This HTML report contains the exploratory data analysis plots, (e.g. density plots)
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| omics data type : | PTX |
| data description : | Proteomics data of CHO cells |
| data collection date : | February 2024 |
| meta condition : | Phase |
| meta batch : | Reactor, |
| limma design : | NA |
| analyst name : | Thomas Rauter |
| contact info : | thomas.rauter@plus.ac.at |
| project name : | DGTX |
| method description : | NA |
| results summary : | NA |
| conclusions : | NA |
| data with annotation : | |
| meta : |
Density plots resemble smooth, continuous hills or curves that illustrate where the data is concentrated over a continuous range of values. By showing how frequently values occur within the data range, density plots show patterns such as peaks, valleys, and the overall spread of the data. The height of the curve at any given point indicates the density of the data points in that area, with higher curves representing more data points.
Violin Box plots combine boxplots and density plots to show the distribution of values. They provide a summary of the data's range, central tendency, and distribution shape. Use violin plots to understand the full distribution and compare between groups.
If you are unsure which dimensionality reduction plot to consult, choose PCA.
PCA plots visualize the major trends and patterns in high-dimensional data by reducing it to a few principal components. Points close to each other are similar, they are correlated and clustered. Use PCA plots to identify clustering and variance explained by the principal components. In the plot below, the level of transparency is based on the timepoint. Earlier timepoints have a higher transparency, later timepoints have a lower transparency.
MDS plots display similarities or dissimilarities between samples in a reduced dimension space. Points close to each other are more similar. Use MDS plots to visualize the distance or similarity between samples. In the plot below, the level of transparency is based on the timepoint. Earlier timepoints have a higher transparency, later timepoints have a lower transparency.
Correlation Heatmaps illustrate the pairwise correlation between all samples. Colors represent the strength of correlation, with a color gradient indicating positive or negative correlations. Use this plot to identify highly correlated samples or groups.
In Mean Time Correlation, the correlation of each feature with time is calculated and the values shown in a histogram. Positive correlation means the values of the feature increase with time, negative means they decrease.
Normalized lag-1 difference is the absolute difference in values between timepoints t and t-1 for a given feature, divided by the mean of all values of that feature. For each feature, the mean of all lag-1 differences is calculated and displayed in a histogram. A small mean lag-1 difference compared to the size of the values indicates that the temporal pattern remains relatively flat. For example, a mean normalized lag-1 difference of 0.2 means on average there was a 20% change between the timepoints.
First lag autocorrelation measures the degree to which a time series is correlated with its immediate past values. A high autocorrelation coefficient (p1) near 1 indicates a strong positive relationship: when the current value increases (or decreases), the previous value tends to do the same. Conversely, a coefficient near -1 indicates a strong negative relationship: when the current value increases (or decreases), the previous value tends to decrease (or increase). A coefficient close to 0 suggests little to no relationship between consecutive values. Understanding first lag autocorrelation helps in identifying if there's a consistent pattern or trend in how the time series behaves over time.
Coefficient of Variation (CV) plots depict the variability relative to the mean of a dataset. A higher CV indicates greater relative variability, while a lower CV suggests more consistency around the mean. These plots help assess the dispersion of data points and are useful in comparing the spread of different datasets or monitoring changes in variability over time.
