diff --git a/.gitignore b/.gitignore index e009e8d..75acbe2 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,4 @@ docs/savefig # Big files example/*.npz +docs/*.npz diff --git a/baredSC/_version.py b/baredSC/_version.py index 1a72d32..b3ddbc4 100644 --- a/baredSC/_version.py +++ b/baredSC/_version.py @@ -1 +1 @@ -__version__ = '1.1.0' +__version__ = '1.1.1' diff --git a/baredSC/baredSC_1d.py b/baredSC/baredSC_1d.py index 720c76d..44bdbcd 100644 --- a/baredSC/baredSC_1d.py +++ b/baredSC/baredSC_1d.py @@ -143,7 +143,9 @@ def plot(oxpdf, x, logprob_values, samples, title, output, data, col_gene, def parse_arguments(args=None): argp = argparse.ArgumentParser( description=("Run mcmc to get the pdf for a given gene using a" - " normal distributions.")) + " normal distributions. The full documentation " + "is available at " + "https://baredsc.readthedocs.io")) argprequired = argp.add_argument_group('Required arguments') argpopt_data = argp.add_argument_group('Optional arguments to select input data') argpopt_mcmc = argp.add_argument_group('Optional arguments to run MCMC') @@ -152,7 +154,8 @@ def parse_arguments(args=None): # Get data: group = argprequired.add_mutually_exclusive_group(required=True) group.add_argument('--input', default=None, - help="Input table with one line per cell" + help="Input table (tabular separated" + " with header) with one line per cell" " columns with raw counts and one column" " nCount_RNA with total number of UMI per cell" " optionally other meta data to filter.") @@ -163,15 +166,15 @@ def parse_arguments(args=None): argpopt_data.add_argument('--metadata1ColName', default=None, help="Name of the column with metadata1 to filter.") argpopt_data.add_argument('--metadata1Values', default=None, - help="Comma separated values for metadata1.") + help="Comma separated values for metadata1 of cells to keep.") argpopt_data.add_argument('--metadata2ColName', default=None, help="Name of the column with metadata2 to filter.") argpopt_data.add_argument('--metadata2Values', default=None, - help="Comma separated values for metadata2.") + help="Comma separated values for metadata2 of cells to keep.") argpopt_data.add_argument('--metadata3ColName', default=None, help="Name of the column with metadata3 to filter.") argpopt_data.add_argument('--metadata3Values', default=None, - help="Comma separated values for metadata3.") + help="Comma separated values for metadata3 of cells to keep.") # MCMC argpopt_mcmc.add_argument('--xmin', default=0, type=float, help="Minimum value to consider in x axis.") @@ -212,7 +215,8 @@ def parse_arguments(args=None): help="Change seed for another output.") argpopt_mcmc.add_argument('--minNeff', default=None, type=float, help="Will redo the MCMC with 10 times more samples until " - "Neff is greater that this value (Default is not set so will not rerun MCMC).") + "the number of effective samples that this value " + "(Default is not set so will not rerun MCMC).") # To save/get MCMC argpopt_mcmc.add_argument('--force', default=None, action='store_true', help="Force to redo the mcmc even if output exists.") diff --git a/baredSC/baredSC_2d.py b/baredSC/baredSC_2d.py index 18aa375..4c2c484 100644 --- a/baredSC/baredSC_2d.py +++ b/baredSC/baredSC_2d.py @@ -231,7 +231,9 @@ def plot(oxpdf, oypdf, x, y, logprob_values, samples, def parse_arguments(args=None): argp = argparse.ArgumentParser( description=("Run mcmc to get the pdf in 2D for 2 given genes using a" - " normal distributions.")) + " normal distributions. The full documentation " + "is available at " + "https://baredsc.readthedocs.io")) argprequired = argp.add_argument_group('Required arguments') argpopt_data = argp.add_argument_group('Optional arguments to select input data') argpopt_mcmc = argp.add_argument_group('Optional arguments to run MCMC') @@ -240,7 +242,8 @@ def parse_arguments(args=None): # To get the data group = argprequired.add_mutually_exclusive_group(required=True) group.add_argument('--input', default=None, - help="Input table with one line per cell" + help="Input table (tabular separated" + " with header) with one line per cell" " columns with raw counts and one column" " nCount_RNA with total number of UMI per cell" " optionally other meta data to filter.") @@ -253,15 +256,15 @@ def parse_arguments(args=None): argpopt_data.add_argument('--metadata1ColName', default=None, help="Name of the column with metadata1 to filter.") argpopt_data.add_argument('--metadata1Values', default=None, - help="Comma separated values for metadata1.") + help="Comma separated values for metadata1 of cells to keep.") argpopt_data.add_argument('--metadata2ColName', default=None, help="Name of the column with metadata2 to filter.") argpopt_data.add_argument('--metadata2Values', default=None, - help="Comma separated values for metadata2.") + help="Comma separated values for metadata2 of cells to keep.") argpopt_data.add_argument('--metadata3ColName', default=None, help="Name of the column with metadata3 to