Biomedical subjects

Arief Gusnanto

Publications and source records attributed to Arief Gusnanto.

9 recordsLinked to original sources

PubMed2026

Sparse Logistic Regression on Genomic Data for Prediction of Tumour Pathological Subtype.

The correct prediction of tumour subtype is critical for the treatment of cancer patients to maximise the chance of survival. The patients' genomic information, such as copy number alterations (CNA) profile, has increasingly become an important factor in the prediction to supplement the traditional pathological subtyping. The incorporation of the CNA information in a prediction model, such as logistic regression, faces two major statistical challenges: first, how to estimate the model parameters in the thousands and, second, how to deal with the correlation of CNA between genomic regions. To address them, we propose a sparse logistic regression model with random effects where some of its parameters are estimated to zero while the other parameters are non-zero. In effect, a variable selection is embedded in the modelling. To deal with the correlation of CNA across genomic regions, we extend further the model to incorporate an additional penalty in the corresponding likelihood function in the logistic regression. The results show that we can identify selected genomic regions that are informative to distinguish different tumour subtypes, while giving a good prediction ability. We illustrate the methodology using CNA dataset from a lung cancer cohort.

Journal Article↗

PubMed2023

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Humans↗

PubMed2006

Comparative gene expression profiling of in vitro differentiated megakaryocytes and erythroblasts identifies novel activatory and inhibitory platelet membrane proteins.

To identify previously unknown platelet receptors we compared the transcriptomes of in vitro differentiated megakaryocytes (MKs) and erythroblasts (EBs). RNA was obtained from purified, biologically paired MK and EB cultures and compared using cDNA microarrays. Bioinformatical analysis of MK-up-regulated genes identified 151 transcripts encoding transmembrane domain-containing proteins. Although many of these were known platelet genes, a number of previously unidentified or poorly characterized transcripts were also detected. Many of these transcripts, including G6b, G6f, LRRC32, LAT2, and the G protein-coupled receptor SUCNR1, encode proteins with structural features or functions that suggest they may be involved in the modulation of platelet function. Immunoblotting on platelets confirmed the presence of the encoded proteins, and flow cytometric analysis confirmed the expression of G6b, G6f, and LRRC32 on the surface of platelets. Through comparative analysis of expression in platelets and other blood cells we demonstrated that G6b, G6f, and LRRC32 are restricted to the platelet lineage, whereas LAT2 and SUCNR1 were also detected in other blood cells. The identification of the succinate receptor SUCNR1 in platelets is of particular interest, because physiologically relevant concentrations of succinate were shown to potentiate the effect of low doses of a variety of platelet agonists.

Cell Differentiation↗

PubMed2006

Detecting multiple associations in genome-wide studies.

Recent developments in the statistical analysis of genome-wide studies are reviewed. Genome-wide analyses are becoming increasingly common in areas such as scans for disease-associated markers and gene expression profiling. The data generated by these studies present new problems for statistical analysis, owing to the large number of hypothesis tests, comparatively small sample size and modest number of true gene effects. In this review, strategies are described for optimising the genotyping cost by discarding promising genes at an earlier stage, saving resources for the genes that show a trend of association. In addition, there is a review of new methods of analysis that combine evidence across genes to increase sensitivity to multiple true associations in the presence of many non-associated genes. Some methods achieve this by including only the most significant results, whereas others model the overall distribution of results as a mixture of distributions from true and null effects. Because genes are correlated even when having no effect, permutation testing is often necessary to estimate the overall significance, but this can be very time consuming. Efficiency can be improved by fitting a parametric distribution to permutation replicates, which can be re-used in subsequent analyses. Methods are also available to generate random draws from the permutation distribution. The review also includes discussion of new error measures that give a more reasonable interpretation of genome-wide studies, together with improved sensitivity. The false discovery rate allows a controlled proportion of positive results to be false, while detecting more true positives; and the local false discovery rate and false-positive report probability give clarity on whether or not a statistically significant test represents a real discovery.

Genetic Techniques↗

PubMed2005

Multidimensional local false discovery rate for microarray studies.

MOTIVATION: The false discovery rate (fdr) is a key tool for statistical assessment of differential expression (DE) in microarray studies. Overall control of the fdr alone, however, is not sufficient to address the problem of genes with small variance, which generally suffer from a disproportionally high rate of false positives. It is desirable to have an fdr-controlling procedure that automatically accounts for gene variability. METHODS: We generalize the local fdr as a function of multiple statistics, combining a common test statistic for assessing DE with its standard error information. We use a non-parametric mixture model for DE and non-DE genes to describe the observed multi-dimensional statistics, and estimate the distribution for non-DE genes via the permutation method. We demonstrate this fdr2d approach for simulated and real microarray data. RESULTS: The fdr2d allows objective assessment of DE as a function of gene variability. We also show that the fdr2d performs better than commonly used modified test statistics. AVAILABILITY: An R-package OCplus containing functions for computing fdr2d() and other operating characteristics of microarray data is available at http://www.meb.ki.se/~yudpaw.

