Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock () or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

SCLC TumorMinerCDB

Version: 1.0 - Release: April 2026


Plot point tooltips provide additional information.

Data type Abbreviation Identifier ID
Drug response act drug name
DNA Mutation mut gene name
RNA-seq Expression using log2(FPKM+1) xsq gene name
Signature sig signature name
NMF cluster nmf NMF label
Miscellaneous phenotypic mda Miscellaneous phenotypic name
Hallmark gene set hll Hallmark gene set name

Pattern comparison results are computed with respect to that data defined and shared by both the x and y-axis inputs.




The number on top of each boxplot represent its number of patients.


Download Plot Data


Plot point tooltips provide additional information.

Data type Abbreviation Identifier ID
Drug response act drug name
DNA Mutation mut gene name
RNA-seq Expression using log2(FPKM+1) xsq gene name
Signature sig signature name
NMF cluster nmf NMF label
Miscellaneous phenotypic mda Miscellaneous phenotypic name
Hallmark gene set hll Hallmark gene set name

Pattern comparison results are computed with respect to that data defined and shared by both the x and y-axis inputs.


Please type patient Identifier:

or select a patient by clicking on a row in the table below


Sample Data

(++) For weak cluster assignment we postfix the NMF cluster with *

Please select a row on the Sample Data table and select the Find Similar Patient or Cell line Button to find similar samples



Gene Expression Data

Note: The genes values are in log2(FPKM + 1).


Mutation Data

Note: The genes values are between 1 and 100 and represent the probability of homozygous, function-impacting somatic variants (see details in help section).


Methylation Data

Note: The genes values are between 0 and 1 and represent the promoter methylation average beta value based on pre-selected probes (see details in help section).


Copy Number Data

Note: The genes values are the average log2 probe intensity ratios based on methylation data (see details in help section).


Looking at specific genes values across all data types

Note: You can enter one or multiple genes (in the left panel); look at their values in expression, mutation, methylation or copy number; and compare to the median, minimum, maximun values for all patients with same disease


Immune therapy treatment options

Note: Drugs were selected based on target gene having expression values (in log2 FPKM) greater or equal than 6






Download Data

Download Footnotes
Download Footnotes







Download current patient set information
Download patients annotation

Table of Contents

Introduction

SCLC TumorMinerCDB is an interactive web application that simplifies access and exploration of Small Cell Lung Cancer (SCLC) patient genomic and treatment response data across different data types (see About the data section for more details). Navigation in the application is done using main menu tabs (see figure below). It includes 10 tabs: 2 variable Analyses, Multivariable Analysis, Expression distribution, Mutation Variants, Prognosis biomarkers, Predictive biomarkers, MyPatient, Download data, Search IDs and Help. 2 variable 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 (sub-menu) options are available on the top for the 2 variable Analysis, Multivariate Analysis, mutation variants prognosis and predictive biomarkers tabs (see sub-menu on figure). The sub-menu first option result is displayed by default (Figure 1).

Screenshot of CellMinerCDB Application

Figure 1: Main application interface

2 variable Analyses

Molecular and/or drug response patterns can be compared to look for possible association in our patient data. The 2 variable 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.

Input data

  1. The horizontal axis data choices includes 4 fields to be filled by the user:
  • Horizontal Axis Patient Set selects the data set. For now we have only one patient data set.
  • Horizontal Axis Data Type selects the data type to query. The options for this appear in the Horizontal Axis Data Type dropdown. See the Metadata tab for descriptions and abbreviations.
  • Identifier selects the identifier of interest for the above selected data type. For instance, if drug response is selected, the user can enter a drug name. The Search IDs tab explores potential identifiers interactively, or to download datasets of interest.
  • Horizontal Axis Range allows the user to control the horizontal axis range for better visualization.

