In scfurl/m3addon: This package adds to the popular "monocle3"

rm(list=ls())
reticulate::use_python("/Users/sfurla/.virtualenvs/py3/bin/python", required = T)
library(reticulate)
reticulate::py_config()
py_available("backspin")
py_available("trimap")
py_available("scrublet")
# py_install("trimap", envname = "py3")
# py_install("sklearn", envname = "py3")
# py_install("scikit-learn", envname = "py3")

import sklearn
import trimap
from sklearn.datasets import load_digits

digits = load_digits()
dat = digits.data
embedding = trimap.TRIMAP(n_inliers=20,
                          n_outliers=10,
                          n_random=10,
                          weight_adj=1000.0).fit_transform(dat)

library(ggplot2)
umap<-as.data.frame(uwot::umap(py$dat))
colnames(umap)<-c("x", "y")
umap$col<-as.factor(py$digits$target)
emb<-as.data.frame(py$embedding)
colnames(emb)<-c("x", "y")
emb$col<-as.factor(py$digits$target)
ggplot(emb, aes(x=x, y=y, color=col))+geom_point()+theme_bw()+ggtitle("trimap")
ggplot(umap, aes(x=x, y=y, color=col))+geom_point()+theme_bw()+ggtitle("umap")

reticulate::use_python("/Users/sfurla/.virtualenvs/py3/bin/python", required = T)
py_module_available("trimap")
suppressPackageStartupMessages({
  library(monocle3)
  library(m3addon)
  library(reticulate)
  library(openxlsx)  
  library(dplyr)
  library(Matrix)
  library(ggplot2)
  #library(rhdf5)
  library(h5)
  library(xfun)
  library(pals)
  library(RColorBrewer)
  library(piano)
  library(GSEABase)
  library(data.table)

})

ROOT_DIR="/Users/sfurla/Box Sync/PI_FurlanS/computation"
CDS_DIR <- file.path(ROOT_DIR, "Analysis", "NHPTreg_mm", "cds", "4thRound")
cds <- readRDS(file.path(CDS_DIR, "190820_m3_CDS.RDS"))
mix_S <-readRDS(file.path(CDS_DIR, "cds_Day3Day20andTregs_NaiveEffGenes.RDS"))
pData(cds)$UMAP_Clust<-NA
pData(cds)$UMAP_Clust[match(colnames(mix_S), colnames(cds))]<-as.character(mix_S@phenoData@data$Cluster_Lab)
pData(cds)$UMAP_1<-NA
pData(cds)$UMAP_2<-NA
pData(cds)$UMAP_1[match(colnames(mix_S), colnames(cds))]<-t(mix_S@reducedDimA)[,1]
pData(cds)$UMAP_2[match(colnames(mix_S), colnames(cds))]<-t(mix_S@reducedDimA)[,2]
cds_S<-cds[,!is.na(pData(cds)$UMAP_Clust)]
cds_S<-cds_S[,match(colnames(cds_S),colnames(mix_S))]
reducedDims(cds_S)[["UMAP"]]<-cbind(pData(cds_S)$UMAP_1, pData(cds_S)$UMAP_2)
rm(cds, mix_S)

#source_python(file.path("/Users/sfurla/Box Sync/PI_FurlanS/computation/Rproj/m3addon/inst/trimap.py"))


plot_cells(cds_S, color_cells_by = "UMAP_Clust", reduction_method = "UMAP",  label_cell_groups = F, cell_size = 0.7)
#X<-t(as.matrix(exprs(cds_S)))
#debug(trimap)
plot_pc_variance_explained(cds_S)
cds<-trimap(cds_S, num_dims = 10)

plot_cells(cds_S, color_cells_by = "Category", reduction_method = "trimap",  label_cell_groups = F, cell_size = 0.7)

def trimap_fromR(data, n_dims, n_inliers, n_outliers, n_random, distance, lr, n_iters, knn_tuple, apply_pca, opt_method, verbose, weight_adj, return_seq):
  import trimap
  knn_tuple=None
  embedding = trimap.TRIMAP(n_dims = int(n_dims), n_inliers = int(n_inliers), n_outliers = int(n_outliers), n_random = int(n_random), distance = str(distance), lr = float(lr), n_iters = int(n_iters), apply_pca = bool(apply_pca),opt_method = str(opt_method), verbose = bool(verbose), weight_adj = float(weight_adj), return_seq = bool(return_seq)).fit_transform(data)
  return(embedding)

