Tumor-Immune Partitioning and Clustering

Introduction

Algorithm

Examples

Run TIPC

References

Contacts

Help

Query data and parameters

2D Cell Data

  • 2D Cell Data is a data frame of cell spatial data with 5 following variables:​

    • Phenotype​

      • cell types, i.e. tumor, stroma, immune​

    • X​

      • X coordinate of cells, in pixels​​

    • Y

      • Y coordinate of cells, in pixels​​​

    • tumor_ids​

      • tumor or patient identifiers, character strings​​​

    • core_ids​

      • core identifiers, character strings​​​​

  • The 2D Cell Data in the example cell data is a data frame containing 30,918 immune cells, 62,669 stromal cells, and 189,428 tumor cells.​​ Totally, there are 283,015 rows with the 5 variables for this 2D Cell Data.

    • Density Data (OPTIONAL for annotating user-input tumor density data to clustering heat maps)​

  • Density Data is a data frame containing tumor_ids in row.names and 2 following variables:​

    • density_ct1​​

      • density values of celltype1​​

    • density_ct2​​

      • density values of celltype2​

  • The Density Data in the example cell data is a data frame containing 50 rows (i.e. tumors) and the 2 variables​.

    • Survival Data (OPTIONAL for Kaplan Meier curves & cox proportional hazards regression analyses)​

  • Survival Data is a data frame of the survival and clinicopathological data for tumors that contains tumor_ids in row.names and n+2 following variables:​

    • time​

      • survival time, numeric​

    • cens​

      • censoring data, binary​

    • covariate_1​​

      • covariate var1, factor​

    • covariate_​2

      • covariate var2, factor​

      ...

    • covariate_n

      • covariate varn, factor​

  • The Survival Data in the example cell data is a data frame containing 50 rows (i.e. tumors) and the 16 variables​.

    • Hexagon Size Min ​

  • OPTIONAL should be ≥ 20; default value is 40

    • Hexagon Size Max ​

  • OPTIONAL should be ≤ 150; default value is 60

    • Step Size

  • OPTIONAL should be ≥ 5; default value is 10

    • TIPC web server allows users to assess the tumor-immune cell interactions at different resolution from Hexagon Size Min to Hexagon Size Max at Step Size. Selection of the optimal subregion size requires balancing the degree of resolution for immune cell partitioning between tumor epithelial and stromal areas against the probability of generating an excess number of uninformative subregions lacking immune cells (Figure 1).​

    Number Cluster Min

  • OPTIONAL should be ≥ 2; default value is 2

    • Number Cluster Max

  • OPTIONAL should be ≤ 30; default value is 6

    • TIPC web server facilitates users to evaluate the spatial characteristics (as quantified by TIPC parameters) at different resolution.​

    Email address

  • This is an optional field. A link to the results page will be sent to the email address provided.
  • For jobs that could take a long time, it may be useful to provide an e-mail id.
  • If no address is provided care should be taken to save the URL that points to the results immediately after submitting a job. This URL refreshes every minute and display the results as soon as the job terminates.

    • Figure 1. Evaluation of effect of subregion size on TIPC spatial parameter value distribution, using CD3+ T-cells in 931 CRC samples. Two representative ROIs demonstrating different subregion sizes in (a) a stromal region and (b) a tumor region predominating CRC tissue sections. (c) Distribution of TIPC spatial parameter values (in normalized counts) across a range of subregion sizes i.e., 20-55 µm. Subregion sizes smaller than 30 µm demonstrated an underrepresented I:T low measure .

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