A graph-based multi-sample test for identifying pathways associated with cancer progression

Zhang, Qingyang; Mahdi, Ghadeer Jasim Mohammed; Tinker, Jian; Chen, Hao

doi:10.1016/j.compbiolchem.2020.107285

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…ThenMCMC is used 9 . There are three popular MCMC sampling techniques, such as Metropolis-Hastings, slice sampling 10 , and Gibbs sampling 11 .MCMC methods are derived from a Monte Carlo (MC) 12 . A chain is used to approximate samples of desired distribution in MCMC, and the approximation is generallyimproved after several stepshave been done 13 .…”

Section: Markov Chain Monte Carlomentioning

confidence: 99%

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

Mahdi,

Salih

2022

Baghdad Sci.J

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Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.

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Section: Markov Chain Monte Carlomentioning

confidence: 99%

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

Mahdi,

Salih

2022

Baghdad Sci.J

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“…It can be shown that Σ W is always invertible [30]. For Σ B and Σ A , it is difficult to check their invertibility theoretically.…”

Section: New Test Statisticsmentioning

confidence: 99%

“…The proof of Theorem 3.1 extends the method in [7]. The asymptotic distribution of S W is also studied in [30], but in this paper we take a different approach in that we first prove the asymptotic distribution of S A and then utilize the property of the multivariate normal distribution and quadratic forms in singular normal variables studied in [28].…”

Section: Remark 32mentioning

confidence: 99%

“…• Identification of genetic pathways : A simultaneous analysis of a genetic pathway might provide clear insight into cause of the phenotypic changes [11,19]. For example, the identification of important genetic pathways that drive the cancer progression may provide insights into the understanding of molecular mechanism of cancer progression [30]. Here, each observation is gene expression and the K-samples are development stages of cancer.…”

Section: Introductionmentioning

confidence: 99%

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New graph-based multi-sample tests for high-dimensional and non-Euclidean data

Song¹,

Chen²

2022

Preprint

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Testing the equality in distributions of multiple samples is a common task in many fields. However, this problem for high-dimensional or non-Euclidean data has not been well explored. In this paper, we propose new nonparametric tests based on a similarity graph constructed on the pooled observations from multiple samples, and make use of both within-sample edges and between-sample edges, a straightforward but yet not explored idea. The new tests exhibit substantial power improvements over existing tests for a wide range of alternatives. We also study the asymptotic distributions of the test statistics, offering easy off-the-shelf tools for large datasets. The new tests are illustrated through an analysis of the age image dataset.Preprint. Under review.

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“…The low number of observations does not allow much flexibility for supervised methods, given the need to split the dataset into training and testing parts. The raw dataset was processed by following the following steps, based on the original paper [22];…”

Section: Leukemia Datasetsmentioning

confidence: 99%

Enhanced Supervised Principal Component Analysis for Cancer Classification

Mahdi

Kalaf

Khaleel

2021

eijs

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In this paper, a new hybridization of supervised principal component analysis (SPCA) and stochastic gradient descent techniques is proposed, and called as SGD-SPCA, for real large datasets that have a small number of samples in high dimensional space. SGD-SPCA is proposed to become an important tool that can be used to diagnose and treat cancer accurately. When we have large datasets that require many parameters, SGD-SPCA is an excellent method, and it can easily update the parameters when a new observation shows up. Two cancer datasets are used, the first is for Leukemia and the second is for small round blue cell tumors. Also, simulation datasets are used to compare principal component analysis (PCA), SPCA, and SGD-SPCA. The results show that SGD-SPCA is more efficient than other existing methods.

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A graph-based multi-sample test for identifying pathways associated with cancer progression

Cited by 8 publications

References 36 publications

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

New graph-based multi-sample tests for high-dimensional and non-Euclidean data

Enhanced Supervised Principal Component Analysis for Cancer Classification

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