8,836 research outputs found
Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data
Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation
On bicluster aggregation and its benefits for enumerative solutions
Biclustering involves the simultaneous clustering of objects and their
attributes, thus defining local two-way clustering models. Recently, efficient
algorithms were conceived to enumerate all biclusters in real-valued datasets.
In this case, the solution composes a complete set of maximal and non-redundant
biclusters. However, the ability to enumerate biclusters revealed a challenging
scenario: in noisy datasets, each true bicluster may become highly fragmented
and with a high degree of overlapping. It prevents a direct analysis of the
obtained results. To revert the fragmentation, we propose here two approaches
for properly aggregating the whole set of enumerated biclusters: one based on
single linkage and the other directly exploring the rate of overlapping. Both
proposals were compared with each other and with the actual state-of-the-art in
several experiments, and they not only significantly reduced the number of
biclusters but also consistently increased the quality of the solution.Comment: 15 pages, will be published by Springer Verlag in the LNAI Series in
the book Advances in Data Minin
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Can graph-cutting improve microarray gene expression reconstructions?
Microarrays produce high-resolution image data that are, unfortunately, permeated with a great deal of ânoiseâ that must be removed for precision purposes. This paper presents a technique for such a removal process. On completion of this non-trivial task, a new surface (devoid of gene spots) is subtracted from the original to render more precise gene expressions. The graph-cutting technique as implemented has the benefits that only the most appropriate pixels are replaced and these replacements are replicates rather than estimates. This means the influence of outliers and other artifacts are handled more appropriately (than in previous methods) as well as the variability of the final gene expressions being considerably reduced. Experiments are carried out to test the technique against commercial and previously researched reconstruction methods. Final results show that the graph-cutting inspired identification mechanism has a positive significant impact on reconstruction accuracy
Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays
Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity
Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm
Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear
time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein
profiles from biological samples with the aim of discovering biomarkers for
disease. However, the raw protein profiles suffer from several sources of bias
or systematic variation which need to be removed via pre-processing before
meaningful downstream analysis of the data can be undertaken. Baseline
subtraction, an early pre-processing step that removes the non-peptide signal
from the spectra, is complicated by the following: (i) each spectrum has, on
average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and
(ii) the time-consuming and error-prone trial-and-error process for optimising
the baseline subtraction input arguments. With reference to the aforementioned
complications, we present an automated pipeline that includes (i) a novel
`continuous' line segment algorithm that efficiently operates over data with a
transformed m/z-axis to remove the relationship between peptide mass and peak
width, and (ii) an input-free algorithm to estimate peak widths on the
transformed m/z scale. The automated baseline subtraction method was deployed
on six publicly available proteomic MS datasets using six different m/z-axis
transformations. Optimality of the automated baseline subtraction pipeline was
assessed quantitatively using the mean absolute scaled error (MASE) when
compared to a gold-standard baseline subtracted signal. Near-optimal baseline
subtraction was achieved using the automated pipeline. The advantages of the
proposed pipeline include informed and data specific input arguments for
baseline subtraction methods, the avoidance of time-intensive and subjective
piecewise baseline subtraction, and the ability to automate baseline
subtraction completely. Moreover, individual steps can be adopted as
stand-alone routines.Comment: 50 pages, 19 figure
A comparative study of different strategies of batch effect removal in microarray data: a case study of three datasets
Batch effects refer to the systematic non-biological variability that is introduced by experimental design and sample processing in microarray experiments. It is a common issue in microarray data and could introduce bias into the analysis, if ignored. Many batch effect removal methods have been developed. Previous comparative work has been focused on their effectiveness of batch effects removal and impact on downstream classification analysis. The most common type of analysis for microarray data is differential expression (DE) analysis, yet no study has examined the impact of these methods on downstream DE analysis, which identifies markers that are significantly associated with the outcome of interest. In this project, we investigated the performance of five popular batch effect removal methods, mean-centering, ComBat_p, ComBat_n, SVA, and ratio based methods, on batch effects reduction and their impact on DE analysis using three experimental datasets with different sources of batch effects. We found that the performance of these methods is data-dependent: simple mean-centering method performed reasonably well in all three datasets, but the more complicated algorithms such as ComBat methodâs performance could be unstable for certain dataset and should be applied with caution. Given a new dataset, we recommend either using the mean-centering method or carefully investigating a few different batch removal methods and choosing the one that is the best for the data, if possible. This study has important public health significance because better handling of batch effect in microarray data can reduce biased results and lead to improved biomarker identification
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data
Inferring gene regulatory networks is an important problem in systems
biology. However, these networks can be hard to infer from experimental data
because of the inherent variability in biological data as well as the large
number of genes involved. We propose a fast, simple method for inferring
regulatory relationships between genes from knockdown experiments in the NIH
LINCS dataset by calculating posterior probabilities, incorporating prior
information. We show that the method is able to find previously identified
edges from TRANSFAC and JASPAR and discuss the merits and limitations of this
approach
BMICA-independent component analysis based on B-spline mutual information estimator
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA
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