deepmg 0.4.15

A python package to visualize/train/predict data using machine/deep learning algorithms

Met2Img (deepmg): Metagenomic data To Images using Deep learning

Met2Img (deepmg) is a computational framework for metagenomic analysis using Deep learning and classic learning algorithms:

• Supports to VISUALIZE data into 2D images, TRAIN data shaped 1D or 2D with many different algorithms and PREDICT new data with a pretrained network.
• Provides a variety of binnings: SPB, QTF, MMS,...
• Supports numerous methods for visualizing data including Fill-up, t-Distributed Stochastic Neighbor Embedding (t-SNE), Linear Discriminant Analysis (LDA), Isomap, Principal Component Analysis (PCA), Random Projection (RD_Pro), Multidimensional Scaling (MDS), Spectral Embeddings (SE), Non-Negative Matrix Factorization (NMF), Locally Linear Embedding (LLE).
• Provides a vast of classifiers (Convolutional Neural Networks, Linear Regression, Random Forests (RFs), Support Vector Machines (SVMs),... also can be loaded from a pretrained network and be able to extend easily) for 1D and 2D data.
• Comprises cross-validation analysis with internal validation and external validation (optional) as well as holdout validation.
• Supports to reduce dimension (for very-dimensional data) before visualizing with PCA, RD_Pro
• Flexibility for testing models with a large range of parameters provided.
• Supports various datatypes, such as abundance and read counts with different levels of OTUs such as species, genus...
• Evaluates models with various metrics: Accuracy (ACC), Area Under Curve (AUC), Matthews Correlation Coefficient (MCC), f1-score, confusion matrix,...
• 25 available datasets with > 5000 samples for test (download from Met2img,deepmg)
• The package is now available to install by pip command, supporting MacOS, Linux

References:

This work was presented in Disease Classification in Metagenomics with 2D Embeddings and Deep Learning.

Please cite Met2Img (deepmg) in your publications if it helped your research. Thank you very much!

Getting Started

Prerequisites

• These packages should be installed before using Met2Img (updated to 21/02/2018):

tensorflow	1.5.0
sklearn	0.19.1
keras	2.1.3
numpy	1.14.0
matplotlib	2.1.2

• Please install if you do not have them

pip install matplotlib
pip install numpy

conda install scikit-learn
conda install -c conda-forge tensorflow 
conda install -c conda-forge keras

pip install Keras-Applications
pip install Keras-Preprocessing
pip install keras_sequential_ascii

• In order to use the packages for explanation of the network trained, please download and install:

[Grad-Cam, Saliency] (https://github.com/jacobgil/keras-grad-cam)

[LIME] (https://github.com/marcotcr/lime/tree/master/doc/notebooks)

Install or Download the package Met2Img

In order to install the package

pip install deepmg

In order to download the package

git clone https://git.integromics.fr/published/deepmg

Running Experiments

How to use Met2Img

• Input:

  • mandatory: csv files containing data (*_x.csv) and labels (*_y.csv)
  • optional: if use external validation set: data (*_zx.csv) and labels (*_zy.csv)) put in data changable with parameters --orginal_data_folder).

  For examples, cirphy_x.csv and cirphy_y.csv for Cirrhosis dataset in [MetAML] (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004977) ONLY for internal validations; and ibdtrainHS_UCr_x.csv ibdtrainHS_UCr_y.csv ibdtrainHS_UCr_zx.csv ibdtrainHS_UCr_zy.csv for a dataset in [Sokol's] (https://www.ncbi.nlm.nih.gov/pubmed/26843508) datasets containing external validation set.

• Output:

  • images: Met2Img will generate images and store them in [images/name_dataset_parameters_to_generate_image/] (images/) (changable with parameters --parent_folder_img)

  • results: performance/training/testing information of each fold and summary results put in [results/name_dataset_parameters_to_generate_image/] (results/) (changable with parameters --parent_folder_results), includes more than 5 files:

    • *file_sum.txt: parameters used to run, performance at each fold. The last rows show training/testing performance in ACC, AUC, execution time, and other metrics of the experiment. When the experiment finishes, a suffix "_ok" (changable with parameters --suff_fini) appended to the name of file marking that the experiment finishes.

    • *file_eachfold.txt (if --save_folds=y): results of each fold with accuracy, auc, mcc, loss of training and testing.

    • *file_mean_acc.txt (if --save_avg_run=y): if the experiment includes n runs repeated independently, so the file includes average performance on k-folds of each run measured by accuracy and time execution at training/testing of beginning, training/testing when finished.

