It is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals. We compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples. REOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.

Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

The Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build “connection” between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelevant genes may blur the “connection” if all the genes are used instead of pre-selected DEGs induced by drug treatments. We applied OneComp, a customized version of RankComp, to identify DEGs in such small-scale cell line datasets. For a cell line, a list of gene pairs with stable relative expression orderings (REOs) were identified in a large collection of control cell samples measured in different experiments and they formed the background stable REOs. When applying OneComp to a small-scale cell line dataset, the background stable REOs were customized by filtering out the gene pairs with reversal REOs in the control samples of the analyzed dataset. In simulated data, the consistency scores of overlapping genes between DEGs identified by OneComp and SAM were all higher than 99%, while the consistency score of the DEGs solely identified by OneComp was 96.85% according to the observed expression difference method. The usefulness of OneComp was exemplified in drug repositioning by identifying phenformin and metformin related genes using small-scale cell line datasets which helped to support them as a potential anti-tumor drug for non-small-cell lung carcinoma, while the pattern-matching strategy adopted by CMAP missed the two connections. The implementation of OneComp is available at https://github.com/pathint/reoa . OneComp performed well in both the simulated and real data. It is useful in drug repositioning studies by helping to find hidden “connections” between drugs and diseases.

Background: Staphylococcus aureus, one of the most prevalent opportunistic pathogens, mainly colonizes the nasal cavity and is a risk factor for severe infections. Virulence factors and accessory gene regulator (agr) are key to the severity and diversity of staphylococcal infection. In this study, we aimed to characterise S. aureus agr-types and virulence genes and correlated them with genetic background and antibiotic-resistant phenotypes. Results: Agr types were identified in 704 isolates (98.5%), with only 11 isolates were negative for agr type. Most of our isolates were classified as agr type I, followed by types III, II and IV. The enterotoxin c gene (sec) was detected in 48.6% of isolates, showing the highest prevalence among the five enterotoxin genes detected. The positivity rates for the lukS/F-PV and tsst genes were 4% and 2.2%, respectively, while neither sed nor SasX were detected. ST45, ST59, ST338, ST188, ST6, ST7, ST22, ST25, ST398, and ST944 belonged to agr I group, while ST5 and ST15 belonged to agr II group. ST30 and ST1 were classified into agr III group, and ST121 was assigned into agr IV group. The tsst gene was found exclusively within agr I and III types belonging to ST7 and ST30 isolates, while the lukS/F-PV was predominantly carried by agr I type isolates primarily within CC59 and CC22 clones. Among the methicillin-resistant S. aureus (MRSA) isolates, 89.7% belonged to agr I group, and 97.8% of rifampicin-resistant or intermediate isolates were assigned to agr I group. MRSA isolates harboured more tested virulence genes compared to methicillin-susceptible S. aureus isolates. Conclusions: We characterized the distributions of agr types and eight major virulence genes of 715 S. aureus isolates, and our findings revealed clear associations between agr types and STs, as well as virulence genes, and drug resistant phenotypes.

Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and relative expression orderings (REOs) of gene pairs, is necessary. A total of 18 datasets were collected, comprising over 800 paired samples. These paired samples were utilized to assess the impact of pre-analytical variables on gene expression measurements and REOs, including sampling methods, tumor sample heterogeneity, fixed time delays, preservation conditions, degradation levels, library preparation kits, amplification kits, RNA quantity, measuring platforms, and laboratory sites at single and multi-variable level. Low-quality samples served as the case group, while paired high-quality samples constituted the control group. In both single and multiple variable analyses, comparing each case sample to paired control sample revealed thousands of genes exhibited a twofold change in expression values. In contrast, on average, 82% and 76% of gene pairs keep consistent REO pattern between paired samples in single-variable and multi-variable analyses, respectively. Notably, the rate steadily increased after excluding gene pairs with the closest expression levels. Statistical analyses shown a higher proportion of differentially expressed genes (DEGs) than that of reversed gene pairs between case and control groups in both single-variable and multi-variable analyses. Furthermore, the proportion of reversal gene pairs among all gene pairs involving DEGs remained below 20% in the majority of comparisons. Our research demonstrates that REOs exhibit higher robustness under the influence of pre-analytical variables. These findings indicate the potential of the REOs-based approach in transcriptomics research and its applicability for biomarker studies.

The amount of RNA per cell, namely the transcriptome size, may vary under many biological conditions including tumor. If the transcriptome size of two cells is different, direct comparison of the expression measurements on the same amount of total RNA for two samples can only identify genes with changes in the relative mRNA abundances, i.e., cellular mRNA concentration, rather than genes with changes in the absolute mRNA abundances. Our recently proposed RankCompV2 algorithm identify differentially expressed genes (DEGs) through comparing the relative expression orderings (REOs) of disease samples with that of normal samples. We reasoned that both the mRNA concentration and the absolute abundances of these DEGs must have changes in disease samples. In simulation experiments, this method showed excellent performance for identifying DEGs between normal and disease samples with different transcriptome sizes. Through analyzing data for ten cancer types, we found that a significantly higher proportion of the DEGs with absolute mRNA abundance changes overlapped or directly interacted with known cancer driver genes and anti-cancer drug targets than that of the DEGs only with mRNA concentration changes alone identified by the traditional methods. The DEGs with increased absolute mRNA abundances were enriched in DNA damage-related pathways, while DEGs with decreased absolute mRNA abundances were enriched in immune and metabolism associated pathways. Both the mRNA concentration and the absolute abundances of the DEGs identified through REOs comparison change in disease samples in comparison with normal samples. In cancers these genes might play more important upstream roles in carcinogenesis.