MOTIVATION: Single-cell and spatial transcriptomics provide unprecedented insight into diseases. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and discovery. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, preclinical research often requires in vivo experiments in non-human species, which makes utilizing such valuable resources difficult. To facilitate both human orthologous conversion of non-human transcriptomes and the application of pharmacotranscriptomic databases to pre-clinical research models, we introduce OrthologAL. OrthologAL interfaces with BioMart to access different gene sets from the Ensembl database, allowing for ortholog conversion without the need for user-generated code. RESULTS: Researchers can input their single-cell or other high-dimensional gene expression data from any species as a Seurat object, and OrthologAL will output a human ortholog-converted Seurat object for download and use. To demonstrate the utility of this application, we tested OrthologAL using single-cell, single-nuclei, and spatial transcriptomic data derived from common preclinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for the simple conversion of Seurat objects and for applying preclinical, high-dimensional transcriptomics data to functional human-derived small molecule predictions. AVAILABILITY AND IMPLEMENTATION: OrthologAL is available for download as an R package with functions to launch the Shiny GUI at https://github.com/AyadLab/OrthologAL or via Zenodo at https://doi.org/10.5281/zenodo.15225041. The medulloblastoma single-cell transcriptomics data were downloaded from the NCBI Gene Expression Omnibus with the identifier GSE129730. 10X Visium data of medulloblastoma PDX mouse models from Vo et al. were acquired by contacting the authors, and the raw data are available from ArrayExpress under the identifier E-MTAB-11720. The single-cell and single-nuclei transcriptomics data of rat and mouse spinal-cord injury were acquired from the Gene Expression Omnibus under the identifiers GSE213240 and GSE234774.

BACKGROUND: Quantification of protein expression by means of mass spectrometry (MS) has been introduced in various proteomics studies. In particular, two label-free quantification methods, such as spectral counting and spectra feature analysis have been extensively investigated in a wide variety of proteomic studies. The cornerstone of both methods is peptide identification based on a proteomic database search and subsequent estimation of peptide retention time. However, they often suffer from restrictive database search and inaccurate estimation of the liquid chromatography (LC) retention time. Furthermore, conventional peptide identification methods based on the spectral library search algorithms such as SEQUEST or SpectraST have been found to provide neither the best match nor high-scored matches. Lastly, these methods are limited in the sense that target peptides cannot be identified unless they have been previously generated and stored into the database or spectral libraries.To overcome these limitations, we propose a novel method, namely Quantification method based on Finding the Identical Spectral set for a Homogenous peptide (Q-FISH) to estimate the peptide's abundance from its tandem mass spectrometry (MS/MS) spectra through the direct comparison of experimental spectra. Intuitively, our Q-FISH method compares all possible pairs of experimental spectra in order to identify both known and novel proteins, significantly enhancing identification accuracy by grouping replicated spectra from the same peptide targets. RESULTS: We applied Q-FISH to Nano-LC-MS/MS data obtained from human hepatocellular carcinoma (HCC) and normal liver tissue samples to identify differentially expressed peptides between the normal and disease samples. For a total of 44,318 spectra obtained through MS/MS analysis, Q-FISH yielded 14,747 clusters. Among these, 5,777 clusters were identified only in the HCC sample, 6,648 clusters only in the normal tissue sample, and 2,323 clusters both in the HCC and normal tissue samples. While it will be interesting to investigate peptide clusters only found from one sample, further examined spectral clusters identified both in the HCC and normal samples since our goal is to identify and assess differentially expressed peptides quantitatively. The next step was to perform a beta-binomial test to isolate differentially expressed peptides between the HCC and normal tissue samples. This test resulted in 84 peptides with significantly differential spectral counts between the HCC and normal tissue samples. We independently identified 50 and 95 peptides by SEQUEST, of which 24 and 56 peptides, respectively, were found to be known biomarkers for the human liver cancer. Comparing Q-FISH and SEQUEST results, we found 22 of the differentially expressed 84 peptides by Q-FISH were also identified by SEQUEST. Remarkably, of these 22 peptides discovered both by Q-FISH and SEQUEST, 13 peptides are known for human liver cancer and the remaining 9 peptides are known to be associated with other cancers. CONCLUSIONS: We proposed a novel statistical method, Q-FISH, for accurately identifying protein species and simultaneously quantifying the expression levels of identified peptides from mass spectrometry data. Q-FISH analysis on human HCC and liver tissue samples identified many protein biomarkers that are highly relevant to HCC. Q-FISH can be a useful tool both for peptide identification and quantification on mass spectrometry data analysis. It may also prove to be more effective in discovering novel protein biomarkers than SEQUEST and other standard methods.

