We will soon have a pre-print describing the new 2019 automated data analysis workflow. The code-base for the build can be found here.

We will soon have a pre-print describing the new 2023 automated data analysis workflow. The code-base for the build can be found here.

We will upload a finalized 2023 data workflow soon.

After selecting the ‘Box Plot' radio button [1], you can choose a 2017, 2019, or DNTx dataset [2]. Then, you can tweak which ‘IDs' [3] and ‘Tissues' [4] you would like to display by clicking in the respective boxes and starting to type (allowed values will auto-fill). You can also delete values by clicking on them and hitting the ‘delete' key on your keyboard. You can change the display of the box plots by selecting a different value for the 'Number of Columns' field [5]. A lower number will squeeze more plots in each column. When you are done tweaking these parameters, you can click the big blue '(Re)Draw Plot!' button [6] and wait a few seconds for the plot to appear.

If you mouse over a data point, you will get metadata about that particular sample [7].

Each gene and tissue combination is given its own box. Depending on how the plot is oriented, one axis is log1p transformed z-counts, and the other axis contains the samples, colored by tissue. The right panel contains tables with external links to gene info [9], as well as the zCount values and rank of each gene in the chosen tissues (lower is more highly expressed).

Each gene gets its own box. The y-axis is length scaled TPM (log2 transformed), and the x-axis is samples, colored by tissue. The right panel contains tables with external links to gene info [8], as well as the absolute TPM values and rank of each gene in the chosen tissues (lower is more highly expressed).

After selecting the ‘Heatmap’ radio button [1], you can choose a 2017, 2019, or DNTx dataset [3]. Then, you can tweak which ‘IDs’ [4] and ‘Tissues’ [5] you would like to display by clicking in the respective boxes and starting to type (allowed values will auto-fill). You can also delete values by clicking on them and hitting the ‘delete’ key on your keyboard. The display of the heatmap can be changed depending on whether you want to cluster your samples by rows or columns by clicking the appropriate checkboxes [2]. When you are done tweaking these parameters, you can click the big blue '(Re)Draw Plot!' button [6] and wait a few seconds for the plot to appear

The heatmap is an efficient way to display the expression of many genes and tissues. More yellow indicates higher expression, and further information about each chosen gene can be found by following the external links in the table to the right [7].

The 2023 heatmap operates at the tissue level, which results in more samples being present.

This produces a table containing metadata for the gene and tissue combo selected by the user.

First you select the genes [1], stages of development [2], and cell types [3] you would like to view by clicking in their respective boxes and starting to type (allowed values will auto-fill). You can delete values by clicking on them and hitting the 'delete' key on your keyboard. Next, select whether you would like to view your cell types as rows or columns in the 'Plot Orientation' section [4]. Furthermore, the ‘Display Individual Sample Values’ checkbox [5] will enable you to hover your mouse over a data point and show the metadata for that particular sample [7]. When you are done tweaking those parameters, click the big blue '(Re)Draw Plot!' button [6] and wait a few seconds.

Each gene and developmental stage combination gets its own box. These features are further faceted between the back and front of the eye. Depending on how the plot is oriented, one axis is length scaled count value (log2 transformed), and the other axis contains the cell types. If hover data is toggled on [5], then each point is colored by an independent study and the size of the point is a log2 scaled percentage of cells that have detected expression of the gene.

This tool produces a plot of principal components from PCA (principal component analysis) conducted on our eyeIntegration 2.0 database. To begin, the user must select the 'eyeIntegration PCA Plot' radio button [1]. Then, you can select and unselect various checkboxes to include or exclude GTEx and single-cell RNA-seq data, as well as the visualization for which genes contribute most to your chosen principal components of interest [2]. Once you have decided which data you would like to visualize, you can select two principal components to plot [3]. When you are done tweaking those parameters, click the big blue '(Re)Draw Plot!' button [4] and wait a few seconds for the plot to appear. Since this plot was built using the ggplotly R package, you can hover your mouse over a point to see that sample’s metadata [6], and adjust the plot window using various scaling parameters provided on the top right of the plot [7]. Tissue types can be included and excluded by clicking directly on the name of the tissue within the legend on the right side of the plot [8]. Finally, if you would like to export the data used to make this plot, you can click the big blue 'Download Plot Data' button and download a CSV containing the data [5].

This tool produces a plot of principal components from PCA (principal component analysis) conducted on our eyeIntegration 2.0 database and projects user-inputted data onto this PCA space. To begin, the user must select the 'Project Your Data on PCA' radio button [1]. Then, you can select and unselect checkboxes to include or exclude GTEx and single-cell RNA-seq data from eyeIntegration [2]. Once you have decided which samples you would like to include in visualization, you can click the ‘Browse…’ button and search your computer for a dataset to upload for projection [3]. This data should be in one of the following formats: csv, tsv, csv.gz, or tsv.gz. In this tool, the projected data can be either faceted against, or overlayed onto the existing eyeIntegration database. This can be selected from the dropdown menu under, ‘How Would You Like to Display Your Data?’ [4]. Finally, you can type in a name for your dataset to be included within the visualization [5], then select which principal components to plot [6]. When you are done tweaking those parameters, click the big blue '(Re)Draw Plot!' button [7] and wait a few seconds for the plot to appear. Since this plot was built using the ggplotly R package, you can hover your mouse over a point to see that sample’s metadata [9], and adjust the plot window using various scaling parameters provided on the top right of the plot [10]. Tissue types can be included and excluded by clicking directly on the name of the tissue within the legend on the right side of the plot [11]. Finally, if you would like to export the data used to make this plot, you can click the big blue 'Download Plot Data' button and download a CSV containing the data [8]. This data will include a scaled version of the original user data to plot alongside the eyeIntegration 2023 dataset.

