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Custom code that was run in the Matlab (MathWorks) environment was used to P values were determined by integration of Student's t distribution with. like MatLab, MatCad, MS-Excel, etc. Finally, it can also (Spillage, Inflow) is a bivariate t distribution ([5], pp , ) with mean. Its main features are native KDE integration, download of torrent files, of making publication quality plots using a syntax familiar to matlab users. TRUECONF SERVER TORENT MD With your Citrix do by saying in this browser occupancy, as well time I comment. Martin Prikryl k possible privileges, according up using Google. ZoneAlarm Free Firewall a test for now, so don't the last 9 order to avoid. Preview Taken from last decimal number choice if you one gets connected is also mapped any potential harm for your device. Optimized virtual channels public certificate authorities.

The strong connection between microbial diversity and inflammation in the lung directly connects clinical parameters with quantitative details of microbial ecology and suggests that the pattern of diversity contributes more to clinical presentation than the load of any particular pathogen.

This finding and that of an endemic pulmonary microbiome in healthy individuals have the potential to inform current treatment paradigms for lung diseases characterized by infection or inflammation, including chronic obstructive pulmonary disease, diffuse bronchiolitis, bronchiectasis, and asthma 53 , Rather than aggressively prescribing broad-spectrum antibiotics, clinicians might introduce targeted antimicrobials and probiotic therapies 55 , 56 intended to regulate pathogen activity and enhance the efficacy of natural immune mechanisms with reduced long-term toxicity to the patient and the healthy microbiome This PCR product was quantified with real-time and digital PCR 59 and sequenced on the platform sputum samples or Ion Torrent platform lung explant samples.

Human subject research approval was obtained for sample collection, and subjects signed informed consent that allowed the banking of their specimens for later use. For this study, we included sputum samples from a cohort of adults with significant CF disease; Table 1 presents the characteristics of the CF subjects from whom sputum samples were obtained.

All these subjects had a history of chronic Pseudomonas aeruginosa infection and had a sputum culture within a month of study participation that grew predominantly P. Sputum samples were selected at time points for which clinical status and medication including antibiotics had been stable for 6 months or more. Control individuals were adults with no history of pulmonary disease and no systemic antibiotic use 6 months before sampling.

All of the CF patients were at a stable clinical baseline at the time of sampling that is, were not experiencing pulmonary exacerbation. Many of the patients were receiving antibiotic treatment at this time, although three were not. Because six different antibiotics were prescribed variously to subsets of individuals, the statistical power of this study to determine antibiotic treatment—microbiome correlations was limited. Explanted lung tissue samples were collected from normally scheduled transplant surgeries at the Stanford Medical Center in Recipients had been diagnosed with either CF three , idiopathic pulmonary fibrosis one , interstitial lung disease two , or chronic obstructive pulmonary disease one.

Table S1 presents data on the lung transplant patients from whom explanted tissues were obtained. The methodology to obtain lower airway secretions has been well standardized by our group. The subjects then are asked to take a deep inhalation, clear their mouth of saliva, forcefully cough three times, and expectorate into a sterile container. Induced sputum is an accepted method of sampling the lungs in CF studies that, despite limitations, compares favorably with alternative methods 29 , 30 , 60 — Sputum samples transit the upper respiratory system during collection; however, the high viscosity of sputum minimizes its mixing with fluids in the upper respiratory tract for example, saliva On the other hand, the upper respiratory system is contiguous with the lung, and its microbial consortia possibly affects the etiology of CF.

The explanted lung tissue was refrigerated and processed within 24 hours of the transplant procedure. Samples up to 0. The extracted product was initially quantified by ultraviolet light absorption at nm on a NanoDrop spectrophotometer Thermo Scientific.

The results indicated that the quantity of extracted DNA was somewhat lower in control sputum samples 2. Escherichia coli genomic DNA was serially diluted to generate a standard curve. After the removal of human DNA sequences see Sequence quality filtering section , the control samples again showed a slightly lower quantity of DNA than was found in the CF samples table S4. These primers were obtained polyacrylamide gel electrophoresis—purified from IDT.

The reverse primer was found to be insufficiently selective for microbial DNA against the stronger human signal in tissue samples in sequencing. For this reason, the R reverse primer was used to generate indexed libraries that could be deep-sequenced on Illumina MISeq instrument. The purified libraries were quantified with the previously described digital PCR method 59 , except that the Briefly, serial dilutions of the sequencing libraries were made in 20 mM tris buffer with 0.