#reticulate::virtualenv_create(envname = "solo", python="/usr/local/bin/python3")
reticulate::use_python("/Users/sfurla/.virtualenvs/solo/bin/python3", required = T)
library(reticulate)
reticulate::py_config()
py_module_available("solo")
py_install("/Users/sfurla/develop/solo")

suppressPackageStartupMessages({
  library(monocle3)
  library(m3addon)
  library(reticulate)
  library(openxlsx)  
  library(dplyr)
  library(Matrix)
  library(ggplot2)
  #library(rhdf5)
  library(h5)
  library(xfun)
  library(pals)
  library(RColorBrewer)
  library(piano)
  library(GSEABase)
  library(data.table)
  library(Seurat)
})

cds<-readRDS("/Users/sfurla/Box Sync/PI_FurlanS/computation/Analysis/KpOxCy/cds/191208_DoubletsCalled4methods.RDS")
og<-cds@int_metadata$dispersion$gene_short_name[cds@int_metadata$dispersion$use_for_ordering]
X<-as.matrix(exprs(cds[rownames(cds) %in% og,cds$group %in% "15w_CAR_Alone"]))
Xs<-t(X[,sample(1:dim(X)[2], 350)])

#dim(Xs)
#write.csv(X, file.path("/Users/sfurla/Box Sync/PI_FurlanS/computation/Rproj/m3addon/testdata.csv"))
#source_python(file.path("/Users/sfurla/Box Sync/PI_FurlanS/computation/Rproj/m3addon/inst/solo.py"))
cn<-colnames(Xs)
#solo(Xs, cn)