filter.") argpopt_data.add_argument('--metadata3Values', default=None, - help="Comma separated values for metadata3.") + help="Comma separated values for metadata3 of cells to keep.") # MCMC argpopt_mcmc.add_argument('--xmin', default=0, type=float, help="Minimum value to consider in x axis.") @@ -323,7 +326,8 @@ def parse_arguments(args=None): help="Change seed for another output.") argpopt_mcmc.add_argument('--minNeff', default=None, type=float, help="Will redo the MCMC with 10 times more samples until " - "Neff is greater that this value (Default is not set so will not rerun MCMC).") + "the number of effective samples that this value " + "(Default is not set so will not rerun MCMC).") # To save/get MCMC argpopt_mcmc.add_argument('--force', default=None, action='store_true', help="Force to redo the mcmc even if output exists.") diff --git a/baredSC/combineMultipleModels_1d.py b/baredSC/combineMultipleModels_1d.py index 6dc9296..05a3650 100644 --- a/baredSC/combineMultipleModels_1d.py +++ b/baredSC/combineMultipleModels_1d.py @@ -88,7 +88,8 @@ def parse_arguments(args=None): # Get data: group = argprequired.add_mutually_exclusive_group(required=True) group.add_argument('--input', default=None, - help="Input table with one line per cell" + help="Input table (tabular separated" + " with header) with one line per cell" " columns with raw counts and one column" " nCount_RNA with total number of UMI per cell" " optionally other meta data to filter.") @@ -99,15 +100,15 @@ def parse_arguments(args=None): argpopt_data.add_argument('--metadata1ColName', default=None, help="Name of the column with metadata1 to filter.") argpopt_data.add_argument('--metadata1Values', default=None, - help="Comma separated values for metadata1.") + help="Comma separated values for metadata1 of cells to keep.") argpopt_data.add_argument('--metadata2ColName', default=None, help="Name of the column with metadata2 to filter.") argpopt_data.add_argument('--metadata2Values', default=None, - help="Comma separated values for metadata2.") + help="Comma separated values for metadata2 of cells to keep.") argpopt_data.add_argument('--metadata3ColName', default=None, help="Name of the column with metadata3 to filter.") argpopt_data.add_argument('--metadata3Values', default=None, - help="Comma separated values for metadata3.") + help="Comma separated values for metadata3 of cells to keep.") # MCMC argprequired.add_argument('--outputs', default=None, required=True, nargs='+', help="Ouput files basename (will be npz)" diff --git a/baredSC/combineMultipleModels_2d.py b/baredSC/combineMultipleModels_2d.py index f3a381e..7fc7da7 100644 --- a/baredSC/combineMultipleModels_2d.py +++ b/baredSC/combineMultipleModels_2d.py @@ -126,7 +126,8 @@ def parse_arguments(args=None): # Get data: group = argprequired.add_mutually_exclusive_group(required=True) group.add_argument('--input', default=None, - help="Input table with one line per cell" + help="Input table (tabular separated" + " with header) with one line per cell" " columns with raw counts and one column" " nCount_RNA with total number of UMI per cell" " optionally other meta data to filter.") @@ -139,15 +140,15 @@ def parse_arguments(args=None): argpopt_data.add_argument('--metadata1ColName', default=None, help="Name of the column with metadata1 to filter.") argpopt_data.add_argument('--metadata1Values', default=None, - help="Comma separated values for metadata1.") + help="Comma separated values for metadata1 of cells to keep.") argpopt_data.add_argument('--metadata2ColName', default=None, help="Name of the column with metadata2 to filter.") argpopt_data.add_argument('--metadata2Values', default=None, - help="Comma separated values for metadata2.") + help="Comma separated values for metadata2 of cells to keep.") argpopt_data.add_argument('--metadata3ColName', default=None, help="Name of the column with metadata3 to filter.") argpopt_data.add_argument('--metadata3Values', default=None, - help="Comma separated values for metadata3.") + help="Comma separated values for metadata3 of cells to keep.") # MCMC argprequired.add_argument('--outputs', default=None, required=True, nargs='+', help="Ouput files basename (will be npz)" diff --git a/docs/content/baredSC_api.rst b/docs/content/baredSC_api.rst new file mode 100644 index 0000000..362fa1e --- /dev/null +++ b/docs/content/baredSC_api.rst @@ -0,0 +1,418 @@ +Use baredSC within python +========================= + +.. contents:: + :local: + +This part is not a proper documentation of baredSC as a package as we are missing a lot of documentation of our methods. +It just gives indications on how it can be used. + +baredSC_1d +---------- + +Use the ``npz`` content +^^^^^^^^^^^^^^^^^^^^^^^ + +baredSC is mainly used with the command line interface. +By default, only the numpy compressed ``.npz`` file is output, +but if the ``--figure`` argument is used, it outputs much more. +All the outputs are described in the :doc:`outputs` page. + +We provide two examples of plots using the text outputs in :doc:`tuto/tutorial_sim_compare_means` and :doc:`tuto/tutorial_sim_custom_simple_plot`. +However, sometimes the user may want to plot information which is not part of the text outputs. +We describe here a script which will use the baredSC_1d output to plot a custom error bars. + +First we get the value of parameters at each step of MCMC: + +.. ipython:: + + In [1]: import numpy as np + ...: import baredSC.oned + ...: import matplotlib.pyplot as plt + ...: + ...: output_baredSC = "../example/first_example_1d_2gauss.npz" + ...: + ...: # First option, use the function extract_from_npz + ...: mu, cov, ox, oxpdf, x, logprob_values, samples = \ + ...: baredSC.oned.extract_from_npz(output_baredSC) + ...: # mu is the mean of each parameter, + ...: # cov is the covariance of parameters + ...: # ox is the oversampled x to compute the poisson noise pdf + ...: # oxpdf is the oversampled x to compute the pdf + ...: # x is the x used to compute the likelihood + ...: # logprob_values is the value of log likelihood at each step fo the MCMC + ...: # samples is the value of each parameter at each step of the MCMC + ...: + ...: # Second option, get the samples and x, oxpdf from the npz: + ...: mcmc_res = np.load(output_baredSC, allow_pickle=True) + ...: samples = mcmc_res['samples'] + ...: x = mcmc_res['x'] + ...: oxpdf = mcmc_res['oxpdf'] + ...: + ...: # From the sample size we deduce the number of Gaussians + ...: nnorm = (samples.shape[1] + 1) // 3 + ...: # We display the parameter names: + ...: p_names = [f'{pn}{i}' for i in range(nnorm) for pn in ['amp', 'mu', 'scale']][1:] + ...: print(f'The parameters are: {p_names}') + ...: + +Then we compute the pdf for each step of the MCMC and we plot the pdf with the custom error bars: + +.. ipython:: + + @savefig plot_from_npz1d.png + In [2]: # We assume x is equally spaced + ...: dx = x[1] - x[0] + ...: nx = x.size + ...: noxpdf = oxpdf.size + ...: # We assume oxpdf is equally spaced + ...: odxpdf = oxpdf[1] - oxpdf[0] + ...: + ...: # Compute the pdf for each sample + ...: # This can be long + ...: pdf = np.array([baredSC.oned.get_pdf(p, nx, noxpdf, oxpdf, odxpdf) + ...: for p in samples]) + ...: + ...: xmin = x[0] - dx / 2 + ...: xmax = x[-1] + dx / 2 + ...: + ...: my_custom_quantiles = {'0.3': [0.25, 0.75], '0.1': [0.1, 0.9]} + ...: + ...: plt.figure() + ...: for alpha in my_custom_quantiles: + ...: pm = np.quantile(pdf, my_custom_quantiles[alpha][0], axis=0) + ...: pp = np.quantile(pdf, my_custom_quantiles[alpha][1], axis=0) + ...: plt.fill_between(x, pm, pp, color='g', alpha=float(alpha), + ...: rasterized=True) + ...: # Mean + ...: plt.plot(x, np.mean(pdf, axis=0), 'r', lw=2, rasterized=True) + ...: + +Run baredSC_1d +^^^^^^^^^^^^^^ + +You can also run the MCMC from python directly. + +However, it requires formating of the input: + +.. ipython:: + + In [1]: import numpy as np + ...: import pandas as pd + ...: from scipy.stats import lognorm, truncnorm, poisson + ...: from baredSC.baredSC_1d import gauss_mcmc + ...: + ...: # I generate 200 cells with normal expression at 1.5 with scale of 0.2 + ...: # In the Seurat scale (log(1 + 10^4 X)) + ...: n_cells = 200 + ...: cur_loc = 1.5 + ...: cur_scale = 0.2 + ...: N = lognorm.rvs(s=0.3, scale=16000, size=n_cells, random_state=1).astype(int) + ...: expression = truncnorm.rvs(- cur_loc / cur_scale, np.inf, + ...: loc=cur_loc, scale=cur_scale, + ...: size=n_cells, + ...: random_state=2) + ...: + ...: ks = poisson.rvs(mu=N * 1e-4 * (np.exp(expression) - 1), + ...: random_state=3) + ...: + ...: # I need to put the ks and the N in a data frame: + ...: # The column containing the total number of UMI per cell + ...: # must be 'nCount_RNA' + ...: data = pd.DataFrame({'my_gene': ks, 'nCount_RNA': N}) + ...: + +Then the actual MCMC can be run with: + +.. ipython:: + + In [2]: results = gauss_mcmc(data=data, + ...: col_gene='my_gene', # Put here the colname you put in your data + ...: nx=50, # Number of bins in x + ...: osampx=10, # Oversampling factor of the Poisson distribution + ...: osampxpdf=5, # Oversampling factor of the PDF + ...: xmin=0, + ...: xmax=3, + ...: min_scale=0.1, # Minimal value of the scale + ...: xscale="Seurat", + ...: target_sum=10000, + ...: nnorm=1, # We use models with a single Gaussian + ...: nsamples_mcmc=100000, # Number of steps in the MCMC + ...: nsamples_burn=25000, # Number of steps in the burning phase of MCMC (we recommand nsampMCMC / 4) + ...: nsplit_burn=10, # The burning phase is splitted in multiple sub-phase where the temperature is decreasing + ...: T0_burn=100.0, + ...: output='temp', # Where the npz output should be stored + ...: seed=1) + ...: print(f'results contains {len(results)} items.') + ...: # The results are: + ...: # mu, cov, ox, oxpdf, x, logprob_values, samples + ...: # mu is the mean of each parameter, + ...: # cov is the covariance of parameters + ...: # ox is the oversampled x to compute the poisson noise pdf + ...: # oxpdf is the oversampled x to compute the pdf + ...: # x is the x used to compute the likelihood + ...: # logprob_values is the value of log likelihood at each step fo the MCMC + ...: # samples is the value of each parameter at each step of the MCMC + ...: + + +baredSC_2d +---------- + +Use the ``npz`` content +^^^^^^^^^^^^^^^^^^^^^^^ + +baredSC is mainly used with the command line interface. +By default, only the numpy compressed ``.npz`` file is output, +but if the ``--figure`` argument is used, it outputs much more. +All the outputs are described in the :doc:`outputs` page. + +We provide an example of a plot using the text output ``_pdf2d.txt`` in :doc:`tuto/tutorial_sim_custom_simple_plot`. +However, sometimes the user may want to plot information which is not part of the text outputs. +We describe here a script which will use the baredSC_2d output to plot the mean and median on the same plot and +another script which will use more bins in the output to get smoother results. + +First we get the value of parameters at each step of MCMC: + +.. ipython:: + + In [1]: import numpy as np + ...: import baredSC.twod + ...: import matplotlib.pyplot as plt + ...: + ...: output_baredSC = "../example/second_example_2d_cellgroup1_1gauss_nx20.npz" + ...: + ...: # First option, use the function extract_from_npz + ...: mu, cov, ox, oy, oxpdf, oypdf, x, y, \ + ...: logprob_values, samples = \ + ...: baredSC.twod.extract_from_npz(output_baredSC) + ...: # mu is the mean of each parameter, + ...: # cov is the covariance of parameters + ...: # ox, oy are the oversampled x, y to compute the poisson noise pdf + ...: # oxpdf, oypdf are the oversampled x, y to compute the pdf + ...: # x, y are the x, y used to compute the likelihood + ...: # logprob_values is the value of log likelihood at each step fo the MCMC + ...: # samples is the value of each parameter at each step of the MCMC + ...: + ...: # Second option, get the samples and x, y, oxpdf, oypdf, samples from the npz: + ...: mcmc_res = np.load(output_baredSC, allow_pickle=True) + ...: samples = mcmc_res['samples'] + ...: x = mcmc_res['x'] + ...: y = mcmc_res['y'] + ...: oxpdf = mcmc_res['oxpdf'] + ...: oypdf = mcmc_res['oypdf'] + ...: + ...: # From the sample size we deduce the number of Gaussians + ...: nnorm = (samples.shape[1] + 1) // 6 + ...: # We display the parameter names: + ...: p_names = [f'{pn}{i}' for i in range(nnorm) + ...: for pn in ['xy_amp', 'xy_mux', 'xy_muy', 'xy_scalex', + ...: 'xy_scaley', 'xy_corr']][1:] + ...: print(f'The parameters are: {p_names}') + ...: + +Then we compute the pdf for each step of the MCMC and we plot the mean and median: + +.. ipython:: + + @savefig plot_from_npz2d.png + In [1]: # We assume x and y are equally spaced + ...: dx = x[1] - x[0] + ...: nx = x.size + ...: dy = y[1] - y[0] + ...: ny = y.size + ...: noxpdf = oxpdf.size + ...: # We assume oxpdf is equally spaced + ...: odxpdf = oxpdf[1] - oxpdf[0] + ...: + ...: noypdf = oypdf.size + ...: # We assume oypdf is equally spaced + ...: odypdf = oypdf[1] - oypdf[0] + ...: + ...: odxypdf = odxpdf * odypdf + ...: oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0) + ...: + ...: # Compute the pdf for each sample + ...: # This can be long + ...: pdf = np.array([baredSC.twod.get_pdf(p, nx, ny, noxpdf, + ...: noypdf, oxypdf, odxypdf) + ...: for p in samples]) + ...: # We plot: + ...: xmin = x[0] - dx / 2 + ...: xmax = x[-1] + dx / 2 + ...: ymin = y[0] - dy / 2 + ...: ymax = y[-1] + dy / 2 + ...: + ...: x_borders = np.linspace(xmin, xmax, len(x) + 1) + ...: y_borders = np.linspace(ymin, ymax, len(y) + 1) + ...: + ...: # Plot 2 panels plot + ...: fig, axs = plt.subplots(1, 2, sharex='row', sharey='row') + ...: axs[0].pcolormesh(x_borders, y_borders, np.mean(pdf, axis=0), + ...: shading='flat', rasterized=True, cmap='Greys') + ...: axs[0].set_xlabel('gene_x') + ...: axs[0].set_ylabel('gene_y') + ...: axs[1].pcolormesh(x_borders, y_borders, np.median(pdf, axis=0), + ...: shading='flat', rasterized=True, cmap='Greys') + ...: axs[1].set_xlabel('gene_x') + ...: axs[1].set_ylabel('gene_y') + ...: + +If you want to get more bins, you just need to change x and y. +We want to warn the user that what will be plotted will be different from +what was used for the likelihood evaluation: + +.. code:: python + + In [1]: # We assume x and y are equally spaced + ...: dx = x[1] - x[0] + ...: dy = y[1] - y[0] + ...: xmin = x[0] - dx / 2 + ...: xmax = x[-1] + dx / 2 + ...: ymin = y[0] - dy / 2 + ...: ymax = y[-1] + dy / 2 + ...: + ...: # We set pretty_bins_x and y + ...: pretty_bins_x = 50 + ...: pretty_bins_y = 50 + ...: from baredSC.common import get_bins_centers + ...: nx = pretty_bins_x + ...: x = get_bins_centers(xmin, xmax, nx) + ...: dx = x[1] - x[0] + ...: noxpdf = nx + ...: oxpdf = x + ...: odxpdf = dx + ...: ny = pretty_bins_y + ...: y = get_bins_centers(ymin, ymax, ny) + ...: dy = y[1] - y[0] + ...: noypdf = ny + ...: oypdf = y + ...: odypdf = dy + ...: + ...: odxypdf = odxpdf * odypdf + ...: oxypdf = np.array(np.meshgrid(oxpdf, oypdf)).transpose(1, 2, 0) + ...: + ...: # Compute the pdf for each sample + ...: # This can be long + ...: pdf = np.array([baredSC.twod.get_pdf(p, nx, ny, noxpdf, + ...: noypdf, oxypdf, odxypdf) + ...: for p in samples]) + ...: # We plot: + ...: x_borders = np.linspace(xmin, xmax, len(x) + 1) + ...: y_borders = np.linspace(ymin, ymax, len(y) + 1) + ...: + ...: + ...: # Plot 2 panels plot + ...: fig, axs = plt.subplots(1, 2, sharex='row', sharey='row') + ...: axs[0].pcolormesh(x_borders, y_borders, np.mean(pdf, axis=0), + ...: shading='flat', rasterized=True, cmap='Greys') + ...: axs[0].set_xlabel('gene_x') + ...: axs[0].set_ylabel('gene_y') + ...: axs[1].pcolormesh(x_borders, y_borders, np.median(pdf, axis=0), + ...: shading='flat', rasterized=True, cmap='Greys') + ...: axs[1].set_xlabel('gene_x') + ...: axs[1].set_ylabel('gene_y') + ...: + +.. image:: ../smooth.png + + +Run baredSC_2d +^^^^^^^^^^^^^^ + +You can also run the MCMC from python directly. + +However, it requires formating of the input: + +.. ipython:: + + In [1]: import numpy as np + ...: import pandas as pd + ...: from scipy.stats import lognorm, truncnorm, poisson + ...: from baredSC.baredSC_2d import gauss_mcmc + ...: from baredSC.twod import trunc_norm2d + ...: + ...: + ...: def trunc_norm_2d(mu, sigma, corr, size, seed): + ...: try: + ...: rng = np.random.default_rng(seed) + ...: except AttributeError: + ...: # For older numpy versions: + ...: np.random.seed(seed) + ...: rng = np.random + ...: cov = np.array([[sigma[0] * sigma[0], sigma[0] * sigma[1] * corr], + ...: [sigma[0] * sigma[1] * corr, sigma[1] * sigma[1]]]) + ...: values = rng.multivariate_normal(mu, cov, size) + ...: mask_0 = [v[0] < 0 or v[1] < 0 for v in values] + ...: # Because we want only positive expression: + ...: while sum(mask_0) > 0: + ...: values[mask_0] = rng.multivariate_normal(mu, cov, sum(mask_0)) + ...: mask_0 = [v[0] < 0 or v[1] < 0 for v in values] + ...: return(values) + ...: + ...: + ...: # I generate 200 cells with normal expression at 1.5 with scale of 0.2 with correlation of 0.5 + ...: # In the Seurat scale (log(1 + 10^4 X)) + ...: n_cells = 200 + ...: cur_mu = [1.5, 1.5] + ...: cur_sigma = [0.2, 0.2] + ...: cur_corr = 0.5 + ...: N = lognorm.rvs(s=0.3, scale=16000, size=n_cells, random_state=1).astype(int) + ...: expression = trunc_norm_2d(mu=cur_mu, sigma=cur_sigma, + ...: corr=cur_corr, + ...: size=n_cells, + ...: seed=2) + ...: exp_values_x, exp_values_y = np.transpose(expression) + ...: ks_x = poisson.rvs(mu=N * 1e-4 * (np.exp(exp_values_x) - 1), + ...: random_state=3) + ...: ks_y = poisson.rvs(mu=N * 1e-4 * (np.exp(exp_values_y) - 1), + ...: random_state=4) + ...: + ...: # I need to put the ks and the N in a data frame: + ...: # The column containing the total number of UMI per cell + ...: # must be 'nCount_RNA' + ...: data = pd.DataFrame({'my_gene_x': ks_x, + ...: 'my_gene_y': ks_y, + ...: 'nCount_RNA': N}) + ...: + +Then the actual MCMC can be run with: + +.. ipython:: + + In [2]: results = gauss_mcmc(data=data, + ...: genex='my_gene_x', # Put here the colname you put in your data + ...: geney='my_gene_y', # Put here the colname you put in your data + ...: nx=20, # Number of bins in x + ...: osampx=10, # Oversampling factor of the Poisson distribution + ...: osampxpdf=5, # Oversampling factor of the PDF + ...: xmin=0, + ...: xmax=3, + ...: ny=20, # Number of bins in y + ...: osampy=10, # Oversampling factor of the Poisson distribution + ...: osampypdf=5, # Oversampling factor of the PDF + ...: ymin=0, + ...: ymax=3, + ...: min_scale_x=0.1, # Minimal value of the scale in x + ...: min_scale_y=0.1, # Minimal value of the scale in y + ...: scale_prior=0.3, # Scale of the truncnorm used in the prior for the correlation + ...: scale="Seurat", + ...: target_sum=10000, + ...: nnorm=1, # We use models with a single Gaussian + ...: nsamples_mcmc=100000, # Number of steps in the MCMC + ...: nsamples_burn=25000, # Number of steps in the burning phase of MCMC (we recommand nsampMCMC / 4) + ...: nsplit_burn=10, # The burning phase is splitted in multiple sub-phase where the temperature is decreasing + ...: T0_burn=100.0, + ...: output='temp', # Where the npz output should be stored + ...: seed=1) + ...: print(f'results contains {len(results)} items.') + ...: # The results are: + ...: # mu, cov, ox, oy, oxpdf, oypdf, x, y, \ + ...: # logprob_values, samples + ...: # mu is the mean of each parameter, + ...: # cov is the covariance of parameters + ...: # ox, oy are the oversampled x, y to compute the poisson noise pdf + ...: # oxpdf, oypdf are the oversampled x, y to compute the pdf + ...: # x, y are the x, y used to compute the likelihood + ...: # logprob_values is the value of log likelihood at each step fo the MCMC + ...: # samples is the value of each parameter at each step of the MCMC diff --git a/docs/content/outputs.rst b/docs/content/outputs.rst index dbc3bec..0c7a036 100644 --- a/docs/content/outputs.rst +++ b/docs/content/outputs.rst @@ -12,6 +12,10 @@ MCMC output The only output by defaut is a numpy compressed ``.npz`` file. This output contains