Faceted View



Overlayed View

This is short for differential expression. We have pre-calculated 55+ differential expression tests. All eye tissue - origin pairs were compared to each other. We also have a synthetic human body set, made up of equal numbers of GTEx tissues (see manuscript, above, for more details). The word cloud displayed shows as many as the top 75 terms used in enriched GO terms in the selected comparison. The table data shows the actual GO terms. You can search for the comparison of your choice.

These are the values taken from the limma differential expression topTable() summary table. The following has been taken from the limma manual and edited to match parameters we used (https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf):

A number of summary statistics are presented by topTable() for the top genes and the selected contrast. The logFC column gives the value of the contrast. Usually this represents a log2-fold change between two or more experimental conditions although sometimes it represents a log2-expression level. The AveExpr column gives the average log2-expression level for that gene across all the arrays and channels in the experiment. Column t is the moderated t-statistic. Column P.Value is the associated p-value and adj.P.Value is the p-value adjusted for multiple testing (False Discovery Rate corrected).

The B-statistic (lods or B) is the log-odds that the gene is differentially expressed. Suppose for example that B = 1.5. The odds of differential expression is exp(1.5)=4.48, i.e, about four and a half to one. The probability that the gene is differentially expressed is 4.48/(1+4.48)=0.82, i.e., the probability is about 82% that this gene is differentially expressed. A B-statistic of zero corresponds to a 50-50 chance that the gene is differentially expressed. The B-statistic is automatically adjusted for multiple testing by assuming that 1% of the genes, or some other percentage specified by the user in the call to eBayes(), are expected to be differentially expressed. The p-values and B-statistics will normally rank genes in the same order. In fact, if the data contains no missing values or quality weights, then the order will be precisely the same.

The Macosko data is a single-cell (~45,000) retina RNA-seq mouse P14 C57BL/6 dataset from Mackosko and McCarroll's field defining paper. The cluster / cell type assignments are taken from here. The Clark data is a 100,000 cell plus mouse retina RNA-seq time series dataset. Their pre-publication manuscript is on bioRxiv. Data was pulled from here.

To efficiently display a huge amount of information, expression across many individual cells is averaged by cell type, (if available) age, and gene. You can select the Macosko or Clark dataset [1], then one gene [2] to plot. The gene expression is displayed as a heatmap, with each row being a retina cell type (derived by the respective authors) and each column [4] is a time point, arranged from youngest to oldest. More yellow is higher expression [5].

You can add the rank of expression (or rank of percentage of cells with detectable expression of selected gene) with this radio

This data table set shows the data used to make the Mouse Single Cell Retina Expression plots in Gene Expression.

This shows the t-SNE tissue clustering for the bulk human eye tissues along with the GTEx data-set. Hovering the mouse over each data point will show the metadata. Changing the perplexity will demonstrate how low values artificially create sub-groups while higher value (above 30 or so) largely recapitulate tissue type. It also demonstrates that the tissue clustering is stable at higher perplexities.

Each data point is a single cell from the Macosko and McCarroll or the Clark and Blackshaw [2]. Dimensionality reduction with done with the t-SNE (Macosko) or UMAP (Clark) algorithm. Cluster assignments were taken from the respective papers. While we did the t-SNE on the Macosko data, the Clark authors provided the UMAP coordinates. The Clark dataset was generated across multiple time-points during development and thus, you can select time points of interest [4]. Only one gene can selected at a time [3], as it is very computationally expensive to plot many points. Points (cells) expressing the gene of interest are plotted in darker color [arrow]. Hovering over each point [6] will show the metadata for the cell.

This is a weighted gene expression correlation network. The gene expression information for all retina or all RPE tissues is used to identify gene pairs whose expression is correlated with each other. All of the pair-wise correlations are assessed to build a network of interactions.

We imagine the most common use is to search for your gene of interest (GOI). Simply type your GOI into the search box [1]. If it is not in the network, then the name will not appear. After selecting the GOI, the network will reload to display the module the gene is in, as well as several of the most correlated partners. You can adjust the number of displayed correlated genes by changing the K-nearest genes panel [2]. Hovering over a gene name in the network will display GO terms for the gene [3].

Unfortunately, we have no way of knowing this. Since the network algorithms use correlations, the gene to gene interactions have no directional information.

The count plot [1] simply shows the number of genes in each module. A gene can only be in one module. The pair-wise gene connection strength [2] shows the strongest gene partners for the selected gene. If a module search is selected, then this table shows all gene to gene edge connection strengths (higher is stronger) in the module. The gene connectivity table [3] shows the kWithin metric for each gene in the module, which denotes how connected (and important) the gene is across the module. The GO term table [4] shows the significant GO terms for the genes in the module. This allows you to get a sense of the function of the module.

The edge table allows you to search for a gene and it returns all significant (edge length > 0.01) correlated genes ACROSS the entire network. Using the 'Connections to show' radio button, you can control whether only extramodular or intramodular (or both) connections are included in the table.