CellMinerCDB is an interactive web application that simplifies access and exploration of cancer cell line pharmacogenomic data across different sources (see Metadata section for more details). Navigation in the application is done using main menu tabs (see figure below). It includes 5 tabs: Univariate Analyses, Regression Models, Metadata, Search and Help. Univariate Analyses is selected by default when entering the site. Each option includes a side bar menu (to choose input) and a user interface output to display results. Analysis options are available on the top for both the Univariant Analysis and Regression model tabs (see sub-menu on figure). The sub-menu first option result is displayed by default (Figure 1).
Figure 1: main application interface
Molecular and/or drug response patterns across sets of cell lines can be compared to look for possible association. The univariate analysis panel includes 3 options: Plot data, Download Data and Compare Patterns. Almost all options have the same input data in the left side panel.
Any pair of features from different sources across common cell lines can be plotted (as a scatterplot) including the resultant Pearson correlation and p value. To generate a scatterplot, enter on the side bar panel:
|Downloads the plot as a png.|
|Allows the user to zoom in on an area of interest by clicking and dragging with the pointer.|
|Autoscales the image.|
|Allows the user to create horizontal and vertical line from either a cell line dot or the regression line, by hovering over them.|
Figure 2: An example scatterplot of SLFN11 gene expression (x-axis) versus Topotecan drug activity (y-axis)/ both from the NCI60. Since Topotecan has 2 different drug ids in the NCI-60, the one with the lowest number of missing data is selected (here 609699). However, the user can type in their specific drug ID of interest. The Pearson correlation value and p value appear at the top of the plot. A linear fitting curve is included. This is an interactive plot and whenever the user changes any input value, the plot will be updated. Any point in the plot can be hovered over to provide additional information about cell line, tissue, Onco tree designation, and x and y coordinate values.
This option both displays the data selected from the Plot Data tab in tabular form, and provides a Download selected x and y axis data as Tab-Delimited File option. The user can change the input data in the left selection panel as described for Plot Data. The displayed table include the cell line, the x-axis value, the y-axis value, the tissue of origin and the 4 onco-tree levels. Within the header the selected features are prefixed by the data type abbreviation and post-fixed by the data source.
Figure 3: shows the selected values for SLFN11 gene expression (x-axis) and Topotecan (id 609699) drug activity (y-axis) from the NCI-60 across all common lines. The features are coded as expSLFN11_nci60 and act609699_nci60 where “exp” and “act” represent respectively prefixes for gene expression based on z-score and drug activity.
This option allows one to compute the correlation between the selected feature as defined from the specified Cell Line Set, Data Type, and Identifier from either the x or y-axis selections, and either all drug or all molecular data from the same source.
Pearson’s correlations are provided, with reported p-values (not adjusted for multiple comparisons) in tabular form. This displays features are organized by level of correlation, and includes target pathway for genes and mechanism of action (MOA) for drugs (if available).
Figure 4: shows correlation results for SLFN11 gene with all other molecular features for all NCI60 datasets sorted by correlation value with gene location and target pathways (annotation field).
The ‘Regression Models’ option (or module) has multiple tabs including Heatmap, Data, Plot, Cross-Validation, Tehnical Details and Partial Correlation (described below), and allows construction and assessment of multivariate linear response prediction models. For instance, we can assess prediction of a drug activity based on some genes expression. To construct a regression model, you need to specify:
Once all the above information is entered, a regression model is built and the results are shown in different ways such as the technical details of the model, observed vs. predictive responses plots or variables heatmap. Find below an explanation of different output for the regression model module.
This option provides the observed response and predictor variables across all source cell lines as an interactive heatmap. The user can restrict the number of cell lines to those that have the highest or lowest response values by selecting Number of High/Low Response Lines to Display
Figure 5: An example heatmap where we selected topotecan as a response variable and SLFLN11 and BPTF gene expression as predictor variables. In this example, we chose to display only 40 cell lines that have the most 20 highest and 20 lowest values for topotecan activity.
In case, the Lasso algorithm is selected more predicted variables are shown based on model result as shown below (STK17B and ABCD3 new genes added)
Figure 6: same example as previous figure with the lasso algorithm
This option shows the detailed data for the model variables for each cell line. Both the 10-fold cross validation (CV) as well as the predicted responses are given. The data is displayed as a table with filtering options for each column.
Figure 7: data related to the simple linear regression model presented in the previous section.
This option enables one to plot and compare the observed response values (y-axis) versus the predicted response values (x-axis). The predicted response values are derived from a linear regression model fit to the full data set.
Figure 8: plot comparing Topotecan observed vs. predicted activity with high correlation value of 0.84
This option enables to plot the observed response values (y-axis) versus the 10-fold cross-validation predicted response values (x-axis). With this approach, the predicted response values are obtained (over 10 iterations) by successively holding out 10% of the cell lines and predicting their response using a linear regression model fit to the remaining 90% of the data. Cross-validation is widely used in statistics to assess model generalization to independent data – with the caveat that the independent data must still share the same essential structure (i.e., probability distribution) as the training data. It can also indicate possible overfitting of the training data, such as when the observed versus full data set model-predicted correlation (shown in ‘Plot’) is substantially better than the observed versus cross-validation predicted correlation (shown in ‘Cross-Validation’).
