Due to the assumptions made by Pearson correlation about linear relationships between continuous features, it was important to compare it to a correlation method that can find non-linear, monotonic relationships, for which the Spearman method was a logical choice. To test these correlation methods, ...
The unique high expression genes of each dataset were selected by looking at a 16× difference in expression and clustering analysis was performed on these selected lists using R’s Pearson correlation. Statistical analysis Unless otherwise indicated, two-tailed Student’s t test or one-way ...
46. By combining the heatmap and the clustering methods, the samples within the data set can be compared, based on their similarity and difference in their corresponding gene expression profiles45,46.
Fuzzy clustering of the expression data along seed development series. The six clusters showing the expression patterns during Arabidopsis seed development. The gene expression values were standardized to have a mean value of zero and a standard deviation of one for each gene profile. The transformed...
Expression pattern and correlation analysis of PtR2R3-MYBs and SGs in different tissues and varieties. a Heatmap showing hierarchical clustering analysis of PtR2R3-MYB genes and SGs across different tissues including leaves, stems and roots in ZG11, ZG39 and ZG19. The color scale is shown on...
Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarc
newly diagnosed with B-ALL. Data of the 136 differentially expressed genes (DEG): 62 upregulated (red) and 74 downregulated genes (blue) were used to create a heatmap with the HeatMapper tool using the clustering method centroid linkage and distance measurement with Pearson’s correlation ...
Again, traditional clustering algorithms are used, but the input to the algorithms – an n× n similarity matrix for n gene expression profiles – is calculated using an error-weighted similarity measure as opposed to traditional Euclidean distances or correlation coefficients. The error-weighted ...
Clustering by ncRNA expression using t-distributed stochastic neighbor embedding (t-SNE), samples split into tissue-specific groups (Fig.1b). One cluster contained skin, GAT and SCAT samples, which likely can be explained by their biological and functional close relationship of containing similar ce...
Cell clustering was performed using the Seurat function FindNeighbors and FindClusters and cell type annotation was manually performed on each object using known cell-type specific markers (Supplementary Fig. 132). For each cell population, cell type annotation was performed at four levels, ranging ...