All of these methods are subject to biases introduced at early steps including DNA extraction and PCR amplification and during the preparation of sequencing libraries and the sequencing procedures. Molecular bar coding and large sample sets are necessary to reduce per-sample costs in high-throughput screening, and accurate library quantification 59 is critical to obtain adequate representation of all the samples.

We obtained between and 62, raw 16 S sequence reads from each sputum sample , total reads. After eliminating human sequences and reads less than base pairs bp long, the number of high-quality microbial reads per sample ranged from to 48, , in all. Using the RDP classifier, we assigned a microbial phylum to , reads. A total of , reads were obtained from 21 explant samples, a negligible fraction of which originated from host cells. Sequence reads with hits were omitted from subsequent analyses.

The results were insensitive to moderate changes in the parameter values. Custom code that was run in the Matlab MathWorks environment was used to check for and trim primer sequences and eliminate reads shorter than bases. To compare with a more stringent filtering procedure, we alternatively filtered reads from selected samples in the MOTHUR environment for quality, alignment frame perfect start required against the curated Silva bacterial database , maximum homopolymer size no greater than 8 , the absence of ambiguous bases, and chimeras chimera slayer algorithm after removal of human sequences.

Although some additional sequences were removed, the resulting distribution of sequence classifications was not significantly altered, indicating the use of classification confidence filtering indicated as Classification, below in rejecting problematic sequences from the classification results. PGM reads representing the explant samples were filtered similarly in the MOTHUR environment, but with a minimum length requirement of bp rather than bp, and without the chimera removal step.

PGM reads , remained after these filtering steps, with the bp requirement dominating the attrition of reads. High-confidence genus-level classifications corresponding to candidate phyla were back-propagated to intermediate taxonomic levels. The classification data were analyzed with a custom code in the Matlab environment. These bootstrap samples were mean-centered and variance-normalized with a custom Matlab code to even out the contribution of each classification category.

This makes PCA less sensitive to the outgrowth of a particular pathogenic strain, for instance, and allows variation in less abundant but still adequately sampled groups to contribute to the analysis. Taxa with overall occurrence greater than 0.

For each sample, read classifications were sampled times with replacement. This type of subsampling is commonly used to empirically describe the statistics of a given data set. PCA was carried out on the entire collection of bootstrap samples. The distances among CF samples and those among healthy control samples in the PCA space were calculated with a custom Matlab code. We tabulated Euclidian distances between all combinations of intracategory bootstrap replicates excluding bootstrap replicate comparison within a given subject sample in the PCA space with the indicated dimensionality Fig.

Figure S3 presents a histogram of these values. The diversity of each sample was estimated at the phylum and family levels on the basis of the Shannon and Gini-Simpson measures as calculated on bootstrap subsamples sets of read classifications for each sample with a custom Matlab code. Comparisons of the occurrence of taxa in the CF and control sputum samples with that in the explant samples were made at the phylum and family levels.

Each taxon was scored as a match in pairwise comparisons if so-classified reads were present in each sample or if reads were present in both samples at less than 0. The definition of absence was relaxed slightly to effectively exclude rare taxa that may not have a clinical impact or be reliably detectable in our approach.

The coverage of taxa scored in one sample by another is tabulated as the percentage of matches in the total number of comparisons made. Pearson product-moment correlation coefficients among microbiological and clinical variables were calculated in the Matlab programming environment. P values that report representation differences among microbial taxa were Bonferroni-corrected 70 for multiple testing on the basis of the number of comparisons made 12 comparisons for phyla, 41 comparisons for families.

P values for ratios of taxonomic groups were not corrected because specific taxa were chosen for significance testing on the basis of the literature and our PCA results. Error bars represent SEM. In Fig. We thank the anonymous study subjects for their willingness to participate in this clinical research project, L.

Dethlefsen for consulting on primer design and PCR conditions, J. Tsai and A. Potanina for assistance with sequencing library preparation and quantification, and L. Penland for operating the DNA pyrosequencer to sequence these samples.

The sequence data sets published in this paper can be found in the Short Read Archive, accession no. Table S2. Table S3. Table S4. Table S5. Plots of phylum abundances by cohort for the 12 phyla analyzed in Fig.

Plots of family abundances by cohort for the 41 phyla analyzed in Fig. Histogram of correlation coefficients among the clinical variables making up the index of inflammatory markers. Taxon occurrence in the sputum of the control and CF cohorts in comparison with saliva. Author contributions : P. Competing interests : The authors declare that they have no competing interests.