the result of the MCMC: * samples: the value of the parameter at each step of the MCMC + + * In the 1d case, the parameters are mu0, scale0, (amp1, mu1, scale1, ...). The first amplitude can be deduced as 1 minus the sum of all other amplitudes. + * In the 2d case, the parameters are mux0, scalex0, muy0, scaley0, corr0, (amp1, mux1, scalex1, muy1, scaley1, corr1, ..). The first amplitude can be deduced as 1 minus the sum of all other amplitudes. + * diagnostics: a dictionary with the diagnostics at each step of the MCMC, among them: * logprob: the log probability at each step of the MCMC @@ -59,7 +63,7 @@ name.extension This is the figure with the results. -- When the 1d version is used, it displays the mean pdf in solid red line, the median in black dashed lines (/!\ the integral of the median is not equal to 1) with the confidence interval of 1 sigma (68%), 2 sigma (95%) and 3 sigma (99.7%) as well as in green, the kernel density estimate of the input values, the detected expression (``log(1 + 10^4 * raw / total UMI)``). +- When the 1d version is used, it displays the mean pdf in solid red line, the median in black dashed lines (/!\backslash the integral of the median is not equal to 1) with the confidence interval of 1 sigma (68%), 2 sigma (95%) and 3 sigma (99.7%) as well as in green, the kernel density estimate of the input values, the detected expression (``log(1 + 10^4 * raw / total UMI)``). - When the 2d version is used, it displays the pdf as a heatmap as well as a projection on the x and y axis. On the projection, the confidence interval 68% is indicated as a shaded area as well as the mean with a solid red line and the median with a dashed black line. On the top right corner, the correlation is indicated with the confidence interval 68% as well as a confidence interval on the one-sided p-value (the probability that the correlation is the opposite sign of the mean, one sigma confidence interval). diff --git a/docs/content/releases.rst b/docs/content/releases.rst index ef9d061..ed3e531 100644 --- a/docs/content/releases.rst +++ b/docs/content/releases.rst @@ -1,6 +1,17 @@ Releases ======== +1.1.1 +----- + +Improvements: +^^^^^^^^^^^^^ + +- The online documentation has been improved. + +- More information obtained by ``--help``. + + 1.1.0 ----- diff --git a/docs/content/tuto/tutorial_sim_change_minScale.rst b/docs/content/tuto/tutorial_sim_change_minScale.rst index 46a4146..6749a02 100644 --- a/docs/content/tuto/tutorial_sim_change_minScale.rst +++ b/docs/content/tuto/tutorial_sim_change_minScale.rst @@ -11,6 +11,7 @@ We took total UMI counts from a real dataset of NIH3T3. We generated a example where 2 genes have the same distribution (2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1). Half of cells goes in each gaussian. The gene is called "0.5_0_0_0.5_x". +The input table can be downloaded from `here `_. minScale -------- diff --git a/docs/content/tuto/tutorial_sim_change_nx.rst b/docs/content/tuto/tutorial_sim_change_nx.rst index ba63f5e..19f74a1 100644 --- a/docs/content/tuto/tutorial_sim_change_nx.rst +++ b/docs/content/tuto/tutorial_sim_change_nx.rst @@ -12,6 +12,7 @@ Inputs We took total UMI counts from a real dataset of NIH3T3. We generated a example where the PDF of the 2 genes is a 2D Gaussian. The mean on each axis and the scale on each axis is equal to 0.5 and the correlation value is also 0.5. +The input table can be downloaded from `here `_. Run baredSC in 2D ----------------- diff --git a/docs/content/tuto/tutorial_sim_change_scalePrior.rst b/docs/content/tuto/tutorial_sim_change_scalePrior.rst index f11376d..b852bc0 100644 --- a/docs/content/tuto/tutorial_sim_change_scalePrior.rst +++ b/docs/content/tuto/tutorial_sim_change_scalePrior.rst @@ -7,6 +7,7 @@ Change scalePrior baredSC_2d uses as a prior on the correlation value of each Gaussian a normal distribution. In order to reduce the number of false-positive (anti-)correlation detection. The scale of the normal distribution is set to 0.3. We show here the influence of this prior. +The input table can be downloaded from `here `_. Inputs ------ diff --git a/docs/content/tuto/tutorial_sim_custom_simple_plot.rst b/docs/content/tuto/tutorial_sim_custom_simple_plot.rst new file mode 100644 index 0000000..a389eb2 --- /dev/null +++ b/docs/content/tuto/tutorial_sim_custom_simple_plot.rst @@ -0,0 +1,79 @@ +Customize your baredSC plots +============================ + +.. contents:: + :local: + +When ``--figure`` is used. baredSC generates plots and output text tables. +To use baredSC results in your publication, you may want to customize your plots. +Here is an example of python script you can use to customize your plots. + +baredSC_1d +---------- + +We assume you ran the :doc:`tutorial_sim_default_1d`. + +We will use matplotlib to display the pdf with the error bar: + +.. ipython:: + + @savefig custom_1d.png + In [1]: import numpy as np + ...: import pandas as pd + ...: import matplotlib.pyplot as plt + ...: # Get the pdf + ...: pdf = pd.read_csv("../example/first_example_1d_1-3gauss_pdf.txt", + ...: sep="\t") + ...: x = pdf['x'].to_numpy() + ...: # We assume x is equally spaced + ...: dx = x[1] - x[0] + ...: xmin = x[0] - dx / 2 + ...: xmax = x[-1] + dx / 2 + ...: # Plot PDF with 1 sigma + ...: plt.figure() + ...: plt.fill_between(x, pdf['low'].to_numpy(), pdf['high'].to_numpy(), + ...: color='g', alpha=0.3, rasterized=True) + ...: # Mean + ...: plt.plot(x, pdf['mean'].to_numpy(), 'g', lw=2, rasterized=True) + ...: plt.plot(x, pdf['median'].to_numpy(), 'k--', lw=2, rasterized=True) + ...: plt.xlim(xmin, xmax) + ...: plt.ylim(0, ) + ...: plt.xlabel('log(1 + 10000 * expression)') + ...: plt.ylabel('PDF') + ...: plt.title('My title') + + +baredSC_2d +---------- + +We assume you ran the :doc:`tutorial_sim_default_2d`. + +We will use matplotlib to display the mean pdf in 2d: + +.. ipython:: + + @savefig custom_2d.png + In [1]: import numpy as np + ...: import matplotlib.pyplot as plt + ...: # Get the pdf + ...: pdf = np.loadtxt("../example/first_example_2d_cellgroup1_1-3gauss_pdf2d.txt", + ...: delimiter="\t", dtype='S30') + ...: x = pdf[0, 1:].astype('float') + ...: y = pdf[1:, 0].astype('float') + ...: # We assume x and y is equally spaced + ...: dx = x[1] - x[0] + ...: xmin = x[0] - dx / 2 + ...: xmax = x[-1] + dx / 2 + ...: x_borders = np.linspace(xmin, xmax, len(x) + 1) + ...: dy = y[1] - y[0] + ...: ymin = y[0] - dy / 2 + ...: ymax = y[-1] + dy / 2 + ...: y_borders = np.linspace(ymin, ymax, len(y) + 1) + ...: plt.figure() + ...: plt.pcolormesh(x_borders, y_borders, pdf[1:,1:].astype('float'), + ...: shading='flat', rasterized=True, cmap='viridis') + ...: plt.xlabel('log(1 + 10000 * genex)') + ...: plt.ylabel('log(1 + 10000 * geney)') + ...: plt.title('My title') + ...: + diff --git a/docs/content/tuto/tutorial_sim_default_1d.rst b/docs/content/tuto/tutorial_sim_default_1d.rst index 2c93871..28621bb 100644 --- a/docs/content/tuto/tutorial_sim_default_1d.rst +++ b/docs/content/tuto/tutorial_sim_default_1d.rst @@ -15,6 +15,7 @@ We took total UMI counts from a real dataset of NIH3T3. We generated a example where 2 genes have the same distribution (2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1). Half of cells goes in each gaussian. The gene is called "0.5_0_0_0.5_x". +The input table can be downloaded from `here `_. Run --- @@ -44,7 +45,7 @@ We first check the convergence: .. image:: ../../../example/first_example_1d_1gauss_convergence.png This plot show the autocorrelation as a function of number of samples. -The earlier the curves goes close to 0, the more it convereged. +The earlier the curves goes close to 0, the more it converged. Here, this is perfect. diff --git a/docs/content/tuto/tutorial_sim_default_2d.rst b/docs/content/tuto/tutorial_sim_default_2d.rst index 6aad1de..cd4aecd 100644 --- a/docs/content/tuto/tutorial_sim_default_2d.rst +++ b/docs/content/tuto/tutorial_sim_default_2d.rst @@ -12,6 +12,7 @@ We generated a example where 2 genes have the same distribution (2 gaussians, one of mean 0.375, scale 0.125 and another one of mean 1 and scale 0.1). For each gene, half of cells goes in each gaussian. The genes are called "0.5_0_0_0.5_x" and "0.5_0_0_0.5_y". +The input table can be downloaded from `here `_. 2d -- diff --git a/docs/content/tutorial_sim.rst b/docs/content/tutorial_sim.rst index 843f6da..c54faf4 100644 --- a/docs/content/tutorial_sim.rst +++ b/docs/content/tutorial_sim.rst @@ -7,6 +7,7 @@ Tutorial on simulated data tuto/tutorial_sim_default_1d.rst tuto/tutorial_sim_default_2d.rst + tuto/tutorial_sim_custom_simple_plot.rst tuto/tutorial_sim_compare_means.rst tuto/tutorial_sim_change_minScale.rst tuto/tutorial_sim_change_nx.rst diff --git a/docs/index.rst b/docs/index.rst index 4d75551..46da7b0 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,6 +11,12 @@ BARED (Bayesian Approach to Retreive Expression Distribution of) Single Cell baredSC is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data. It uses the raw counts and the total number of UMI for each cell. The PDF is approximated by a number of 1d or 2d gaussians provided by the user. The likelihood is estimated using the asumption that the raw counts follow a Poisson distribution of parameter equal to the proportion of mRNA for the gene in the cell multiplied by the total number of UMI identified in this cell. +We encourage users which are not familiar with MCMC to start with the :doc:`content/installation` page to install the baredSC package. Then, follow the tutorial for a single gene (:doc:`content/tuto/tutorial_sim_default_1d`) which is a step by step example of application using an input provided on the github repository with default parameters. The other tutorials describe influences of some parameters or use a 2d example. + +Users familiar with MCMC could directly go to the :doc:`content/usage` page and read the :doc:`content/outputs` page. + +Advanced users who wants to use baredSC as a package within python should go to the :doc:`content/baredSC_api`. + .. toctree:: :maxdepth: 