Statistical analysis of single-cell protein data

被引:0
|
作者
Fridley, Brooke L. [1 ,2 ]
Vandekar, Simon [3 ]
Chervoneva, Inna [4 ]
Wrobel, Julia [5 ]
Ma, Siyuan [3 ]
机构
[1] H Lee Moffitt Canc Ctr & Res Inst, Dept Biostat & Bioinformat, Tampa, FL 33612 USA
[2] Childrens Mercy Hosp, Biostat & Epidemiol Core, Kansas City, MO 64108 USA
[3] Vanderbilt Univ, Dept Biostat, Med Ctr, Nashville, TN 37203 USA
[4] Thomas Jefferson Univ, Div Biostat, Philadelphia, PA 19107 USA
[5] Emory Univ, Dept Biostat & Bioinformat, Atlanta, GA 30322 USA
基金
美国国家卫生研究院;
关键词
spatial biology; multiplex immunofluorescence; single-cell protein; tumor microenvironment; biostatistical analysis; spatial analysis; CANCER; CYTOMETRY;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Immune modulation is considered a hallmark of cancer initiation and progression, with immune cell density being consistently associated with clinical outcomes of individuals with cancer. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor microenvironment (TME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in the TME with enhanced reproducibility and accuracy. Generally, mIF data has been used to examine the presence and abundance of immune cells in the tumor and stroma compartments; however, this aggregate measure assumes uniform patterns of immune cells throughout the TME and overlooks spatial heterogeneity. Recently, the spatial contexture of the TME has been explored with a variety of statistical methods. In this PSB workshop, speakers will present some of the state-of-the-art statistical methods for assessing the TIME from mIF data.
引用
收藏
页码:654 / 660
页数:7
相关论文
共 50 条
  • [31] Single-cell multiomics: technologies and data analysis methods
    Jeongwoo Lee
    Do Young Hyeon
    Daehee Hwang
    Experimental & Molecular Medicine, 2020, 52 : 1428 - 1442
  • [32] An introduction to representation learning for single-cell data analysis
    Gunawan, Ihuan
    Vafaee, Fatemeh
    Meijering, Erik
    Lock, John George
    CELL REPORTS METHODS, 2023, 3 (08):
  • [33] Complex Analysis of Single-Cell RNA Sequencing Data
    Khozyainova, Anna A. A.
    Valyaeva, Anna A. A.
    Arbatsky, Mikhail S. S.
    Isaev, Sergey V. V.
    Iamshchikov, Pavel S. S.
    Volchkov, Egor V. V.
    Sabirov, Marat S. S.
    Zainullina, Viktoria R. R.
    Chechekhin, Vadim I. I.
    Vorobev, Rostislav S. S.
    Menyailo, Maxim E. E.
    Tyurin-Kuzmin, Pyotr A. A.
    Denisov, Evgeny V. V.
    BIOCHEMISTRY-MOSCOW, 2023, 88 (02) : 231 - 252
  • [34] Interactive single-cell data analysis using Cellar
    Hasanaj, Euxhen
    Wang, Jingtao
    Sarathi, Arjun
    Ding, Jun
    Bar-Joseph, Ziv
    NATURE COMMUNICATIONS, 2022, 13 (01)
  • [35] The Current State of Single-Cell Proteomics Data Analysis
    Vanderaa, Christophe
    Gatto, Laurent
    CURRENT PROTOCOLS, 2023, 3 (01):
  • [36] SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data
    Das, Samarendra
    Rai, Shesh N.
    GENOMICS, 2021, 113 (03) : 1308 - 1324
  • [37] BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
    Vallejos, Catalina A.
    Marioni, John C.
    Richardson, Sylvia
    PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (06)
  • [38] Protocol for analysis of single-cell sequencing data by Seqtometry
    Kousnetsov, Robert
    Hawiger, Daniel
    STAR PROTOCOLS, 2024, 5 (03):
  • [39] Single-cell multiomics: technologies and data analysis methods
    Lee, Jeongwoo
    Hyeon, Do Young
    Hwang, Daehee
    EXPERIMENTAL AND MOLECULAR MEDICINE, 2020, 52 (09): : 1428 - 1442
  • [40] Computational methods for the integrative analysis of single-cell data
    Forcato, Mattia
    Romano, Oriana
    Bicciato, Silvio
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (03) : 20 - 29