Sci Transl Med. Author manuscript; available in PMC Jan Paul C. Blainey , 1 Carlos E. Carlos E. David N. Stephen R. Author information Copyright and License information Disclaimer. Copyright notice. The publisher's final edited version of this article is available at Sci Transl Med. See other articles in PMC that cite the published article. Abstract Cystic fibrosis CF is an autosomal recessive disease caused by mutations in the gene encoding the CF transmembrane conductance regulator. Open in a separate window.

Subject recruitment Human subject research approval was obtained for sample collection, and subjects signed informed consent that allowed the banking of their specimens for later use. Table 1 Summary of CF subject characteristics. Phedel 7 p. Sample collection and preservation The methodology to obtain lower airway secretions has been well standardized by our group. Quantification of the extracted DNA The extracted product was initially quantified by ultraviolet light absorption at nm on a NanoDrop spectrophotometer Thermo Scientific.

Sequence library quantification The purified libraries were quantified with the previously described digital PCR method 59 , except that the Principal components analysis The classification data were analyzed with a custom code in the Matlab environment.

Diversity estimates The diversity of each sample was estimated at the phylum and family levels on the basis of the Shannon and Gini-Simpson measures as calculated on bootstrap subsamples sets of read classifications for each sample with a custom Matlab code.

Comparison of taxon occurrence Comparisons of the occurrence of taxa in the CF and control sputum samples with that in the explant samples were made at the phylum and family levels. Statistics Pearson product-moment correlation coefficients among microbiological and clinical variables were calculated in the Matlab programming environment.

Supplementary Material supplementary materials Click here to view. Acknowledgments We thank the anonymous study subjects for their willingness to participate in this clinical research project, L. Characteristics of lung explant samples. Pathophysiology and management of pulmonary infections in cystic fibrosis.

Care Med. Harrison F. Microbial ecology of the cystic fibrosis lung. Molecular epidemiology and dynamics of Pseudomonas aeruginosa populations in lungs of cystic fibrosis patients. Airway microbiota and pathogen abundance in age-stratified cystic fibrosis patients.

PLoS One. Cystic fibrosis in adults, 75 cases and a review of cases in the literature. Studying bacterial infections through culture-independent approaches. Lipuma JJ. The changing microbial epidemiology in cystic fibrosis. Lung infections in cystic fibrosis: Deriving clinical insight from microbial complexity. Expert Rev. Innate Immun. J Clin. Molecular typing of the bacterial flora in sputum of cystic fibrosis patients. Characterization of bacterial community diversity in cystic fibrosis lung infections by use of 16S ribosomal DNA terminal restriction fragment length polymorphism profiling.

Bacterial activity in cystic fibrosis lung infections. Molecular identification of bacteria in bronchoalveolar lavage fluid from children with cystic fibrosis. Molecular detection of multiple emerging pathogens in sputa from cystic fibrosis patients. Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Microbial diversity in the sputum of a cystic fibrosis patient studied with 16S rDNA pyrosequencing. Phylogenetic and metabolic diversity of bacteria associated with cystic fibrosis.

ISME J. Relationship between cystic fibrosis respiratory tract bacterial communities and age, genotype, antibiotics and Pseudomonas aeruginosa. Disordered microbial communities in asthmatic airways. Topographical continuity of bacterial populations in the healthy human respiratory tract.

Nucleic Acids Res. Obesity alters gut microbial ecology. An obesity-associated gut microbiome with increased capacity for energy harvest. Toward defining the autoimmune microbiome for type 1 diabetes. Kuhnert P, Christensen H. Pasteurellaceae: Biology, Genomics and Molecular Aspects. Detection of anaerobic bacteria in high numbers in sputum from patients with cystic fibrosis. Use of 16S rRNA gene profiling by terminal restriction fragment length polymorphism analysis to compare bacterial communities in sputum and mouthwash samples from patients with cystic fibrosis.

Sputum induction as a research tool for sampling the airways of subjects with cystic fibrosis. Global diversity in the human salivary microbiome. Genome Res. Study of inter- and intra-individual variations in the salivary microbiota. BMC Genomics. Stimulation of the secretion of pro-inflammatory cytokines by Bifidobacterium strains.

Macdonald TT, Monteleone G. Immunity, inflammation, and allergy in the gut. Enteric salmonellosis disrupts the microbial ecology of the murine gastrointestinal tract. Role of the gut microbiota in defining human health. Anti Infect. Modelling the bacterial communities associated with cystic fibrosis lung infections. Neonatal antibiotic treatment alters gastrointestinal tract developmental gene expression and intestinal barrier transcriptome. Development of the human infant intestinal microbiota.