X = r[["Xs"]]
gene_names = r[["cn"]]
doublet_depth=2.0
gpu=False
out_dir ='solo_out'
doublet_ratio=2.0
seed=None
known_doublets=None
doublet_type='multinomial'
expected_number_of_doublets=None
plot=False
normal_logging=False
n_hidden = 128
n_latent = 16
cl_hidden= 64
cl_layers = 1
dropout_rate = 0.1
learning_rate=0.001
valid_pct=0.1
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import json
import os
import shutil
import anndata
import numpy as np
from anndata import AnnData
from sklearn.metrics import roc_auc_score, roc_curve
from scipy.sparse import issparse
from collections import defaultdict
import scvi
from scvi.dataset import AnnDatasetFromAnnData, LoomDataset, GeneExpressionDataset
from scvi.models import Classifier, VAE
from scvi.inference import UnsupervisedTrainer, ClassifierTrainer
import torch
from solo.utils import create_average_doublet, create_summed_doublet, create_multinomial_doublet, make_gene_expression_dataset
if not normal_logging:
  scvi._settings.set_verbosity(10)
if gpu and not torch.cuda.is_available():
  gpu = torch.cuda.is_available()
  print('Cuda is not available, switching to cpu running!')
# if not os.path.isdir(out_dir):
#     os.mkdir(out_dir)
# data_ext = os.path.splitext(data_file)[-1]
# if data_ext == '.loom':
#     scvi_data = LoomDataset(data_file)
# elif data_ext == '.h5ad':
#     scvi_data = AnnDatasetFromAnnData(anndata.read(data_file))
# else:
#     msg = f'{data_ext} is not a recognized format.\n'
#     msg += 'must be one of {h5ad, loom}'
#     raise TypeError(msg)
# if issparse(scvi_data.X):
#     scvi_data.X = scvi_data.X.todense()
scvi_data = make_gene_expression_dataset(X, gene_names)
num_cells, num_genes = scvi_data.X.shape
if known_doublets is not None:
  print('Removing known doublets for in silico doublet generation')
  print('Make sure known doublets are in the same order as your data')
  known_doublets = np.loadtxt(known_doublets, dtype=str) == 'True'
  assert len(known_doublets) == scvi_data.X.shape[0]
  known_doublet_data = make_gene_expression_dataset(
      scvi_data.X[known_doublets],
      scvi_data.gene_names)
  known_doublet_data.labels = np.ones(known_doublet_data.X.shape[0])
  singlet_scvi_data = make_gene_expression_dataset(
      scvi_data.X[~known_doublets],
      scvi_data.gene_names)
  singlet_num_cells, _ = singlet_scvi_data.X.shape
else:
  known_doublet_data = None
  singlet_num_cells = num_cells
  known_doublets = np.zeros(num_cells, dtype=bool)
  singlet_scvi_data = scvi_data
singlet_scvi_data.labels = np.zeros(singlet_scvi_data.X.shape[0])
scvi_data.labels = known_doublets.astype(int)
params = {
"n_hidden": n_hidden,
"n_latent": n_latent,
"cl_hidden": cl_hidden,
"cl_layers": cl_layers,
"dropout_rate": dropout_rate,
"learning_rate": learning_rate,
"valid_pct": valid_pct
}
# set VAE params
vae_params = {}
for par in ['n_hidden', 'n_latent', 'n_layers', 'dropout_rate', 'ignore_batch']:
  if par in params:
      vae_params[par] = params[par]
vae_params['n_batch'] = 0 if params.get(
    'ignore_batch', False) else scvi_data.n_batches
# training parameters
valid_pct = params.get('valid_pct', 0.1)
learning_rate = params.get('learning_rate', 1e-3)
stopping_params = {'patience': params.get('patience', 10), 'threshold': 0}
##################################################
# VAE
vae = VAE(n_input=singlet_scvi_data.nb_genes, n_labels=2,
          reconstruction_loss='nb',
          log_variational=True, **vae_params)
if seed:
    if gpu:
        device = torch.device('cuda')
        vae.load_state_dict(torch.load(os.path.join(seed, 'vae.pt')))
        vae.to(device)
    else:
        map_loc = 'cpu'
        vae.load_state_dict(torch.load(os.path.join(seed, 'vae.pt'),
                                       map_location=map_loc))
    # copy latent representation
    latent_file = os.path.join(seed, 'latent.npy')
    if os.path.isfile(latent_file):
        shutil.copy(latent_file, os.path.join(out_dir, 'latent.npy'))
else:
    stopping_params['early_stopping_metric'] = 'reconstruction_error'
    stopping_params['save_best_state_metric'] = 'reconstruction_error'
    # initialize unsupervised trainer
    utrainer = UnsupervisedTrainer(vae, singlet_scvi_data,
                            train_size=(1. - valid_pct),
                            frequency=2,
                            metrics_to_monitor=['reconstruction_error'],
                            use_cuda=gpu,
                            early_stopping_kwargs=stopping_params)
    utrainer.history['reconstruction_error_test_set'].append(0)
    # initial epoch
    utrainer.train(n_epochs=2000, lr=learning_rate)
    # drop learning rate and continue
    utrainer.early_stopping.wait = 0
    utrainer.train(n_epochs=500, lr=0.5 * learning_rate)
    # save VAE
    torch.save(vae.state_dict(), os.path.join(out_dir, 'vae.pt'))
    # save latent representation
    full_posterior = utrainer.create_posterior(
        utrainer.model,
        singlet_scvi_data,
        indices=np.arange(len(singlet_scvi_data)))
    latent, _, _ = full_posterior.sequential().get_latent()
    np.save(os.path.join(out_dir, 'latent.npy'),
            latent.astype('float32'))
##################################################
# simulate doublets
non_zero_indexes = np.where(singlet_scvi_data.X > 0)
cells = non_zero_indexes[0]
genes = non_zero_indexes[1]
cells_ids = defaultdict(list)
for cell_id, gene in zip(cells, genes):
    cells_ids[cell_id].append(gene)
# choose doublets function type
if doublet_type == 'average':
    doublet_function = create_average_doublet
elif doublet_type == 'sum':
    doublet_function = create_summed_doublet
else:
    doublet_function = create_multinomial_doublet
cell_depths = singlet_scvi_data.X.sum(axis=1)
num_doublets = int(doublet_ratio * singlet_num_cells)
if known_doublet_data is not None:
    num_doublets -= known_doublet_data.X.shape[0]
    # make sure we are making a non negative amount of doublets
    assert num_doublets >= 0
in_silico_doublets = np.zeros((num_doublets, num_genes), dtype='float32')
# for desired # doublets
for di in range(num_doublets):
    # sample two cells
    i, j = np.random.choice(singlet_num_cells, size=2)
    # generate doublets
    in_silico_doublets[di, :] = \
        doublet_function(singlet_scvi_data.X, i, j,
                         doublet_depth=doublet_depth,
                         cell_depths=cell_depths, cells_ids=cells_ids)
