Please go through the below explanation for details on table and plot interpretations

Table column interpretation:

Within Gene Expression input box, we have the lab names from where gene expression data have been collected for different tissues. Therefore to reduce complexity and time, both for the table and for the choose analysis options: Histogram, Count and distribution plot, Pair plots, Box plots, Violin plots, Joint plots, Scatter plots, Correlations plots and Downsampled Hirarchial Scatter plot only the first five tissues from the selected lab is displayed wheareas for all other plots (PCA [2D,3D] , Heatmap and Downsampled Hieararhial Heatmap) all tissues from the selected lab were considered.

The following papers [by lab] were the sources of the RNA-seq datasets included in this interface:

8. Average gene abundance

This is average gene expression level of each gene across all tissues collected from different labs mentioned above.

9. Gene breadth

It is the number of tissues in which a gene is expressed.

10. Max FPKM

This is maximum gene expression level of each gene across all tissues collected from different labs mentioned above.

10. Labels

The labels are the classes or the groups the genes are mapped into.The labels can act as both target variable or feature as per the need of the user for solving their specific problem

10.1 No Label

This selection is provided to enable users to view the properties of all genes without labeling them into different gene categories or annotations. This is to let users examine the features of multiple genes and identify common patterns among them. As it involves the inspection of all the genes therefore they work only for "Submit for analysis" button .

10.2 Classical Genes

Classical genes can be defined as the most well-studied genes mainly for their visible mutant phenotype (for example: liguleless3).

10.3 Pan-genome Genes

A gene in a given taxonomic group is either present in every individual (core), or absent in at least a single individual (dispensable).

10.4 Origin Genes

Gene duplication is an important evolutionary mechanism allowing new genetic material and thus opportunities to acquire new gene functions for an organism. There are different origins of duplications such as whole-genome duplications, tandems, etc.

Graph interpretations:

To the top right corner of the plots/graphs, there are options to download plot, zoom-out/zoom in, reset axes, autoscale, toggle spike lines, show closest data on hover, compare data on hover, box select, pan and lasso. Users can also select specific legends to view data only for the selected legends. Details on the interactive plot options are available here:
Interactive graph features

1. Histogram

The Histogram shows the frequency distribution of the selected lab tissue such as in Varotto lab tissues: rmr6_leaf_control_T0, rmr6_leaf_control_T7, rmr6_leaf_drought_salt_T0 etc. The X-axis in the histogram represents the range of values present in the selected lab tissue. The Y-axis represents the frequency of values. In addition to the graph,to increase the interpretability of the data, We have also included P-values,mean and standard deviations of the selected datasets.

2. Count and distribution

The Count and distribution plot is a smoothed, continuous version of a histogram estimated from the data. The most common form of estimation is known as kernel density estimation.The x-axis is the value of the selected lab tissue just like in a histogram and the y-axis in a distribution plot is the probability density function and not a probability. The difference is the probability density is the probability per unit on the x-axis. In general the y-axis on the distribution plot is a value only for relative comparisons between different categories like classical and other genes.

3. Pair plots

The pairs plot builds on two basic figures, the distributions and the scatter plot. The distributions on the diagonal allows us to see the distribution of a single selected lab tissue while the scatter plots on the upper and lower triangles show the relationship (or lack thereof) between two variables such as rmr6_leaf_control_T0 and rmr6_leaf_control_T7.

3. Box plots

Boxplots are a measure of how well distributed the data in the selected lab tissue's are. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the selected lab tissue. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them such as the core, non-core, dispensable and private genes. Boxplots can be used to:

Identify outliers or anomalous data points
To determine if our data is skewed
To understand the spread/range of the data

4. Violin plots

Violin plots are similar to box plots, except that they also show the probability density of the selected lab tissue at different values. These plots include a marker for the median of the data and a box indicating the interquartile range, as in the standard box plots. Overlaid on this box plot is a kernel density estimation. Like box plots, violin plots are used to represent comparison of a variable distribution such as rmr6_leaf_control_T0, rmr6_leaf_control_T7 (or sample distribution) across different "categories" (Classical/Other or Core/Non-core) .
A violin plot is more informative than a plain box plot. In fact while a box plot only shows summary statistics such as mean/median and interquartile ranges, the violin plot shows the full distribution of the selected lab tissue.

