import pandas as pd
import sys
import warnings
from .utils_dynamics import *
from .utils import despline, _matplotlib_points, _datashade_points, _select_font_color
from .utils import arrowed_spines, despline_all, deaxis_all
from .utils import quiver_autoscaler, default_quiver_args
from .utils import save_fig
from .scatters import scatters
from ..estimation.csc.velocity import sol_u, sol_s, solve_first_order_deg
from ..estimation.tsc.utils_moments import moments
from ..tools.utils import get_mapper, log1p_
from ..tools.utils import update_dict, get_valid_bools
from ..configuration import _themes
[docs]def phase_portraits(
adata,
genes,
x=0,
y=1,
pointsize=None,
vkey="S",
ekey="M_s",
basis="umap",
log1p=True,
color='cell_cycle_phase',
use_smoothed=True,
highlights=None,
discrete_continous_div_themes=None,
discrete_continous_div_cmap=None,
discrete_continous_div_color_key=[None, None, None],
discrete_continous_div_color_key_cmap=None,
figsize=(6, 4),
ncols=None,
legend="upper left",
background=None,
show_quiver=False,
quiver_size=None,
quiver_length=None,
frontier=True,
q_kwargs_dict={},
show_arrowed_spines=None,
save_show_or_return='show',
save_kwargs={},
**kwargs,
):
"""Draw the phase portrait, expression values , velocity on the low dimensional embedding.
Note that this function allows to manually set the theme, cmap, color_key and color_key_cmap
for the phase portrait, expression and velocity subplots. When the background is 'black',
the default themes for each of those subplots are ["glasbey_dark", "inferno", "div_blue_black_red"],
respectively. When the background is 'black', the default themes are "glasbey_white", "viridis",
"div_blue_red".
Parameters
----------
adata: :class:`~anndata.AnnData`
an Annodata object
genes: `list`
A list of gene names that are going to be visualized.
x: `int` (default: `0`)
The column index of the low dimensional embedding for the x-axis
y: `int` (default: `1`)
The column index of the low dimensional embedding for the y-axis
pointsize: `None` or `float` (default: None)
The scale of the point size. Actual point cell size is calculated as `2500.0 / np.sqrt(adata.shape[0]) *
pointsize`
vkey: `string` (default: velocity)
Which velocity key used for visualizing the magnitude of velocity. Can be either velocity in the layers slot
or the keys in the obsm slot.
ekey: `str` (default: `M_s`)
The layer of data to represent the gene expression level.
basis: `string` (default: umap)
Which low dimensional embedding will be used to visualize the cell.
log1p: `bool` (default: `True`)
Whether to log1p transform the expression so that visualization can be robust to extreme values.
color: `string` (default: 'cell_cycle_phase')
Which group will be used to color cells, only used for the phase portrait because the other two plots are
colored by the velocity magnitude or the gene expression value, respectively.
use_smoothed: `bool` (default: `True`)
Whether to use smoothed 1/2-nd moments as gene expression for the first and third columns. This is useful
for checking the confidence of velocity genes. For example, you may see a very nice linear pattern for some
genes with the smoothed expression but this could just be an artifically generated when the number of
expressed cells is very low. This raises red flags for the quality of the velocity values we learned for
those genes. And we recommend to set the higher values (for example, 10% of all cells) for `min_cell_s`,
`min_cell_u` or `shared_count` of the `fg_kwargs` argument of the dyn.pl.receipe_monocle. Note that this is
often related to the small single cell datasets (like plate-based scRNA-seq or scSLAM-seq/NASC-seq, etc).
highlights: `list` (default: None)
Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color
element.
discrete_continous_div_themes: `list[str, str, str]` (optional, default None)
The discrete, continous and divergent color themes to use for plotting. The description for each element in
the list is as following.