2 :caption: Contents: @@ -19,6 +25,7 @@ baredSC is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence content/usage.rst content/outputs.rst content/tutorial_sim.rst + content/baredSC_api.rst content/releases.rst content/citation.rst diff --git a/docs/smooth.png b/docs/smooth.png new file mode 100644 index 0000000..ee028b0 Binary files /dev/null and b/docs/smooth.png differ diff --git a/example/first_example_1d_2gauss.npz b/example/first_example_1d_2gauss.npz new file mode 100644 index 0000000..f581080 Binary files /dev/null and b/example/first_example_1d_2gauss.npz differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20.npz b/example/second_example_2d_cellgroup1_1gauss_nx20.npz new file mode 100644 index 0000000..fb77bda Binary files /dev/null and b/example/second_example_2d_cellgroup1_1gauss_nx20.npz differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20.png b/example/second_example_2d_cellgroup1_1gauss_nx20.png index f4ff7f4..9a600d8 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20.png and b/example/second_example_2d_cellgroup1_1gauss_nx20.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_convergence.png b/example/second_example_2d_cellgroup1_1gauss_nx20_convergence.png index fab076f..bef8ec9 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20_convergence.png and b/example/second_example_2d_cellgroup1_1gauss_nx20_convergence.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png b/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png index 9741d5a..b36bba5 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png and b/example/second_example_2d_cellgroup1_1gauss_nx20_corner.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt index 99ce340..86ac89f 100644 --- a/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt +++ b/example/second_example_2d_cellgroup1_1gauss_nx20_corr.txt @@ -1,2 +1,2 @@ mean median low high pval error -0.3361626418003792 0.33709430340111035 0.23710432048758964 0.4355111563234078 0.0004674903797985895 0.000387276275663326 +0.3361626529481973 0.33709431583650323 0.2371043332203131 0.43551115964745624 0.0004674903807069359 0.0003872762765878134 diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png b/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png index e3fb7c7..c7f2c01 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png and b/example/second_example_2d_cellgroup1_1gauss_nx20_individuals.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_median.png b/example/second_example_2d_cellgroup1_1gauss_nx20_median.png index acd2b91..0c567ad 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20_median.png and b/example/second_example_2d_cellgroup1_1gauss_nx20_median.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt index 4c46f82..1c6ac22 100644 --- a/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt +++ b/example/second_example_2d_cellgroup1_1gauss_nx20_neff.txt @@ -1 +1 @@ -3116.9575806460402 +3116.957571821069 diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_p.png b/example/second_example_2d_cellgroup1_1gauss_nx20_p.png index 1e134aa..109fe8a 100644 Binary files a/example/second_example_2d_cellgroup1_1gauss_nx20_p.png and b/example/second_example_2d_cellgroup1_1gauss_nx20_p.png differ diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt index 668af80..f9a8d0c 100644 --- a/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt +++ b/example/second_example_2d_cellgroup1_1gauss_nx20_p.txt @@ -1,6 +1,6 @@ name low median high -xy_mux0 0.4563448378360943 0.5652508914382371 0.6449632865048556 -xy_muy0 0.5110438188304344 0.5851675836852515 0.6416991845981193 -xy_scalex0 0.47650078031570364 0.5359655188155821 0.6103637891270763 -xy_scaley0 0.3738519777605548 0.41982471706798763 0.4752504563701382 -xy_corr0 0.3188055789591601 0.4395793906818417 0.5513853745476183 +xy_mux0 0.4563448497272519 0.565250881653337 0.6449633094212769 +xy_muy0 0.5110438228953402 0.5851675895665642 0.6416991842927675 +xy_scalex0 0.4765007741343093 0.5359655186057848 0.6103637885351297 +xy_scaley0 0.37385197743543686 0.41982471397262777 0.4752504636302334 +xy_corr0 0.31880559579690065 0.43957940301761694 0.5513853878754051 diff --git a/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt index c73736b..fc50fae 100644 --- a/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt +++ b/example/second_example_2d_cellgroup1_1gauss_nx20_pdf2d.txt @@ -1,21 +1,21 @@ xy 0.0625 0.1875 0.3125 0.4375 0.5625 0.6875 0.8125 0.9375 1.0625 1.1875 1.3125 1.4375 1.5625 1.6875 1.8125 1.9375 2.0625 2.1875 2.3125 2.4375 -6.250000000000000000e-02 4.058428380050588258e-01 4.378944191963303756e-01 4.437929690317643505e-01 4.217588325846560737e-01 3.755080311191841269e-01 3.131598310381266126e-01 2.447563995121018743e-01 1.794902445999781160e-01 1.237273337207813739e-01 8.035604035731884476e-02 4.930684694815348074e-02 2.867501968181941294e-02 1.586031822990442708e-02 8.374085080710214415e-03 4.237011651546859596e-03 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