# merge datasets
# we can maybe up sample the known doublets
# concatentate
classifier_data = GeneExpressionDataset()
classifier_data.populate_from_data(
    X=np.vstack([scvi_data.X,
                 in_silico_doublets]),
    labels=np.hstack([np.ravel(scvi_data.labels),
                      np.ones(in_silico_doublets.shape[0])]),
    remap_attributes=False)
assert(len(np.unique(classifier_data.labels.flatten())) == 2)
##################################################
# classifier
# model
classifier = Classifier(n_input=(vae.n_latent + 1),
                        n_hidden=params['cl_hidden'],
                        n_layers=params['cl_layers'], n_labels=2,
                        dropout_rate=params['dropout_rate'])
# trainer
stopping_params['early_stopping_metric'] = 'accuracy'
stopping_params['save_best_state_metric'] = 'accuracy'
strainer = ClassifierTrainer(classifier, classifier_data,
                             train_size=(1. - valid_pct),
                             frequency=2, metrics_to_monitor=['accuracy'],
                             use_cuda=gpu,
                             sampling_model=vae, sampling_zl=True,
                             early_stopping_kwargs=stopping_params)
# initial
strainer.train(n_epochs=1000, lr=learning_rate)
# drop learning rate and continue
strainer.early_stopping.wait = 0
strainer.train(n_epochs=300, lr=0.1 * learning_rate)
torch.save(classifier.state_dict(), os.path.join(out_dir, 'classifier.pt'))
##################################################
# post-processing
# use logits for predictions for better results
logits_classifier = Classifier(n_input=(vae.n_latent + 1),
                               n_hidden=params['cl_hidden'],
                               n_layers=params['cl_layers'], n_labels=2,
                               dropout_rate=params['dropout_rate'],
                               logits=True)
logits_classifier.load_state_dict(classifier.state_dict())
# using logits leads to better performance in for ranking
logits_strainer = ClassifierTrainer(logits_classifier, classifier_data,
                                    train_size=(1. - valid_pct),
                                    frequency=2,
                                    metrics_to_monitor=['accuracy'],
                                    use_cuda=gpu,
                                    sampling_model=vae, sampling_zl=True,
                                    early_stopping_kwargs=stopping_params)
# models evaluation mode
vae.eval()
classifier.eval()
logits_classifier.eval()
print('Train accuracy: %.4f' % strainer.train_set.accuracy())
print('Test accuracy:  %.4f' % strainer.test_set.accuracy())
# compute predictions manually
# output logits
train_y, train_score = strainer.train_set.compute_predictions(soft=True)
test_y, test_score = strainer.test_set.compute_predictions(soft=True)
# train_y == true label
# train_score[:, 0] == singlet score; train_score[:, 1] == doublet score
train_score = train_score[:, 1]
train_y = train_y.astype('bool')
test_score = test_score[:, 1]
test_y = test_y.astype('bool')
train_auroc = roc_auc_score(train_y, train_score)
test_auroc = roc_auc_score(test_y, test_score)
print('Train AUROC: %.4f' % train_auroc)
print('Test AUROC:  %.4f' % test_auroc)
train_fpr, train_tpr, train_t = roc_curve(train_y, train_score)
test_fpr, test_tpr, test_t = roc_curve(test_y, test_score)
train_t = np.minimum(train_t, 1 + 1e-9)
test_t = np.minimum(test_t, 1 + 1e-9)
train_acc = np.zeros(len(train_t))
for i in range(len(train_t)):
    train_acc[i] = np.mean(train_y == (train_score > train_t[i]))
test_acc = np.zeros(len(test_t))
for i in range(len(test_t)):
    test_acc[i] = np.mean(test_y == (test_score > test_t[i]))
# write predictions
# softmax predictions
order_y, order_score = strainer.compute_predictions(soft=True)
_, order_pred = strainer.compute_predictions()
doublet_score = order_score[:, 1]
np.save(os.path.join(out_dir, 'softmax_scores.npy'), doublet_score[:num_cells])
np.save(os.path.join(out_dir, 'softmax_scores_sim.npy'), doublet_score[num_cells:])
# logit predictions
logit_y, logit_score = logits_strainer.compute_predictions(soft=True)
logit_doublet_score = logit_score[:, 1]
np.save(os.path.join(out_dir, 'logit_scores.npy'), logit_doublet_score[:num_cells])
np.save(os.path.join(out_dir, 'logit_scores_sim.npy'), logit_doublet_score[num_cells:])
if expected_number_of_doublets is not None:
    solo_scores = doublet_score[:num_cells]
    k = len(solo_scores) - expected_number_of_doublets
    if expected_number_of_doublets / len(solo_scores) > .5:
      print('Make sure you actually expect more than half your cells to be doublets. If not change your -e parameter value')
    assert k > 0
    idx = np.argpartition(solo_scores, k)
    threshold = np.max(solo_scores[idx[:k]])
    is_solo_doublet = doublet_score > threshold
else:
    is_solo_doublet = order_pred[:num_cells]
is_doublet = known_doublets
new_doublets_idx = np.where(~(is_doublet) & is_solo_doublet[:num_cells])[0]
is_doublet[new_doublets_idx] = True
np.save(os.path.join(out_dir, 'is_doublet.npy'), is_doublet[:num_cells])
np.save(os.path.join(out_dir, 'is_doublet_sim.npy'), is_doublet[num_cells:])
np.save(os.path.join(out_dir, 'preds.npy'), order_pred[:num_cells])
np.save(os.path.join(out_dir, 'preds_sim.npy'), order_pred[num_cells:])
if plot:
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    import seaborn as sns
    # plot ROC
    plt.figure()
    plt.plot(train_fpr, train_tpr, label='Train')
    plt.plot(test_fpr, test_tpr, label='Test')
    plt.gca().set_xlabel('False positive rate')
    plt.gca().set_ylabel('True positive rate')
    plt.legend()
    plt.savefig(os.path.join(out_dir, 'roc.pdf'))
    plt.close()
    # plot accuracy
    plt.figure()
    plt.plot(train_t, train_acc, label='Train')
    plt.plot(test_t, test_acc, label='Test')
    plt.axvline(0.5, color='black', linestyle='--')
    plt.gca().set_xlabel('Threshold')
    plt.gca().set_ylabel('Accuracy')
    plt.legend()
    plt.savefig(os.path.join(out_dir, 'accuracy.pdf'))
    plt.close()
    # plot distributions
    plt.figure()
    sns.distplot(test_score[test_y], label='Simulated')
    sns.distplot(test_score[~test_y], label='Observed')
    plt.legend()
    plt.savefig(os.path.join(out_dir, 'train_v_test_dist.pdf'))
    plt.close()
    plt.figure()
    sns.distplot(doublet_score[:num_cells], label='Simulated')
    plt.legend()
    plt.savefig(os.path.join(out_dir, 'real_cells_dist.pdf'))
    plt.close()

