Tissue differences revealed by gene expression profiles of various cell lines

Abstract

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue‐specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue‐specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue‐specific genes identified by the Genotype‐tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.

Identifying Cell-Type Specific Genes and Expression Rules Based on Single-Cell Transcriptomic Atlas Data

“…Different from mRMR method, MCFS (Draminski et al, 2008;Cai et al, 2018;Li et al, 2018;Chen et al, 2019) method evaluates the importance of features in a completely different way. This method is based on decision trees.…”

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

“…After the irrelevant features were removed, the relevant methylation and expression features were ranked based on their importance evaluated with MCFS (Monte Carlo Feature Selection) (Draminski et al, 2008). The MCFS was a widely used method to rank features based on classification trees (Chen et al, , 2019Pan et al, 2018Pan et al, , 2019aLi et al, 2019). First, for the d features, we selected s subsets and each subset included m features (m was much smaller than d).…”

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

“…The second stage was to determine the number of selected genes using the IFS method (Chen et al, 2018b;Chen et al, 2019b;Chen et al, 2019c;Chen et al, 2019d;Chen et al, 2019f;Li et al, 2019a;Pan et al, 2019a;Pan et al, 2019b;). To do so, 200 classifiers were constructed using top 1, top 2, top 200 genes.…”

“…We tried several different classifiers: (1) SVM (Support Vector Machine) (Jiang et al, 2019;Yan et al, 2019;Chen et al, 2019a;Li et al, 2019a;Pan et al, 2019a;Wang and Huang, 2019b;Chen et al, 2019d), (2) 1NN (1 Nearest Neighbor) (Lei et al, 2013;Chen et al, 2016;Wang et al, 2017a), (3) 3NN (3 Nearest Neighbors), (4) 5NN (5 Nearest Neighbors), (5) Decision Tree (DT) (Huang et al, 2008;Huang et al, 2011;Chen et al, 2015), (6) Neural Network (NN) (Liu et al, 2017;Pan et al, 2018;Chen et al, 2019e). The function svm from R package e1071, function knn from R package class, function rpart from R package rpart, function nnet from R package nnet were used to apply these classification algorithms.…”