  1. The vertical axis data choices are as explained above for the horizontal axis.

  2. Selected subsets: by default, all tissues are selected and included in the scatter plot. To include or exclude patient samples from specific tissues or subsets, the user should specify:
  • Select Subsets to include or exclude specific tissues or other subsets
  • Select Subset(s) functionality at the bottom of the left-hand panel. The subsets are organized as a tree and are all selected by default. In order to select a specific subset/tissue, the user should click on the root of the tree represented by the triangle icon to expand the tree recursively until reaching a specific sub tree or leaf. The selection is finalized by clicking on the leaf label. On Macs, more than one tissue of origin may be selected using the “command” button. On PC’s use the “control” key. All patient samples were mapped to the two first of the four-level OncoTree cancer tissue type hierarchy developed at Memorial Sloan-Kettering Cancer Center.
  • Select Subset(s) to Color to locate samples related to desired tissues or subsets within the scatter plot. By default, the samples are colored by their dataset assigned color. Selecting a specific subset makes related samples appear in red while remaining samples are colored in grey. The Show Color checkbox should be active.

Plot Data

Any pair of features across patient samples can be plotted (as a scatterplot) including the resultant Pearson correlation and p-value. Since the website includes datasets from multiple institutions, we provide a correlation value for each dataset. The p-value estimates assume multivariate normal data, and are less reliable as the data deviate from this. Please use the scatter plot to check the data distribution (e.g., for outlying points outside of a more elliptically concentrated set). The user also has the option to select a set of samples to highlight using a list box of sample Ids on the top of the scatter plot.

Some options are available to play with the plot image using icons on the top from left to right:
icon Downloads the plot as a png.
icon Allows the user to zoom in on an area of interest by clicking and dragging with the pointer.
icon Autoscales the image.
icon Allows the user to create horizontal and vertical line from either a sample dot or the regression line, by hovering over them.

Screenshot of CellMinerCDB Application

Figure 2: An example scatterplot of ASCL1 gene expression (horizontal axis) versus YAP1 gene expression (verical axis). 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 sample, tissue, Onco tree designation, and x and y coordinate values.

View Data

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 sample, the x-axis value, the y-axis value, the tissue of origin, the 4 onco-tree levels and other available sample annotation such as Epithelial Mesenchymal Transition (EMT) status. Within the header the selected features are prefixed by the data type abbreviation and post-fixed by the data source.

Screenshot of CellMinerCDB Application

Figure 3: Shows the selected values for ASCL1 gene expression (horizontal axis) and YAP1 gene expression (vertical axis) across all samples. The features are coded as xsqASCL1_patient and xsqYAP1_patient where “xsq” represent gene expression based on rnaseq.

Compare Patterns

This option allows one to compute the correlation between the selected feature as defined from the specified **Horizontal Axis sample Set, Horizontal Axis Data Type**, and **Identifier** and either all drug or all molecular data from the (same) Horizontal Axis or Vertical Axis source. By default all tissues are selected however the user can restrict the analysis to specific tissue of origin.

Pearson’s correlations are provided, with reported p-values (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).

Screenshot of CellMinerCDB Application

Figure 4: Shows correlation results for ASCL1 gene with all other molecular features for all data types (gene expression, mutation and metadata) sorted by correlation value with gene location and target pathways (annotation field).

Multivariable Analysis

The ‘Multivariable Analysis’ option (or module) has multiple tabs including Heatmap, Data, Plot, Cross-Validation, Technical Details and Partial Correlation (described below), and allows construction and assessment of multivariate linear response prediction models within a patient set. For instance, we can assess prediction of a drug response based on some genes expression. To construct a regression model, you first need to specify the input data in the left side panel.