    • *file_mean_auc.txt (if --save_avg_run=y): if the experiment includes n runs repeated independently, so the file includes average performance on k-folds of each run measured by AUC at training/testing of beginning, training/testing when finished.

    • If --save_para=y: configuration file to repeat the experiment

    • If use --save_w=y (save weights of trained networks) and/or --save_entire_w=y, --save_d=y, then 2 folders will be created:

      • results/name_dataset_parameters_to_generate_image/models/: includes *weightmodel*.json contains structure of the model *weightmodel*.h5 stores weights.

      • results/name_dataset_parameters_to_generate_image/details/*weight_*.txt: contains accuracy and loss of training and testing every epochs --save_d=y. If --save_rf=y, then we will have important scores generated from RFs for each run.

• Get help to see parameters in the package:

Usage: dev_met2img.py [options]

Options:
  -h, --help            show this help message and exit
  -a TYPE_RUN, --type_run=TYPE_RUN
                        select a mode to run
  --time_run=TIME_RUN   give the #runs (default:10)
  --config_file=CONFIG_FILE
                        specify config file if reading parameters from files
  --seed_value_begin=SEED_VALUE_BEGIN
                        set the beginning seed for different runs (default:1)
  --channel=CHANNEL     channel of images, 1: gray, 2: color (default:3)
  -m DIM_IMG, --dim_img=DIM_IMG
                        width or height (square) of images, -1: get real size
                        of original images (default:-1)
  -k N_FOLDS, --n_folds=N_FOLDS
                        number of k folds (default:10)
  --test_size=TEST_SIZE
                        test size in holdout validation
  --test_exte=TEST_EXTE
                        if==y, using external validation sets (default:n)
  --parent_folder_img=PARENT_FOLDER_IMG
                        name of parent folder containing images
                        (default:images)
  -r ORIGINAL_DATA_FOLDER, --original_data_folder=ORIGINAL_DATA_FOLDER
                        parent folder containing data (default:data)
  -i DATA_DIR_IMG, --data_dir_img=DATA_DIR_IMG
                        name of dataset to run the experiment
  --search_already=SEARCH_ALREADY
                        earch existed experiments before running-->if existed,
                        stopping the experiment (default:y)
  --cudaid=CUDAID       id cuda to use <-1: use cpu, -1: use all available
                        gpus, >-1: use specific gpu (default:-2)
  --gpu_memory_fraction=GPU_MEMORY_FRACTION
                        gpu_memory_fraction for running by cuda
                        (0:auto_increase based on requirement)
  --preprocess_img=PREPROCESS_IMG
                        support resnet50/vgg16/vgg19, none: no use
                        (default:none)
  --mode_pre_img=MODE_PRE_IMG
                        support caffe/tf (default:caffe)
  --parent_folder_results=PARENT_FOLDER_RESULTS
                        parent folder containing results (default:results)
  --save_w=SAVE_W       save weight mode
  --save_d=SAVE_D       save details of learning
  --save_avg_run=SAVE_AVG_RUN
                        save avg performance of each run
  --suff_fini=SUFF_FINI
                        append suffix when finishing (default:ok)
  --save_rf=SAVE_RF     save important features and scores for Random Forests
  --save_para=SAVE_PARA
                        save parameters to files
  --path_config_w=PATH_CONFIG_W
                        if empty, save to the same folder of the results
  --path_vis_learn=PATH_VIS_LEARN
                        read config for generating images (for qtf/mms combine
                        fills/manifolds)
  --path_data_vis_learn=PATH_DATA_VIS_LEARN
                        path to read learned data for visualizing (for qtf/mms
                        combine fills/manifolds)
  --save_entire_w=SAVE_ENTIRE_W
                        save weight of model on whole datasets
  --save_folds=SAVE_FOLDS
                        save results of each folds (acc/loss/auc/mcc
  --debug=DEBUG         show DEBUG if y (default:n)
  --check_ok=CHECK_OK   check whether deepmg installed properly or not
  -v VISUALIZE_MODEL, --visualize_model=VISUALIZE_MODEL
                        visualize the model
  --algo_redu=ALGO_REDU
                        algorithm of dimension reduction (rd_pro/pca/fa),  if
                        emtpy so do not use (default:'')
  --rd_pr_seed=RD_PR_SEED
                        seed for random projection (default:None)
  --new_dim=NEW_DIM     new dimension after reduction (default:676)
  --reduc_perle=REDUC_PERLE
                        perlexity for tsne (default:10)
  --reduc_ini=REDUC_INI
                        ini for reduction (default:pca)
  --rnd_seed=RND_SEED   shuffle order of feature: if none, use original order
                        of data (only use for fillup) (default:none)
  -t TYPE_EMB, --type_emb=TYPE_EMB
                        type of the embedding (default:raw)
  --imp_fea=IMP_FEA     use sorting important feature supported 'rf' for many
                        overlapped figures (default:none)
  -g LABEL_EMB, --label_emb=LABEL_EMB
                        taxa level of labels provided in supervised embeddings
                        'kingdom=1','phylum=2','class=3','order=4','family=5',
                        'genus=6' (default:0)
  --emb_data=EMB_DATA   data embbed: '': transformed data; o: original data
                        (default:'')
  -y TYPE_DATA, --type_data=TYPE_DATA
                        type of binnings: species-bins with
                        spb/eqw/presence(pr) (default:spb)
  --del0=DEL0           if yes, delete features have nothing
  -p PERLEXITY_NEIGHBOR, --perlexity_neighbor=PERLEXITY_NEIGHBOR
                        perlexity for tsne/#neighbors for others (default:5)
  --lr_tsne=LR_TSNE     learning rate for tsne (default:100.0)
  --label_tsne=LABEL_TSNE
