+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

Bmc Medical Genomics 12(Suppl 1): 21

Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered. We analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx. Among 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers. Our findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 066446573

Download citation: RISBibTeXText

PMID: 30704474

DOI: 10.1186/s12920-018-0463-6

Related references

Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach. Bmc Oral Health 18(1): 128, 2018

The importance of selecting the appropriate reference genes for quantitative real time PCR as illustrated using colon cancer cells and tissue. F1000research 5: 99, 2016

Precise Th/ U-dating of small and heavily coated samples of deep sea corals. Earth and Planetary Science Letters 170(4): 391-401, 1999

A Deep Learning Approach for Cancer Detection and Relevant Gene Identification. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 22: 219-229, 2017

How "healthy" should children be when selecting reference samples for spirometry?. European Respiratory Journal 45(6): 1576-1581, 2016

The internal reference technique in microdialysis a practical approach to monitoring dialysis efficiency and to calculating tissue concentration from dialysate samples. Journal of Neuroscience Methods 40(1): 31-38, 1991

Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. Journal of Pathology Informatics 7: 38, 2016

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. Neuroimage. Clinical 17: 169-178, 2018

Correspondence re: M. R. Teixeira et al., Karyotypic comparisons of multiple tumorous and macroscopically normal surrounding tissue samples from patients with breast cancer. Cancer Res., 56: 855-859, 1996. Cancer Research 56(21): 5098-5098, 1996

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection. Ieee/Acm Transactions on Computational Biology and Bioinformatics 2018, 2018

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images. Journal of Digital Imaging 2018, 2018

Development of rapid and precise Pb isotope analytical techniques using MC-ICP-MS and new results for GSJ rock reference samples. Geochemical Journal 40(2): 121-133, 2006

Evaluation of the methods for enumerating coliform bacteria from water samples using precise reference standards. Letters in Applied Microbiology 42(4): 350-356, 2006

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. Ieee/Acm Transactions on Computational Biology and Bioinformatics 2018, 2018

Optimization of reference genes for normalization of the quantitative polymerase chain reaction in tissue samples of gastric cancer. Asian Pacific Journal of Cancer Prevention 15(14): 5815-5818, 2015