A small set of predefined themes are provided which have relatively good aesthetics. Available themes are:
* 'blue'
* 'red'
* 'green'
* 'inferno'
* 'fire'
* 'viridis'
* 'darkblue'
* 'darkred'
* 'darkgreen'
discrete_continous_div_cmap: `list[str, str, str]` (optional, default 'Blues')
The names of discrete, continous and divergent matplotlib colormap to use for coloring
or shading points. The description for each element in the list is as following. If no labels or values are
passed this will be used for shading points according to density (largely only of relevance for very large
datasets). If values are passed this will be used for shading according the value. Note that if theme is
passed then this value will be overridden by the corresponding option of the theme.
discrete_continous_div_color_key: `list[dict or array,, dict or array,, dict or array,]` (default [None, None,
None]).
The description for each element in the list is as following. The shape (n_categories) (optional, default
None). A list to assign discrete, continous and divergent colors to categoricals. This can either be an
explicit dict mapping labels to colors (as strings of form '#RRGGBB'), or an array like object providing one
color for each distinct category being provided in ``labels``. Either way this mapping will be used to color
points according to the label. Note that if theme is passed then this value will be overridden by the
corresponding option of the theme.
discrete_continous_div_color_key_cmap: `list[str, str, str]`, (optional, default 'Spectral')
The names of discrete, continous and divergent matplotlib colormap to use for categorical coloring.
The description for each element in the list is as following. If an explicit ``color_key`` is not given a
color mapping for categories can be generated from the label list and selecting a matching list of colors
from the given colormap. Note that if theme is passed then this value will be overridden by the corresponding
option of the theme.
figsize: `None` or `[float, float]` (default: None)
The width and height of each panel in the figure.
ncols: `None` or `int` (default: None)
ncol: `None` or `int` (default: None)
Number of columns in each facet grid.
legend: `str` (default: `on data`)
Where to put the legend. Legend is drawn by seaborn with “brief” mode, numeric hue and size variables
will be represented with a sample of evenly spaced values. By default legend is drawn on top of cells.
show_quiver: `bool` (default: False)
Whether to show the quiver plot. If velocity for x component (corresponds to either spliced, total RNA,
protein, etc) or y component (corresponds to either unspliced, new RNA, protein, etc) are both calculated,
quiver represents velocity for both components otherwise the uncalculated component (usually y component)
will be set to be 0.
quiver_size: `float` or None (default: None)
The size of quiver. If None, we will use set quiver_size to be 1. Note that quiver quiver_size is used to
calculate the head_width (10 x quiver_size), head_length (12 x quiver_size) and headaxislength (8 x
quiver_size) of the quiver. This is done via the `default_quiver_args` function which also calculate the
scale of the quiver (1 / quiver_length).
quiver_length: `float` or None (default: None)
The length of quiver. The quiver length which will be used to calculate scale of quiver. Note that befoe
applying `default_quiver_args` velocity values are first rescaled via the quiver_autoscaler function. Scale
of quiver indicates the nuumber of data units per arrow length unit, e.g., m/s per plot width; a smaller
scale parameter makes the arrow longer.
q_kwargs_dict: `dict` (default: {})
The dictionary of the quiver arguments. The default setting of quiver argument is identical to that used in
the cell_wise_velocity and grid_velocity.
show_arrowed_spines: bool or None (optional, default None)
Whether to show a pair of arrowed spines represeenting the basis of the scatter is currently using. If None,
only the first panel in the expression / velocity plot will have the arrowed spine.
save_show_or_return: {'show', 'save', 'return'} (default: `show`)
Whether to save, show or return the figure.
save_kwargs: `dict` (default: `{}`)
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig
function will use the {"path": None, "prefix": 'phase_portraits', "dpi": None, "ext": 'pdf', "transparent":
True, "close": True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly
modify those keys according to your needs.
**kwargs:
Additional parameters that will be passed to plt.scatter function
Returns
-------
A matplotlib plot that shows 1) the phase portrait of each category used in velocity embedding, cells' low
dimensional embedding, colored either by 2) the gene expression level or 3) the velocity magnitude values.