MOTIVATION: ChIP-chip has been widely used for various genome-wide biological investigations. Given the small number of replicates (typically two to three) per biological sample, methods of analysis that control the variance are desirable but in short supply. We propose a double error shrinkage (DES) method by using moving average statistics based on local-pooled error estimates which effectively control both heterogeneous error variances and correlation structures of an extremely large number of individual probes on tiling arrays. RESULTS: Applying DES to ChIP-chip tiling array study for discovering genome-wide protein-binding sites, we identified 8400 target regions that include highly likely TFIID binding sites. About 33% of these were well matched with the known transcription starting sites on the DBTSS library, while many other newly identified sites have a high chance to be real binding sites based on a high positive predictive value of DES. We also showed the superior performance of DES compared with other commonly used methods for detecting actual protein binding sites.

BACKGROUND: Microarray gene expression data are often analyzed together with corresponding physiological response and clinical metadata of biological subjects, e.g. patients' residual tumor sizes after chemotherapy or glucose levels at various stages of diabetic patients. Current clustering analysis cannot directly incorporate such quantitative metadata into the clustering heatmap of gene expression. It will be quite useful if these clinical response data can be effectively summarized in the high-dimensional clustering display so that important groups of genes can be intuitively discovered with different degrees of relevance to target disease phenotypes. RESULTS: We introduced a novel clustering analysis approach, response projected clustering (RPC), which uses a high-dimensional geometrical projection of response data to the gene expression space. The projected response vector, which becomes the origin in the projected space, is then clustered together with the projected gene vectors based on their different degrees of association with the response vector. A bootstrap-counting based RPC analysis is also performed to evaluate statistical tightness of identified gene clusters. Our RPC analysis was applied to the in vitro growth-inhibition and microarray profiling data on the NCI-60 cancer cell lines and the microarray gene expression study of macrophage differentiation in atherogenesis. These RPC applications enabled us to identify many known and novel gene factors and their potential pathway associations which are highly relevant to the drug's chemosensitivity activities and atherogenesis. CONCLUSION: We have shown that RPC can effectively discover gene networks with different degrees of association with clinical metadata. Performed on each gene's response projected vector based on its degree of association with the response data, RPC effectively summarizes individual genes' association with metadata as well as their own expression patterns. Thus, RPC greatly enhances the utility of clustering analysis on investigating high-dimensional microarray gene expression data with quantitative metadata.

A number of statistical approaches have been proposed for evaluating the statistical significance of a differential expression in microarray data. The error estimation of these approaches is inaccurate when the number of replicated arrays is small. Consequently, their resulting statistics are often underpowered to detect important differential expression patterns in the microarray data with limited replication. In this paper, we propose an empirical Bayes (EB) heterogeneous error model (HEM) with error-pooling prior specifications for varying technical and biological errors in the microarray data. The error estimation of HEM is thus strengthened by and shrunk toward the EB priors that are obtained by the error-pooling estimation at each local intensity range. By using simulated and real data sets, we compared HEM with two widely used statistical approaches, significance analysis of microarray (SAM) and analysis of variance (ANOVA), to identify differential expression patterns across multiple conditions. The comparison showed that HEM is statistically more powerful than SAM and ANOVA, particularly when the sample size is smaller than five. We also suggest a resampling-based estimation of Bayesian false discovery rate to provide a biologically relevant cutoff criterion of HEM statistics.

BACKGROUND: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. RESULTS: Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CST-based BN algorithm tends to be less than that of the original BN. CONCLUSION: The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms.