                        use label when using t-SNE,'': does not use
                        (default:'')
  --iter_tsne=ITER_TSNE
                        #iteration for run tsne, should be at least 250, but
                        do not set so high (default:300)
  --ini_tsne=INI_TSNE   Initialization of embedding: pca/random/ or an array
                        for tsne (default:pca)
  --n_components_emb=N_COMPONENTS_EMB
                        ouput dim after embedding (default:2)
  --method_lle=METHOD_LLE
                        method for lle embedding:
                        standard/ltsa/hessian/modified (default:standard)
  --eigen_solver=EIGEN_SOLVER
                        method for others (except for tsne) (default:auto)
  -s SHAPE_DRAWN, --shape_drawn=SHAPE_DRAWN
                        shape of point to illustrate data:
                        ,(pixel)/ro/o(circle) (default:,)
  --fig_size=FIG_SIZE   size of one dimension in pixels (if <=0: use the
                        smallest which fit data, ONLY for fillup) (default:24)
  --point_size=POINT_SIZE
                        point size for img (default:1)
  --setcolor=SETCOLOR   mode color for images (gray/color) (default:color)
  --colormap=COLORMAP   colormap for color images (diviris/gist_rainbow/rainbo
                        w/nipy_spectral/jet/Paired/Reds/YlGnBu) (default:'')
  --cmap_vmin=CMAP_VMIN
                        vmin for cmap (default:0)
  --cmap_vmax=CMAP_VMAX
                        vmax for cmap (default:1)
  --margin=MARGIN       margin to images (default:0)
  --alpha_v=ALPHA_V     The alpha blending value, between 0 (transparent) and
                        1 (opaque) (default:1)
  --recreate_img=RECREATE_IMG
                        if >0 rerun to create images even though they are
                        existing (default:0)
  --scale_mode=SCALE_MODE
                        scaler mode for input (default:none)
  --n_quantile=N_QUANTILE
                        n_quantile in quantiletransformer (default:1000)
  --min_scale=MIN_SCALE
                        minimum value for scaling (only for minmaxscaler)
                        (default:0) use if --auto_v=0
  --max_scale=MAX_SCALE
                        maximum value for scaling (only for minmaxscaler)
                        (default:1) use if --auto_v=0
  --min_v=MIN_V         limit min for Equal Width Binning (default:1e-7)
  --max_v=MAX_V         limit min for Equal Width Binning (default:0.0065536)
  --num_bin=NUM_BIN     the number of bins (default:10)
  --auto_v=AUTO_V       if y, auto adjust min_v and max_v (default:n)
  -f NUMFILTERS, --numfilters=NUMFILTERS
                        #filters/neurons for each cnn/neural layer
                        (default:64)
  -n NUMLAYERCNN_PER_MAXPOOL, --numlayercnn_per_maxpool=NUMLAYERCNN_PER_MAXPOOL
                        #cnnlayer before each max pooling (default:1)
  --nummaxpool=NUMMAXPOOL
                        #maxpooling_layer (default:1)
  --dropout_cnn=DROPOUT_CNN
                        dropout rate for CNN layer(s) (default:0)
  -d DROPOUT_FC, --dropout_fc=DROPOUT_FC
                        dropout rate for FC layer(s) (default:0)
  --padding=PADDING     ='y' uses pad, others: does not use (default:n)
  --filtersize=FILTERSIZE
                        the filter size (default:3)
  --poolsize=POOLSIZE   the pooling size (default:2)
  --model=MODEL         type of model (fc_model/model_cnn1d/model_cnn/model_vg
                        glike/model_lstm/resnet50/rf_model/svm_model/none)
                        (none: only visualization not learning)
  -c NUM_CLASSES, --num_classes=NUM_CLASSES
                        the output of the network (default:1)
  -e EPOCH, --epoch=EPOCH
                        the epoch used for training (default:500)
  --learning_rate=LEARNING_RATE
                        learning rate, if -1 use default value of the
                        optimizer (default:-1)
  --batch_size=BATCH_SIZE
                        batch size (default:16)
  --learning_rate_decay=LEARNING_RATE_DECAY
                        learning rate decay (default:0)
  --momentum=MOMENTUM   momentum (default:0)
  -o OPTIMIZER, --optimizer=OPTIMIZER
                        support sgd/adam/Adamax/RMSprop/Adagrad/Adadelta/Nadam
                        (default:adam)
  -l LOSS_FUNC, --loss_func=LOSS_FUNC
                        support binary_crossentropy/mae/squared_hinge
                        (default:binary_crossentropy)
  -q E_STOP, --e_stop=E_STOP
                        #epochs with no improvement after which training will
                        be stopped (default:5)
  --e_stop_consec=E_STOP_CONSEC
                        option to choose consective (self defined: consec) or
                        not (default:consec)
  --svm_c=SVM_C         Penalty parameter C of the error term for SVM
                        (default:1.0)
  --svm_kernel=SVM_KERNEL
                        the kernel type used in the algorithm (linear, poly,
                        rbf, sigmoid, precomputed) (default:linear)
  --rf_n_estimators=RF_N_ESTIMATORS
                        The number of trees in the forest (default:500)
  --pretrained_w_path=PRETRAINED_W_PATH
                        path of weight file of a pretrained model
  -z COEFF, --coeff=COEFF
                        coeffiency (divided) for input (should use 255 for
                        images) (default:1)
  --grid_coef_time=GRID_COEF_TIME
                        choose the best coef from #coef default for tuning
                        coeficiency (default:5)
  --cv_coef_time=CV_COEF_TIME
                        k-cross validation for each coef for tuning
                        coeficiency (default:4)
  --coef_ini=COEF_INI   initilized coefficient for tuning coeficiency
                        (default:255)
  --metric_selection=METRIC_SELECTION
                        roc_auc/accuracy/neg_log_loss/grid_search_mmc for
                        tuning coeficiency (default:roc_auc)

