DOI: 10.5061/dryad.b8gtht7p4
title: Single-cell multi-modal analysis of tumor microenvironment in human non-small cell lung cancer tissues

description: To understand the role of tumor microenvironement in affecting clinical
 outcomes, we generated tissue-matched multiplex immunofluorescence (mIF)
 images, H&E-stained histopathological images, and RNA-seq data of
 human non-small cell lung cancer tissues.
description: # Single-cell multi-modal analysis of tumor microenvironment in non-small
 cell lung cancer (NSCLC) tissues ## Components * mIF_data.h5ad: Multiplex
 immunofluorescence imaging data of NSCLC tissues. Data are stored in an
 AnnData object (denoted as "adata"). * adata.X:
 immunofluorescence signals quantified from each cell with dimension
 N<sub>cells</sub> x N<sub>biomarkers</sub> *
 adata.obs: metadata of each cell, containing the following variables *
 CellID: Unique cell identifiers * Centroid X µm: Nucleus centroid
 coordinate (X-axis) * Centroid Y µm: Nucleus centroid coordinate (Y-axis)
 * Nucleus detection probability as determined by the StarDist model
 (version 0.4.0) * Nucelus: *: Nuclear morphology and texture measurements
 as quantified from the nucleus segmentation results * Cell: *: Cellular
 morphology and texture measurements as quantified from the cell
 segmentation results * phenotype: Cell type as determined by the
 hierarchical lineage assignment strategy * RCN: Membership of the
 recurrent spatial cell neighborhood as determined by the neighborhood
 cell-type composition * patient_id: De-anonymized patient identifier *
 core_id: Tissue core identifier * Histology folder: Nucleus segmentation
 and cell-type classification results on H&E-stained whole-slide images
 by NucSegAI. Each file is named by its tissue identifier as indicated in
 the tissue_patient_map.csv. Each cell nucleus contains the following
 information: * bbox: two dimensional spatial coordinates of the bounding
 box (rectangles) * centroid: nucleus centroid coordinates * contour:
 nucleus contour coordinates * type_prob: the probability of the predicted
 cell-type class * cell_type: the predicted cell-type class * RNA-seq
 folder: Geneset variation analysis of the FFPE RNA-seq data. A csv file of
 dimension N<sub>samples</sub> x
 N<sub>pathways</sub>. The pathway score is determined using
 the GSEAPy package (version 1.1). * tissue_patient_map.csv: Map from
 tissue IDs to patient IDs * deident_clinical.csv: De-identified clinical
 information with the following variables * patient_id: patient identifiers
 * Gender (0: Female, 1:Male) * Race (Asian: 0, Black: 1, White: 2,
 Other/Unknown: 3) * Smoking Status (0: Never, 1: Former, 2: Daily) * Age
 (&lt;70: 0, &gt;=70: 1) * Stage: Cancer stage * Histology class (0: LUAD,
 1: LUSC, 2: Other) * IO_received (0: No, 1: Yes) * IO_response (0: No, 1:
 Yes) * Deceased (0: No, 1: Yes): Whether the patient is deceased within
 the follow-up period * Survival or loss to follow-up (months) ## Authors *
 Yuanning Zheng\ Stanford University\
 [eric2021@stanford.edu](mailto:eric2021@stanford.edu) * Olivier Gevaert\
 Stanford University\ [ogevaert@stanford.edu](mailto:ogevaert@stanford.edu)
 ## Human subjects data The use of human samples in this study was approved
 by Stanford’s Institutional Review Board (IRB). The IRB waived the
 requirement for informed consent due to the de-identified retrospective
 nature of the study. Data were de-identified in accordance with HIPAA
 privacy regulations. Patient and tissue identifiers were anonymized by
 randomizing them into numerical integers (1-136), and ages were
 categorized into two ranges: less than 70 and 70 or older.