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Code to generate Figure 2 of the paper "Predicting age from the transcriptome of human dermal fibroblasts" from the FPKM tables
Jupyter Notebook Python
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README.md

Predicting-age-from-the-transcriptome-of-human-dermal-fibroblasts

This project generates Figure 2 from the paper "Predicting age from the transcriptome of human dermal fibroblasts", Genome Biology, 2018. (Generate panels for figure.ipynb)

There are also notebooks that allow you either train new ensembles on your own data (Train your own predictor on your data.ipynb) pr to try to use ensembles trained on our data to predict age in your own data (Run a pre-trained predictor on your own data.ipynb). In general I expect that training your own ensemble will work well if you have skin fibroblast RNA-seq data from at least 3 people per decade spanning an age range from 0 to 70 years. I generally expect that batch effects will dominate and prevent good predictions if you try to use one of my pre-trained classifiers, but I am always happy to be proved wrong and it's easy to do, so why not try?. Each of these notebooks has detailed instructions and explanations inside.

PLEASE NOTE: I've had a lot of trouble with git lfs... I migrated away from keeping the large fully trained classifiers (ensemble LDA > 4GB) in pickle format. There were also troubles with this for reproducability: if you used a different numpy/joblib than I you wouldn't be able to load the binary dump. Now to use a "pretrained" classifier the software actually retrains on the Hetzer lab data with the correct parameters used in the paper. This may mean that exact results will differ slightly from published as libraries like sklearn change. Sorry about that, but this is best for the long term usability of this software.

Abstract

There is a marked heterogeneity in human lifespan and health outcomes for people of the same chronological age. Thus, one fundamental challenge is to identify molecular and cellular biomarkers of aging that could predict lifespan and be useful in evaluating lifestyle changes and therapeutic strategies in the pursuit of healthy aging. Here, we developed a computational method to predict biological age from gene expression data in skin fibroblast cells using an ensemble of machine learning classifiers. We generated an extensive RNA-seq dataset of fibroblast cell lines derived from 133 healthy individuals whose ages range from 1 to 94 years, and 10 patients with Hutchinson-Gilford Progeria Syndrome (HGPS), a premature aging disease. On this dataset, our method predicted chronological age with a median error of 4 years, outperforming algorithms proposed by prior studies that predicted age from DNA methylation [1–5] and gene expression data [3,6] for fibroblasts. Importantly, our method consistently predicted higher ages for Progeria patients compared to age-matched controls, suggesting that our algorithm can identify accelerated aging in humans. These results show that the transcriptome of skin fibroblasts retains important age-related signatures. Our computational tool may also be applicable to predicting age from other genome-wide datasets.

  1. Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome Biology 14, 3156.
  2. Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013). Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Molecular Cell 49, 359–367.
  3. Peters, M.J., Joehanes, R., Pilling, L.C., Schurmann, C., Conneely, K.N., Powell, J., Reinmaa, E., Sutphin, G.L., Zhernakova, A., Schramm, K., et al. (2015). The transcriptional landscape of age in human peripheral blood. Nature Communications 6, 8570.
  4. Xu, C., Qu, H., Wang, G., Xie, B., Shi, Y., Yang, Y., Zhao, Z., Hu, L., Fang, X., Yan, J., et al. (2015). A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Scientific Reports 5, 17788.
  5. Zhang, Y., Wilson, R., Heiss, J., Breitling, L.P., Saum, K.-U., Schöttker, B., Holleczek, B., Waldenberger, M., Peters, A., and Brenner, H. (2017). DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nature Communications 8, 14617.
  6. Holly Alice C., Melzer David, Pilling Luke C., Henley William, Hernandez Dena G., Singleton Andrew B., Bandinelli Stefania, Guralnik Jack M., Ferrucci Luigi, and Harries Lorna W. (2013). Towards a gene expression biomarker set for human biological age. Aging Cell 12, 324–326.

Getting started

Prerequisites

While it may work with different versions of these packages, the software was tested in a GNU Linux environment using the following packages:

  • Jupyter notebook 5.0.0
  • Python 2.7.9
  • Pandas 0.22
  • Sklearn 0.19 for the Generate panels notebook, 0.20 for the others
  • Matplotlib 2.1.0
  • Seaborn 0.7.1

Using this software to try to predict age from your own data

Open one of the notebooks (Train your own or Run pre-trained) and follow the detailed instructions inside.

Using this software to regenerate the figures of the paper

Open the notebook "Generate panels for figure.ipynb" in Jupyter.

To regenerate figures from the saved runs, simply select "Cell -> Run All" from the pull down menus.

To run the analysis again from scratch using the fpkm tables and metadata distributed in this repository, go to the cell where the function make_figs() is declared. In the next 4 cells after that declaration are the calls to make_figs() that generate the data. Inside those calls are three lines that are commented out, that need to be uncommented before selecting "Run All" from teh pull down menus. The lines like:

svregr = make_figs( 'Support vector regression', model=subsvr,
                  #search_cval=search_cval, parameters=parameters, # uncomment these lines
                  #plot_cval=LeaveOneOut(),                        # to rerun the analysis from scratch
                  #lcurve_cval=lcurve_cval,                        # instead of loading results from disk
                  njobs=njobs)   

Be aware that running the LDA ensemble from scratch will take many days of compute time, as documented in code comments.

After training a model, e.g. linregr, one might wish to examine the particular genes that meet subsetting criteria, which are used to predict age. This list of genes is in the class variable linregr.genecolumns_ Similarly, one might wish to know how important a given gene is to creating the age prediction; this can be found in the class variable linregr.coef_ which contains the linear coeficients that transform the FPKM vector (arranged in the order specified in genecolumns_) into an age prediction for a given subject. For the ensemble, the genes used are in ensemble.genecolumns_. The importance of each gene to an age prediction is more difficult to understand in an ensemble, since there are N classifiers in the ensemble, and each has coefficients for M = ceil( ( subject_age_range / N ) classes. Thus ensemble.classifiers_[X].coef_[Y], where X ∈ [0,N) and Y ∈ [0,M), contains the linear coeficients that transform the FPKM vector into a confidence score that classifier X would assign this subject to class Y. A subject is assigned to the class with the highest such confidence score. For further details see the documentation and source code for LinearDiscriminantAnalysis in scikit-learn.

Code structure

  • A custom function handles the loading of the FPKM and metadata.
  • Sublasses of scikit-learn functions are implemented that do gene subsetting during the .fit() call for all the regression algorithms.
  • A class is implemented that handles the staggered age-bin ensenble.
  • Thereafter, standard scikit-learn libraries are used to do parameter search and cross-validation.
  • make_figs() function handles the creation of the models, stores the model & predictions for later so that figures can be re-generated/modified from previous runs, and creates the figures themselves.

Author

Jason G. Fleischer

License

This project is licensed under the BSD License - see the LICENSE.md file for details

Acknowledgments

As always, we stand on the shoulders of giants.

This research would not have been possible without the ideas and backing of Martin Hetzer and Saket Navlakha. Robbie Schulte and Hannah Tsai did an enormous amount of bench work to make this happen. The real bioformaticians on this paper are Max Shokirev and Ling Huang, their expertise kept me from many mistakes. And none of this would exist without the many researchers who have worked to understand the difference between biological age and chronological age over the years.

The computational end of this research owed a huge debt to many pioneers of machine learning and statistical modelling. The programming of this project would have been an order of magnitude harder without the many developers of scikit-learn, scipy, numpy, pandas, seaborn, matplotlib, jupyter, and python.

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