import pandas as pd
import anndata
import solo
from solo.utils import create_average_doublet, create_summed_doublet, create_multinomial_doublet, make_gene_expression_dataset
rn=pd.read_csv("/Users/sfurla/Box Sync/PI_FurlanS/computation/Rproj/m3addon/testdata.csv")
rn=list(rn[rn.columns[0]])
d=anndata.read_csv("/Users/sfurla/Box Sync/PI_FurlanS/computation/Rproj/m3addon/testdata.csv")
scvi_data = make_gene_expression_dataset(d.X.transpose(), gene_names=rn)

rm(list=ls())
#reticulate::virtualenv_create(envname = "solo", python="/usr/local/bin/python3")
reticulate::use_python("/Users/sfurla/.virtualenvs/py3/bin/python3", required = T)
library(reticulate)
reticulate::py_config()
py_module_available("trimap")
#py_install("/Users/sfurla/develop/solo")
py_config()

suppressPackageStartupMessages({
  library(monocle3)
  library(m3addon)
  library(reticulate)
  library(openxlsx)  
  library(dplyr)
  library(Matrix)
  library(ggplot2)
  #library(rhdf5)
  library(h5)
  library(xfun)
  library(pals)
  library(RColorBrewer)
  library(piano)
  library(GSEABase)
  library(data.table)
  library(Seurat)
})

cds<-readRDS("/Users/sfurla/Box Sync/PI_FurlanS/computation/Analysis/KpOxCy/cds/191208_DoubletsCalled4methods.RDS")
plot_pc_variance_explained(cds)
debug(trimap)
cds<-trimap(cds, num_dims = 30)
plot_cells(cds, color_cells_by = "group", reduction_method = "trimap",  label_cell_groups = F, cell_size = 0.7)
plot_cells(cds, gene="Nos2", reduction_method = "trimap",  label_cell_groups = F, cell_size = 0.7)

scfurl/m3addon documentation built on Aug. 9, 2021, 5:30 p.m.