5. Joint plots

A Jointplot comprises three plots. Out of the three, one plot displays a bivariate graph(scatter plot) which shows how the one variable( such as rmr6_leaf_control_T0) varies with the another variable(such as Exon number). Another plot is placed horizontally at the top of the bivariate graph and it shows the distribution in the form of marks along an axis for each one of the selected lab tissue's ( for example rug plot of rmr6_leaf_control_T0 and rmr6_leaf_control_T7). The third plot is placed on the right margin of the bivariate graph with the orientation set to vertical and it shows the distribution of again the two selected lab tissue's .
It is very helpful to have univariate and bivariate plots together in one figure. This is because the univariate analysis focuses on one variable, it describes, summarizes and shows any patterns in your data and the bivariate analysis explores the relationship between two variables and also describes the strength of their relationship.

6. Scatter plots

A Scatter plot is a great way of exploring relationships or patterns in data. But adding a regression line can make those patterns stand out . Therefore Scatter plot with simple linear regression for the selected lab tissue's, explains the strength of the relationship between the two variables such as rmr6_leaf_control_T0 or rmr6_leaf_control_T7 in your scatter-plot using R2, the squared correlation coefficient.It is always between 0 and 1. Higher R2 indicates stronger relationship between the selected lab tissue's .

7. Correlation plots

Correlation heatmap is graphical representation of correlation matrix representing correlation between different selected lab tissue's. Correlation ranges from -1 to +1. Values closer to zero means there is no linear trend between the two variables. The closer to 1 the correlation is the more positively correlated they are; that is as one increases so does the other and the closer to 1 the stronger this relationship is. Correlation plots also alerts us to potential multicollinearity problems.

8. Downsampled Dendrogram plots

A dendrogram is a type of tree diagram showing hierarchical clustering — relationships between similar sets of genes based on the selected lab tissue's. They are frequently used in biology to show clustering between genes or samples, but they can represent any type of grouped data.The dendrogram is built on downsampled data to save time and complexity.
The branches in the dendrogram are arranged according to how similar (or dissimilar) they are. branches that are close to the same height are similar to each other; branches with different heights are dissimilar — the greater the difference in height, the more dissimilarity. Also the different clusters of genes based on the selected lab tissue's are marked by different colors.

9. Downsampled Hierarchical Scatterplot

The Hierarchical scatter plot is a type of pair plot that can be used to visualize the relationship of different pairs of lab tissue's on the clusters of genes formed during dendrogram.Therefore to create Hierarchial scatterplot we need to input the number of clusters in the choose cluster input box. For the Downsampled Hierarchical Scatter plot, we need to specify the number of clusters we want to view. Moreover, this number of clusters corresponds to the number of clusters formed in the Dendrogram plot, so we perform this analysis only after studying the Dendrogram plot. The Downsampled Hierarchical Scatter plot is dynamically sized for the number of clusters, so every user can analyze the dataset based on how many clusters they believe the selected data is forming .

10. Downsampled Hierarchical Heatmap

Hierarchical clustering heatmap is an intuitive way to visualize information from complex data. It’s also called a false colored image, where data values are transformed to color scale. Heat maps allow us to simultaneously visualize clusters of samples(selected lab tissue's) in the column and features (genes) in the rows.
First hierarchical clustering is done of both the rows and the columns of the data matrix. The columns/rows of the data matrix are re-ordered according to the hierarchical clustering result, putting similar observations close to each other. The blocks of ‘high’ and ‘low’ values are adjacent in the data matrix. Finally, a color scheme is applied for the visualization and the data matrix is displayed. Visualizing the data matrix in this way can help to find the variables(selected lab tissue's) that appear to be characteristic for each sample cluster.