"""
import matplotlib.pyplot as plt
from matplotlib import rcParams
from matplotlib.colors import to_hex
# from matplotlib.colors import DivergingNorm # TwoSlopeNorm
if background is None:
_background = rcParams.get("figure.facecolor")
_background = to_hex(_background) if type(_background) is tuple else _background
else:
_background = background
mapper = get_mapper(smoothed=use_smoothed)
point_size = (
500.0 / np.sqrt(adata.shape[0]) * 5
if pointsize is None
else 500.0 / np.sqrt(adata.shape[0]) * 5 * pointsize
)
scatter_kwargs = dict(
alpha=0.2, s=point_size, edgecolor=None, linewidth=0
) # (0, 0, 0, 1)
if kwargs is not None:
scatter_kwargs.update(kwargs)
div_scatter_kwargs = scatter_kwargs.copy()
# div_scatter_kwargs.update({"norm": DivergingNorm(vcenter=0)})
if type(genes) == str:
genes = [genes]
_genes = list(set(adata.var.index).intersection(genes))
# avoid object for dtype in the gamma column https://stackoverflow.com/questions/40809503/python-numpy-typeerror-ufunc-isfinite-not-supported-for-the-input-types
k_name = 'gamma_k' if adata.uns['dynamics']['experiment_type'] == 'one-shot' else 'gamma'
valid_id = np.isfinite(
np.array(adata.var.loc[_genes, k_name], dtype="float")
).flatten()
genes = np.array(_genes)[valid_id].tolist()
# idx = [adata.var.index.to_list().index(i) for i in genes]
if len(genes) == 0:
raise Exception(
"adata has no genes listed in your input gene vector or "
"velocity estimation for those genes are not performed. "
"Please try to run dyn.tl.dynamics(adata, filter_gene_mode='no')"
"to estimate velocity for all genes: {}".format(_genes)
)
if not "X_" + basis in adata.obsm.keys():
warnings.warn("{} is not applied to adata.".format(basis))
from ..tools.dimension_reduction import reduceDimension
reduceDimension(adata, reduction_method=basis)
embedding = pd.DataFrame(
{
basis + "_0": adata.obsm["X_" + basis][:, x],
basis + "_1": adata.obsm["X_" + basis][:, y],
}
)
embedding.columns = ["dim_1", "dim_2"]
if all([i in adata.layers.keys() for i in ["X_new", "X_total"]]) or all(
[i in adata.layers.keys() for i in [mapper["X_new"], mapper["X_total"]]]
):
mode = "labeling"
elif all([i in adata.layers.keys() for i in ["X_spliced", "X_unspliced"]]) or all(
[i in adata.layers.keys() for i in [mapper["X_spliced"], mapper["X_unspliced"]]]
):
mode = "splicing"
elif all(
[i in adata.layers.keys() for i in ["X_uu", "X_ul", "X_su", "X_sl"]]
) or all(
[
i in adata.layers.keys()
for i in [mapper["X_uu"], mapper["X_ul"], mapper["X_su"], mapper["X_sl"]]
]
):
mode = "full"
else:
raise Exception(
"your data should be in one of the proper mode: labelling (has X_new/X_total layers), splicing "
"(has X_spliced/X_unspliced layers) or full (has X_uu/X_ul/X_su/X_sl layers)"
)
layers = list(adata.layers.keys())
layers.extend(["X", "protein", "X_protein"])
if ekey in layers:
if ekey == "X":
E_vec = (
adata[:, genes].layers[mapper["X"]]
if mapper["X"] in adata.layers.keys()
else adata[:, genes].X
)
elif ekey in ["protein", "X_protein"]:
E_vec = (
adata[:, genes].layers[mapper[ekey]]
if (ekey in mapper.keys()) and (mapper[ekey] in adata.obsm_keys())
else adata[:, genes].obsm[ekey]
)
else:
E_vec = (