Some examples as below:

Available Datasets

The framework, in default, run 10 times with 10-stratified-cross-validation

** NOTES:

1. Select run on GPU, set cudaid in (0,1,2,3) (id of GPU on machine, id=-1 means use of cpu). Note: your computation nodes must be supported with GPU and installed Tensorflow GPU.
2. Select dataset with parameter '-i', eg. '-i cirphy' (phylogenetic cirrhosis dataset)
3. Select model with parameter '--model', eg. '--model model_cnn'. Default: The model with one Fully connected layer (FC) model
4. Other parameters, One can refer at the function para_cmd()

Code to run experiment (for raw data)

Parameters: -n: number of convolutional layers, -f : number of filters, -t: type of embeddings (suporting raw-1D and images 2D such as fillup, t-sne, isomap, lda,....)

cd ~/deepMG_tf/
db='wt2dphy';
type_embed='raw';
python dev_met2img.py -i $db -t $type_embed 
python dev_met2img.py -i $db -t $type_embed --model model_cnn1d -n 1 -f 64 

Code to run experiment (for fill-up with gray images)

use SPB

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t fill -y spb --fig_size 0 -z 255 --setcolor gray --channel 1
python dev_met2img.py -i $db -t fill -y spb --fig_size 0 -z 255 --setcolor gray --channel 1 --model model_cnn -n 1 -f 64 

use PR

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t fill -y pr --fig_size 0 -z 255 --setcolor gray --channel 1

use QTF

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t fills -y eqw --scale_mode qtf --auto_v y  --fig_size 0 -z 255 --setcolor gray --channel 1

Code to run experiment (for fill-up with color images)

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t fill -y spb --fig_size 0 -z 255 --preprocess_img vgg16 --colormap jet_r

Code to run experiment with visualizations based on manifold learning, e.g, t-sne (changed the parameter of '-t' to 'tsne')

We are also able to test another embeddings such isomap, lle,... We use images of 24x24 (--fig_size 24) and transparent rate (alpha_v = 0.5)

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t tsne -y spb --fig_size 24 -z 255 --setcolor gray --channel 1 --alpha_v 0.5

Scripts (*.sh) in details provided in utils/scripts with:

The scripts mostly are used for datasets in group A (if not specified) including predicting cirrhosis, colorectal, IBD, obesity, T2D (and WT2D dataset). The header part of each file consists of the information on memory, number of cores, walltime, email, etc. which used in job schedulers. These parameters should be modified depending on your available resources. Each file runs numerous models for one dataset.