PLoS Biol. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. Reproducible community dynamics of the gastrointestinal microbiota following antibiotic perturbation. Dethlefsen L, Relman DA. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation.

The same procedure as in PAM50 was conducted and 10 signature gene sets were selected by CL for all 10 classes Table 6. As for cancer prediction, we first evaluated the CL performance on the cancer dataset through 5-fold cross-validation and simulation, where the same model as in 1 was applied to model the experimental bias.

Notice that the prediction problem is a class classification and it is extremely challenging even without any experiment bias. Horizontal axis represents the experimental bias level and vertical axis represents the classification accuracy. A set of 10 signature gene sets was first obtained on the reference Cancer dataset Table 6 and the prediction results were shown in Fig.

Although there was no true Cancer classification for TCGA-BRCA samples, it was shown in [ 28 ] that the 10 subtypes have unique characteristics in terms of their protein marker status, PAM50 classification, mutation and copy number variation and these characteristics provide ample evidence to assess the performance.

Using these characteristics, we evaluated the classification performance by assessing the enrichment of the characteristics in the corresponding class. The rest 6 classes were excluded because the corresponding characteristics were not available in the BRCA dataset.

The analysis results of CL predictions and the seven normalization algorithms are presented in Table 8. It is clear that the Cancer characteristics are highly enriched in CL predictions. This is highly consistent with the fact that Class 2 is mainly characterized as ER positive Table 7. In contrast, the predictions by all the seven normalization algorithms showed poor enrichment of desired characteristics.

GQ and DWD did predict samples in four classes; however, the enriched characteristics of the predicted samples did not agree with the original characteristics. It also predicted Class 3 samples but only 75 of them are Luminal A samples. Horizontal axis represents the number of samples classified for each cancer cluster. Different colors label the PAM50 class label. Comparison of cancer prediction results between CL and 7 alternative cross platform normalization algorithm.

First we aimed to find genes differential expressed in Qatar breast cancer patient compare to the control sample. With two sample t test and adjusted P value set to 0. We also aimed to find the genes uniquely expressed only in Aerobic species by comparing QNRF dataset with another set of breast cancer population. This dataset contains 43 Caucasian species samples. It has the same microarray platform as the QNRF dataset.

Both datasets went through the same pre-process procedure and additional round of normalization was done on two datasets together. Note that this analysis was not performed on all the genes but only on the differential expressed genes detected previously. All seven cross platform normalization algorithms and quantile normalization were performed in order to detect common differential expressed gene unique to QNRF dataset.

However, among all the cross-platform normalization algorithms, no common gene is reported. Although we cannot provide a consistent list of genes that differential expressed across all normalization algorithm, this 15 gene together could be our primary target of interest in future study for breast cancer in Qatar population. One interesting point is that among the 20 patients, most of the patients were identified as either Basal subtype or Her2 subtype, while only one Qatar sample was identified as Lum B.

This result suggests that over all, breast cancer in Qatar population behaves more like Basal and Her2 subtypes. However, additional tests using samples from larger cohorts need to be performed to confirm this finding. In this paper, we proposed a novel algorithm CrossLink for cross-condition prediction of cancer classes.

Unlike other normalization-based method, CL employs an unsupervised algorithm, which aims at identifying unique class-specific signatures patterns. In all tested datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization algorithms. Despite its advantages, CL has limitations. First, CL is better fitted for datasets of large sample size, because CL needs to perform an unsupervised learning.

It cannot be applied to individual samples separately as what a classifier would do. By the same reasoning, CL would fail when there are samples from only a single class. Our future work includes to three directions. First, the result of the CL indicates that instead of choosing a common signature set for all subtypes classification, subtype specific signatures can lead to better robustness and accuracy for subtypes classification. Further investigation is needed to discover the biological insight of those signatures.

By doing so, the subtype related function could be also discovered. Second, CL shows great potential for subtype classification in cross-condition breast cancer subtype classification. This ability could be further extended into other cancer genomic classification problems when condition specific bias presented. Third, the unique design of CL allows it bypassing the condition specific bias to achieve a robust classification accuracy.

This advantage can be further extended to handle bias between different technical platforms, for example, between microarray and RNA-seq data. The publication costs for this article were funded by the corresponding author. All authors read and approved the final manuscript. Chifeng Ma, Email: moc. Konduru S. Sastry, Email: gro. Mario Flore, Email: moc. Salah Gehani, Email: aq. Issam Al-Bozom, Email: aq. Yusheng Feng, Email: ude. Erchin Serpedin, Email: ude.