We greatly thank the authors of this letter for pointing out the significance of our original contribution of the hierarchical error model (HEM) in Cho and Lee (2004). As the authors suggested, we agree that an extension of HEM can be made for gene expression data with biological and/or experimental correlations. However, we here discuss several issues in response to some of the points raised in this letter. First, in this letter the simplified posterior distributions were derived under the assumption of the same numbers of biological and experimental replicates for all conditions, i.e. mij = m and nijk = n. However, in practical microarray studies, the numbers of replicates can often differ among different conditions (e.g. mi1 = 5 and mi2 = 6). Thus, in our original HEM model we considered this kind of heterogeneous sample sizes among different combinations of genes and conditions. Furthermore, expression values can be completely missing for a certain combination of gene and condition, i.e. nijk = 0, owing to quality control and other experimental reasons. In this case the posterior probability of xijk has to be defined with a reduced form xijk∣.∼N(μ+gi+cj+rij,σbij2)⁠, which is a logical imputation for the missing combination from the available information of the corresponding gene and condition. Thus, we respectfully argue that our simplified form of Equation (6) is appropriate for the aforementioned general cases. Also, we note that the case that nijk = 1 for all i, j, k was presented in the section entitled ‘Hierarchical error model with no replicates’ in our original paper. Second, as pointed out in this letter, parameter Λ must be replaced with Λ, ‥, Λi., and Λj for μ, gi and cj, respectively. The errors, however, occurred while we attempted to simplify notations in our paper, and it was irrelevant to claim a mistake in our actual derivation and implementation of the corresponding posterior distributions. For example, our HEM methodology was a further extension from a previous study in Lee et al. (2001), in which similar expressions have been shown without such simplification. Therefore, all the results in Cho and Lee (2004) were obtained from the correct posterior distributions, which can be performed by our software HEM available at the BioConductor website (Author Webpage). Third, we again completely agree that the extension of this letter addresses the important issue of biological and experimental correlations in practical microarray studies. However, we cautiously point out that it may be extremely difficult to precisely estimate all the numerous correlation parameters from practical microarray data, which are often based on a small sample size, e.g. duplicates or triplicates per condition. Finally, we would like to mention that we have recently further refined our HEM methodology by precisely estimating heterogeneous errors based on an error-pooling-based empirical Bayes prior specification (under review); this refined algorithm has also been added to our HEM package at the Bioconductor website. In summary, as the authors in this letter pointed out, we strongly believe that HEM methodology is quite useful in microarray data analysis by carefully incorporating complex error structures of such complex, high-throughput biological data, and that further extensions and refinements such as the ones proposed in this letter and our empirical Bayes approach will greatly enhance the utility of this technology.

BACKGROUND: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustment methods for multiple comparisons such as family-wise error rate (FWER) have been found to be too conservative in analyzing large-screening microarray data, and the False Discovery Rate (FDR), the expected proportion of false positives among all positives, has been recently suggested as an alternative for controlling false positives. Several statistical approaches have been used to estimate and control FDR, but these may not provide reliable FDR estimation when applied to microarray data sets with a small number of replicates. RESULTS: We propose a rank-invariant resampling (RIR) based approach to FDR evaluation. Our proposed method generates a biologically relevant null distribution, which maintains similar variability to observed microarray data. We compare the performance of our RIR-based FDR estimation with that of four other popular methods. Our approach outperforms the other methods both in simulated and real microarray data. CONCLUSION: We found that the SAM's random shuffling and SPLOSH approaches were liberal and the other two theoretical methods were too conservative while our RIR approach provided more accurate FDR estimation than the other approaches.

MOTIVATION: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY: The software for the HEM is available from the authors upon request.

MOTIVATION: In microarray studies gene discovery based on fold-change values is often misleading because error variability for each gene is heterogeneous under different biological conditions and intensity ranges. Several statistical testing methods for differential gene expression have been suggested, but some of these approaches are underpowered and result in high false positive rates because within-gene variance estimates are based on a small number of replicated arrays. RESULTS: We propose to use local-pooled-error (LPE) estimates and robust statistical tests for evaluating significance of each gene's differential expression. Our LPE estimation is based on pooling errors within genes and between replicate arrays for genes in which expression values are similar. We have applied our LPE method to compare gene expression in naïve and activated CD8+ T-cells. Our results show that the LPE method effectively identifies significant differential-expression patterns with a small number of replicated arrays. AVAILABILITY: The methodology is implemented with S-PLUS and R functions available at http://hesweb1.med.virginia.edu/bioinformatics

We have studied the evolutionary stages of pure e-tailers, click & mortar (C&M) and brick & mortar (B&M) retailers for three points of time: June 1999, June 2000, and June 2001. To evaluate the dynamic stages of e-tailing business as an innovative venture, we propose four stages: exploration, breakeven, growth, and maturity. The stages are measured by the impact of revenue and income on the firm value, and a regression model is adopted to formulate the model. To empirically examine the stages of e-tailers and retailers, we have collected 14 e-tailers, 112 C&M, and 75 B&M from the U.S. stock markets. According to this study, the proposed stage model explains the evolution of pure e-tailers very meaningfully. E-tailers were in the late exploration stage in 1999, breakeven stage in 2000, and growth stage in 2001. Unlike our hypothetical expectation, the stage model could not adequately explain the effect of online business to C&M. In this regard, the impact of online channel to traditional retailers was not revolutionary. In 1999 and 2000, the primary contributing factor to firm value of C&M was income, but in 2001, it was revenue. According to this result, investors were very conservative to the risky investment on the click business of traditional retailers. However, it turned out that C&M has performed better than B&M in terms of revenue, income, income/revenue, stock price, and market capitalization. It is noteworthy that the revenue effect of C&M in 2001 was significantly higher than that of B&M.