Algorithms↗

PubMed2005

Fold-change estimation of differentially expressed genes using mixture mixed-model.

Microarray experiments produce expression measurements for thousands of genes simultaneously, though usually for a small number of RNA samples. The most common problem is the identification of genes that are differentially expressed between different groups of samples or biological conditions. As the number of genes far exceeds the number of RNA samples, the inherent multiplicity poses a severe problem in both hypothesis testing and effect estimation. While much of the recent literature is focused on the hypothesis aspects, we concentrate in this paper on effect estimation as a tool for the identification of differentially expressed genes. We propose a linear mixed model where the random effects are assumed to follow a mixture distribution, and study in detail the case of three normals, corresponding to genes that are down-, up- or non regulated. Our approach leads to a new type of non-linear shrinkage estimation, where a proportion of estimates is shrunk to zero, while the rest follows standard linear shrinkage. This allows us to estimate the log fold-change of the genes involved and to identify those that are differentially expressed within the same model framework. We investigate the operating characteristics of our method using simulation and spike-in studies, and illustrate its application to real data using a breast-cancer dataset.

Journal Article↗

PubMed2005

False discovery rate, sensitivity and sample size for microarray studies.

MOTIVATION: In microarray data studies most researchers are keenly aware of the potentially high rate of false positives and the need to control it. One key statistical shift is the move away from the well-known P-value to false discovery rate (FDR). Less discussion perhaps has been spent on the sensitivity or the associated false negative rate (FNR). The purpose of this paper is to explain in simple ways why the shift from P-value to FDR for statistical assessment of microarray data is necessary, to elucidate the determining factors of FDR and, for a two-sample comparative study, to discuss its control via sample size at the design stage. RESULTS: We use a mixture model, involving differentially expressed (DE) and non-DE genes, that captures the most common problem of finding DE genes. Factors determining FDR are (1) the proportion of truly differentially expressed genes, (2) the distribution of the true differences, (3) measurement variability and (4) sample size. Many current small microarray studies are plagued with large FDR, but controlling FDR alone can lead to unacceptably large FNR. In evaluating a design of a microarray study, sensitivity or FNR curves should be computed routinely together with FDR curves. Under certain assumptions, the FDR and FNR curves coincide, thus simplifying the choice of sample size for controlling the FDR and FNR jointly.

Algorithms↗

PubMed2005

Platelet genomics and proteomics in human health and disease.

Proteomic and genomic technologies provide powerful tools for characterizing the multitude of events that occur in the anucleate platelet. These technologies are beginning to define the complete platelet transcriptome and proteome as well as the protein-protein interactions critical for platelet function. The integration of these results provides the opportunity to identify those proteins involved in discrete facets of platelet function. Here we summarize the findings of platelet proteome and transcriptome studies and their application to diseases of platelet function.

Blood Platelets↗

PubMed2004

A DNA microarray for fission yeast: minimal changes in global gene expression after temperature shift.

Completion of the fission yeast genome sequence has opened up possibilities for post-genomic approaches. We have constructed a DNA microarray for genome-wide gene expression analysis in fission yeast. The microarray contains DNA fragments, PCR-amplified from a genomic DNA template, that represent > 99% of the 5000 or so annotated fission yeast genes, as well as a number of control sequences. The GenomePRIDE software used attempts to design similarly sized DNA fragments corresponding to gene regions within single exons, near the 3'-end of genes that lack homology to other fission yeast genes. To validate the design and utility of the array, we studied expression changes after a 2 h temperature shift from 25 degrees C to 36 degrees C, conditions widely used when studying temperature-sensitive mutants. Obligingly, the vast majority of genes do not change more than two-fold, supporting the widely held view that temperature-shift experiments specifically reveal phenotypes associated with temperature-sensitive mutants. However, we did identify a small group of genes that showed a reproducible change in expression. Importantly, most of these corresponded to previously characterized heat-shock genes, whose expression has been reported to change after more extreme temperature shifts than those used here. We conclude that the DNA microarray represents a useful resource for fission yeast researchers as well as the broader yeast community, since it will facilitate comparison with the distantly related budding yeast, Saccharomyces cerevisiae. To maximize the utility of this resource, the array and its component parts are fully described in On-line Supplementary Information and are also available commercially.

DNA, Fungal↗