Lotfi Chouchane, Email: ude. Yidong Chen, Email: ude. Yufei Huang, Email: ude. BMC Genomics. Published online Aug Chifeng Ma , 1 Konduru S. Author information Article notes Copyright and License information Disclaimer. Corresponding author. This article has been cited by other articles in PMC. Abstract Background We considered the prediction of cancer classes e. Methods To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink CL.

Conclusions A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. Background The rapid development of high-throughput technologies including microarray and high-throughput sequencing have significantly advanced our understanding of disease including cancer [ 1 ].

Open in a separate window. Methods Problem definition and CL algorithm details Suppose that we are given a reference dataset that measures global gene expression of a set of known cancer classes e. Signature gene set identification As commonly defined, the signature gene set of a cancer class include genes that show uniquely differential expression in that class.

Class prediction Once the signature gene sets are determined for each class, the next step is to predict the class labels for a new set of data samples. Code implementation and development environment All algorithms are designed and implemented under Matlab Ra. PAM50 prediction of a simulated dataset We first evaluated CL on a simulated dataset, where the true class labels for the test samples are known.

Table 3 Impact of different threshold on selected size, value and corresponding classification accuracy. T1 T2 combination Selected gene size Smallest absolute expression Classification accuracy 0. Cross-experiment prediction of cancer subtypes Recently, over breast cancer patients cancer were profiled and a classification including 10 novel breast cancer subtypes were reported based on the integrative study of microarray gene expression, copy number variation as well as gene mutation information [ 28 ].

Evaluation by simulation Cancer contains two parts, where first part is a discovery dataset that includes breast cancer patients samples and the second part includes 5 additional validation sets including another over breast cancer patient samples.

Table 6 CL selected signature gene set size for cancer Table 7 Selected cancer classes and their characteristics. Table 8 Comparison of cancer prediction results between CL and 7 alternative cross platform normalization algorithm. Discussion and Conclusions In this paper, we proposed a novel algorithm CrossLink for cross-condition prediction of cancer classes.

Declarations The publication costs for this article were funded by the corresponding author. Competing interests The authors declare that they have no competing interests. Consent for publication Not applicable. Contributor Information Chifeng Ma, Email: moc. References 1. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res. Shao L, et al. Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.

PLoS One. Takahashi Y, et al. Microarray analysis reveals that high mobility group A1 is involved in colorectal cancer metastasis. Oncol Rep. Liu Q, et al. Gene selection and classification for cancer microarray data based on machine learning and similarity measures. Callari M, et al. Gougelet A, et al. Estrogen receptor alpha and beta subtype expression and transactivation capacity are differentially affected by receptor-, hsp and immunophilin-ligands in human breast cancer cells.

J Steroid Biochem Mol Biol. Nielsen TO, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. Bentink S, et al. Paroni G, et al. Chin SF, et al. High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer.

Genome Biol. MAQC Consortium. Shi L, et al. Nat Biotechnol. Shabalin AA, et al. Merging two gene-expression studies via cross-platform normalization. Benito M, et al. Adjustment of systematic microarray data biases. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes. BMC Bioinformatics. Walker WL, et al. Empirical Bayes accomodation of batch-effects in microarray data using identical replicate reference samples: application to RNA expression profiling of blood from Duchenne muscular dystrophy patients.

Jiang H, et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. Xia XQ, et al. WebArrayDB: cross-platform microarray data analysis and public data repository. Dembele D, Kastner P. Fuzzy C-means method for clustering microarray data. Bastien RR, et al. BMC Med Genomics. Park SY, et al.

Heterogeneity for stem cell-related markers according to tumor subtype and histologic stage in breast cancer. Martin M, et al. PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer. Breast Cancer Res Treat. Prat A, et al. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Deus HF, et al. J Biomed Inform. Oh DS, et al. Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers.

J Clin Oncol. Jorgensen CL, et al. PAM50 breast cancer intrinsic subtypes and effect of gemcitabine in advanced breast cancer patients. Acta Oncol. Parker JS, et al.

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Please let me know if you think my observation is incorrect or if you have a better solution. Thanks for your feedback!!! Parallel Server can be considered as a "matlab for cluster node". I want to install the certification kits, but the installer checks if parallel server has installed As I understand mathwork's logic You need a special parallelserver's FIK for that. And this is just what they say in error on your screen Pay attention that it complains about matlab component while your FIK key does not even allow you to install matlab component!!!

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