Figure 9: plot comparing Topotecan observed vs. cross-validation predicted activity with still high correlation value of 0.82
This option enables the user to view the R statistical and other technical details related to the predicted response model. To save, these results may be copied and pasted into the document or spreadsheet of your choice.
Figure 10: example of regular regression model fitting results
This function is used to identify additional predictive variables for a multivariate linear model. Conceptually, the aim is to identify additional predictive variables that are independently correlated with the response variable, after accounting for the influence of the existing predictor set. Computationally, a linear model is fit, with respect to the existing predictor set, for both the response variable and each candidate predictor variable. The partial correlation is then computed as the Pearson’s correlation between the resulting pairs of model residual vectors (which capture the variation not explained by the existing predictor set). The p-values reported for the correlation and linear modeling analyses assume multivariate normal data. The two-variable plot feature of CellMinerCDB allows informal assessment of this assumption, with clear indication of outlying observations. The reported p-values are less reliable as the data deviate from multivariate normality.
In order to run a partial correlation analysis, the user should first construct a linear model (providing response and predictor variables as explained earlier - steps 1 to 5 in figure below-) and then:
Figure 11: An example of partial correlation results for selected gene expression data using all gene sets.
This option enumerates for each cell line set, the available data types that could be queried within the app providing the data type abbreviation or prefix, description, feature value unit (z-score, intensity, probability …), platform or experiment and related publication reference (pubmed). The user should:
Figure 12: shows all data types for NCI60
This page lists the identifiers (ID) available in the selected data source for use in the univariate analysis or regression models. The user chooses:
This enables to search all related ID for each combination. For the molecular data, the gene names (ID) and specific data type information are provided. For the drugs and compounds, the identifiers (ID), Drug name (when available), and Drug MOA (when available) are displayed. The user can scroll down the whole list of IDs, or search specific ID(s) by entering a value in the header of any column.
For the NCI-60 and NCI/DTP SCLC, the drug identifiers (ID) are NSC's or names. For the CCLE, GTRP, and CTRP, the drug identifiers are the Drug names.
Figure 13: Example of a search: if looking for a drug ID in the NCI-60 select “NCI-60” as the cell line source and select “Drug Activity” as the data type. You can type in search box of column “Drug name” or “MOA”.
For all data sources, the gene ID is the gene name (Hugo name)
Figure 14: Example of a search: if looking for a gene ID in the NCI-60 select “NCI-60” as the cell line source and select “gene expression” as the data type. You can type in search box of column “gene name” or “entrez gene id” or “Chromosme”…
In order to select multiple choice from a list, use “command” button for Mac or “alt” button for PC and then click
You can change the x-axis or y-axis lower or higher value to have different views of the displayed plot.
It is a checkbox that enable and disable colors in the scatter plots
For an introduction please refer to our video tutorial
Basic linear regression models are implemented using the R stats package lm() function
Lasso (penalized linear regression models) are implemented using the glmnet R package. The lasso performs both variable selection and linear model coefficient fitting. The lasso lambda parameter controls the tradeoff between model fit and variable set size. Lambda is set to the value giving the minimum error with 10-fold cross-validation. For either standard linear regression or LASSO models, 10-fold cross validation is applied to fit model coefficients and predict response, while withholding portions of the data to better estimate robustness.
Please send comments and feedback to
CellMinerCDB integrates data from the following sources, which provide additional data and specialized analyses.
For specific information about the data made available for particular sources, please refer to the 'Metadata' navbar tab.
Drug mechanism of action details:
Gene sets used for annotation of analysis results or algorithm input filtering were curated by the NCI/DTB CellMiner team, based on surveys of the applicable research literature.
The CellMinerCDB application is developed and maintained using R and Shiny by:
Shankavaram UT, Varma S, Kane D, Sunshine M, Chary KK, Reinhold WC, Pommier Y, Weinstein JN. CellMiner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics. 2009 Jun 23;10:277. doi: 10.1186/1471-2164-10-277.
Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, Pommier Y. CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res. 2012 Jul 15;72(14):3499-511. doi: 10.1158/0008-5472.CAN-12-1370.
Reinhold WC, Sunshine M, Varma S, Doroshow JH, Pommier Y. Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60. Clin Cancer Res. 2015 Sep 1;21(17):3841-52. doi: 10.1158/1078-0432.CCR-15-0335. Epub 2015 Jun 5.
Luna A, Rajapakse VN, Sousa FG, Gao J, Schultz N, Varma S, Reinhold W, Sander C, Pommier Y. rcellminer: exploring molecular profiles and drug response of the NCI-60 cell lines in R. Bioinformatics. 2015 Dec 3. pii: btv701.