Input data

  1. The response variable is chosen by selecting:
  • Response patient Set for now the current patient set
  • Response Data Type selects the data type for the response variable (example: drug response or a molecular dataset). The options appear in the Response Data Type dropdown. See the Metadata tab for data types description.
  • Response Identifier selects the identifier for the response variable (e.g., a specific drug or gene identifier)

  1. The predictor variables are chosen by selecting:
  • Predictor Data Type/s selects the data type(s) for the predictors variables. Use command button on Macs or control key on PCs to select more than one dataset.
  • Minimal Predictor Range provides a required minimum value for the identifier to be included for the first listed data type. The default is 0. One may increase this value to eliminate predictors that are considered to have insufficient range to be biologically meaningful.
  • Predictor Identifiers selects the identifiers for the predictors.When using the Linear Regression algorithm, predictors are required to be enter. In figure 5, we explore linear model prediction of Durvalumab/Olaparib drug response choosing MAGEH1 and NFKBIA gene expression. Identifiers from different types may be combined using 2 methods. In the first, select multiple Data Types as desired, and enter your identifiers. The model will be built automatically using those Data Types and Identifiers. For example, if expression and mutation are selected as Data Types and MAGEH1 and NFKBIA are entered as identifiers, the model will be built using 4 identifiers: expMAGEH1, expNFKBIA, mutMAGEH1 AND mutNFKBIA. In the second, more specific approach, you enter the identifier with the data type prefix. For example, if your predictor variables are specifically the expression value for MAGEH1 and mutation value for NFKBIA then you can enter as identifiers: expMAGEH1 and mutNFKBIA. When using the Lasso algorithm, predictors are optional for the Lasso algorithm (see point 4) since it identifies automatically the ones that best fit the Lasso model.

  1. Select Tissue/s of Origin is used to include or exclude specific tissues, as defined in the next step. By default, all tissue types are included, howver you can select one or any multiple of tissue types (to include or exclude). Use the radio buttons To include or To exclude to select specific tissues to include or exclude. To make selections on Macs, use the “command” key. To make selections on PC’s use the “control” key

  2. Algorithm: by default, the mode Display is selected to review the data however you can select the Linear Regression model or the Lasso model (penalized linear regression model) machine learning approach. Linear regression is a linear approach to modeling the relationship between a response (or dependent variable) and one or more predictor variables (or independent variables). It is implemented using the R stats package lm() function. 10-fold cross validation is applied to fit model coefficients and predict response, while withholding portions of the data to better estimate robustness. Lasso is Least absolute selection and shrinkage operator, a penalized linear regression model. Lasso is implemented using the cv.glmnet function (R package glmnet). 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. The lasso lambda parameter controls the tradeoff between model fit and variable set size. The Lambda is set to the value giving the minimum error with 10-fold cross-validation. Set.seed, the initial seed is set to 1. Alpha is set to one. The minimum lambda is used to select the intercept and the coefficient for the variable (there is no range). 10-fold cross validation is applied to fit model coefficients and predict response, while withholding portions of the data to better estimate robustness. For further details on either of these outputs, see the respective R packages. If Lasso algorithm is selected, you have to specify:
  • Select Gene Sets: The gene selection is based on curated gene sets such as DNA Damage Repair DDR or Apoptosis. The user can select one or more gene sets.
  • Maximum Number of Predictors allows choice of the number of predictors (default 4)

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.

Heatmap

This option provides the observed response and predictor variables across all samples as an interactive heatmap. For the heatmap visualization, data are range standardized (subtract the minimum, and divide by the range) to values between 0 and 1, based on the value range within all rows of a given data type (by default) or within each row of data (if ‘Use Row Color Scale’ is selected). For data types other than mutation data, the range is trimmed to the difference between the 95th and 5th percentiles; values below or above the 5th and 95th percentile values are scaled to 0 and 1, respectively. In the case of mutation data, the range used for scaling is the difference between the maximum and minimum values. If the values within a data type (or data row if ‘Use Row Color Scale’ is selected) are constant, the scaled value for heatmap visualization is set to 0.5.

The user can restrict the number of samples to those that have the highest or lowest response values by selecting Number of High/Low Response Lines to Display. The user can download the heatmap related data by clicking on Download Heatmap Data.