysis method in iPathway (Advaita Corporation), we conducted unbiased pathway an...
Unsupervised clustering analysis combining gene expression levels of CK20, CD44, E-CAD and Survivin ...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...
Identifying the hallmarks of cancer is essential for cancer research, and the genes involved in canc...