10. Heatmap

A heatmap is a plot of rectangular data as a color-encoded matrix.This is a great way to visualize data, because it can show the relation between variables (selected lab tissue's) including genes. The heatmap here shows the relation between the first 100 genes and the selected lab tissue's values to reduce time and complexity .

PCA

PCA reduces the high-dimensional interrelated data to low-dimension by linearly transforming the old variable into a new set of uncorrelated variables called principal component (PC) while retaining the most possible variation.
The first component has the largest variance followed by the second component and so on. The first few components retain most of the variation, which is easy to visualize and summarize the features of original high-dimensional datasets in low-dimensional space. PCA helps to assess which original samples are similar and different from each other.
In our case when the selected number of lab tissue's are higher than 2 then, it is arduous to visualize them at the same time to interpret the genes. PCA transforms them into a new set of variables (PCs) with top PCs having the highest variation. PCs are ordered which means that the first few PCs (generally first 3 PCs but can be more) contribute most of the variance present in the original high-dimensional dataset. These top first 2 or 3 PCs can be plotted easily and summarize the features of all original variables (selected lab tissue's).

11. PCA 2D variables cluster

A PCA 2D variables cluster plot shows how strongly each characteristic or variable influences a principal component. In the plot we can see these vectors(variables) are pinned at the origin of PCs (PC1 = 0 and PC2 = 0). Their project values on each PC show how much weight they have on that PC.

The data points are annotated by grouped tissue or treatment name . For example: PCA analysis output with the "Scanlon lab" displays preblade_adaxial tissue annotation in dark blue which comprises of three replicates of the tissue : B L1 1 preblade adaxial, B L1 2 preblade adaxial and B L1 3 preblade adaxial. The grouped annotation is done to make it easier to identify if same tissue from a lab treated under different time points cluster together or not. From the plot, we observe that two preblade adaxial are placed together whereas one of them is positioned slightly further.

12. PCA 2D observation cluster

A PCA 2D observation cluster plot showing clusters of samples/observations based on their similarity.PCA does not discard any samples or observations. Instead, it reduces the overwhelming number of dimensions by constructing principal components (PCs). PCs describe variation and account for the varied influences of the original characteristics. Such influences, or loadings, can be traced back from the PCA plot to find out what produces the differences among clusters.
Observations further out are either outliers or naturally extreme observations. Plot observations are annotated with shapes and colors to highlight gene models and gene types for example (Classical/Others). In this way, we can easily hover over any data point, especially outliers, to find gene models with extreme observation values across various sample types. (selected lab tissue's)

13. PCA 2D biplot cluster

PCA biplot = PCA observations plot + variable plot.
we can probably notice that a PCA biplot simply merges an usual observations PCA plot with a plot of variables. The arrangement is like this:
*Bottom axis: PC1 score.
* Left axis: PC2 score.
In other words, the left and bottom axes are of the PCA observations plot — use them to read PCA scores of the observations (dots).

Another nice thing about these plots: the angles between the vectors/variables tell us how characteristics or variables correlate with one another.
* When two vectors are close, forming a small angle, the two variables they represent are positively correlated.
*If they meet each other at 90°, they are not likely to be correlated.
*When they diverge and form a large angle (close to 180°), they are negatively correlated.

14. PCA 3D variables cluster

This is similar to the PCA 2D variables cluster plot but now instead of two principal components, we can have three PC'S that contribute to most of the variance present in the original high-dimensional dataset. These top 3 PCs can be plotted easily in 3 dimensional space and summarize the features of all original variables (selected lab tissue's) .

15. PCA 3D observation cluster

This is similar to the PCA 2D observation cluster but now instead of two principal components, we can have three PC'S that contribute to most of the variance present in the original high-dimensional dataset. These top 3 PCs can be plotted easily in 3 dimensional space and summarize the variance in the observations .
A PCA 3D observation cluster plot displays how much variation each principal component captures from the data. If the first three PCs are sufficient to describe the essence of the data.

For further details on these features please go through the below tool :
https://qteller.maizegdb.org/