Scripts for 6 datasets (files: cirphy_* (cirphy_x.csv for data and cirphy_y.csv for labels, in order to run training on this dataset set the parameter -i, eg. "-i cirphy"), colphy_*, ibdphy_*, obephy_*, t2dphy_*, wt2dphy_*) in group A [MetAML] (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004977):

• 1d: scripts for running models with 1D data
• manifold_iso: training species abundance using visualizations based on Isomap.
• manifold_mds: training species abundance using visualizations based on MDS.
• manifold_nmf: training species abundance using visualizations based on NMF.
• manifold_pca: training species abundance using visualizations based on PCA.
• manifold_lda1,2,3,4,5,6: training species abundance using visualizations based on LDA (supervised) with labels using various levels of OTUs (1: Kingdom, 2: Phylum, 3: Class,4: Order,5: Family and 6: genus).
• phy0_24_cmap_r: investigate a vast of colormaps (viridis, rainbow, jet,...)
• phyfill0_vgg: investigate various paramters of VGG architectures.
• fill0cnn: run experiments using Fill-up with different CNN hyper-parameters.
• phyfill0_rnd: the experiments using Fill-up with random feature ordering

Scripts training datasets for other groups:

• gene_fill: training gene families abundance (names: cirgene, colgene, ibdgene, obegene, t2dgene, wt2dgene) with Fill-up and machine_learning_gene: training gene families abundance with stardard learning algorithms (SVM, RF).
• phyfill0_CRC: experiments on datasets (yu, feng, zeller, vogtmann, crc) in the paper Multi-cohort analysis of colorectal cancer metagenome identified altered bacteria across populations and universal bacterial
• phyfill0_phcnn: experiments on datasets (files: ibdtrainHS_CDf, ibdtrainHS_CDr, ibdtrainHS_iCDf, ibdtrainHS_iCDr, ibdtrainHS_UCf, ibdtrainHS_UCr)in the paper Phylogenetic convolutional neural networks in metagenomics
• balance_phyfill0 (for color images) and balance_phygrayfill0 (for gray images): experiments on datasets (hiv, crohn) in the paper Balances: a New Perspective for Microbiome Analysis

Utilities and visualizations

Visualize models by ASCII

Just add "-v 1" to visualize the network. In order to use this feature, please install 'keras_sequential_ascii'

cd ~/deepMG_tf/
db='wt2dphy';
size_img=0;
python dev_met2img.py -i $db -t fill -y spb --fig_size 0 -z 255 --setcolor gray --channel 1 -v y
python dev_met2img.py -i $db -t fill -y spb --fig_size 0 -z 255 --setcolor gray --channel 1 --model model_cnn -n 1 -f 64 -v y

Jupyter: Visualization of representations

Please move to ./utils/jupyter/ to visualize representations based on images:

cd ~/deepMG_tf/utils/jupyter/
sudo jupyter notebook --allow-root

• compare_manifolds.ipynb : visualizations generated from manifold learning such as t-SNE, LDA, Isomap
• plot_distribution_taxa_levels_colormaps.ipynb : show how fill-up works and visualize important features using fill-up
• visual_fillup_colormaps.ipynb : illustrate various colormaps
• vis_explanations_cnn_LIME_GRAD.ipynb : exhibit explanations by Saliency, LIME and Grad-Cam

Summarize the results

Some tools are available in this project (./utils/read_results) supporting to collect the data, filter the results, and delete uncompleted experiments

• read_res.py: collect all experiments with each row presenting the average performance in ACC, AUC, MCC, f1-score, execution time...
• filtered_metrics.py : explore in detail the performance in ACC, AUC, MCC on all folds of one or more experiments. The output includes ACC, AUC, MCC of all folds based on a file containing a list of files.
• unfi_delete.py: delete/count uncompleted log files of unfinished experiments.

Authors

• Thanh Hai Nguyen (E-mail: hainguyen579 [at] gmail.com)
• Edi Prifti (E-mail: e.prifti [at] ican-institute.org)
• Nataliya Sokolovska (E-mail: nataliya.sokolovska [at] upmc.fr)
• Jean-daniel Zucker (E-mail: jean-daniel.zucker [at] ird.fr)