adata[:, genes].layers[mapper[ekey]]
if (ekey in mapper.keys()) and (mapper[ekey] in adata.layers.keys())
else adata[:, genes].layers[ekey]
)
if log1p: E_vec = log1p_(adata, E_vec)
n_cells, n_genes = adata.shape[0], len(genes)
color_vec = np.repeat(np.nan, n_cells)
if color is not None:
color_vec = adata.obs[color].to_list()
if "velocity_" not in vkey:
vkey = "velocity_" + vkey
if vkey == "velocity_U":
V_vec = adata[:, genes].layers["velocity_U"]
if "velocity_P" in adata.obsm.keys():
P_vec = adata[:, genes].layer["velocity_P"]
elif vkey == "velocity_S":
V_vec = adata[:, genes].layers["velocity_S"]
if "velocity_P" in adata.obsm.keys():
P_vec = adata[:, genes].layers["velocity_P"]
else:
raise Exception(
"adata has no vkey {} in either the layers or the obsm slot".format(vkey)
)
E_vec, V_vec = (
E_vec.A if issparse(E_vec) else E_vec,
V_vec.A if issparse(V_vec) else V_vec,
)
if k_name in adata.var.columns:
if (
not ("gamma_b" in adata.var.columns)
or all(adata.var.gamma_b.isna())
):
adata.var.loc[:, "gamma_b"] = 0
gamma, velocity_offset = (
adata[:, genes].var.loc[:, k_name].values,
adata[:, genes].var.gamma_b.values,
)
(
gamma[~np.isfinite(list(gamma))],
velocity_offset[~np.isfinite(list(velocity_offset))],
) = (0, 0)
else:
raise Exception(
"adata does not seem to have velocity_gamma column. Velocity estimation is required before "
"running this function."
)
if mode == "labeling":
new_mat, tot_mat = (
adata[:, genes].layers[mapper["X_new"]],
adata[:, genes].layers[mapper["X_total"]],
)
new_mat, tot_mat = (
(new_mat.A, tot_mat.A) if issparse(new_mat) else (new_mat, tot_mat)
)
vel_u, vel_s = (
adata[:, genes].layers["velocity_U"].A,
adata[:, genes].layers["velocity_S"].A,
)
df = pd.DataFrame(
{
"new": new_mat.flatten(),
"total": tot_mat.flatten(),
"gene": genes * n_cells,
"gamma": np.tile(gamma, n_cells),
"velocity_offset": np.tile(velocity_offset, n_cells),
"expression": E_vec.flatten(),
"velocity": V_vec.flatten(),
"color": np.repeat(color_vec, n_genes),
"vel_u": vel_u.flatten(),
"vel_s": vel_s.flatten(),
},
index=range(n_cells * n_genes),
)
elif mode == "splicing":
unspliced_mat, spliced_mat = (
adata[:, genes].layers[mapper["X_unspliced"]],
adata[:, genes].layers[mapper["X_spliced"]],
)
unspliced_mat, spliced_mat = (
(unspliced_mat.A, spliced_mat.A)
if issparse(unspliced_mat)
else (unspliced_mat, spliced_mat)
)
vel_u, vel_s = (
np.zeros_like(adata[:, genes].layers["velocity_S"].A),
adata[:, genes].layers["velocity_S"].A,
)
df = pd.DataFrame(
{
"unspliced": unspliced_mat.flatten(),
"spliced": spliced_mat.flatten(),
"gene": genes * n_cells,
"gamma": np.tile(gamma, n_cells),
"velocity_offset": np.tile(velocity_offset, n_cells),
"expression": E_vec.flatten(),
"velocity": V_vec.flatten(),
"color": np.repeat(color_vec, n_genes),
"vel_u": vel_u.flatten(),
"vel_s": vel_s.flatten(),
},
index=range(n_cells * n_genes),
)
elif mode == "full":
uu, ul, su, sl = (
adata[:, genes].layers[mapper["X_uu"]],
adata[:, genes].layers[mapper["X_ul"]],
adata[:, genes].layers[mapper["X_su"]],
adata[:, genes].layers[mapper["X_sl"]],
)
vel_u, vel_s = (
np.zeros_like(adata[:, genes].layers["velocity_S"].A),
adata[:, genes].layers["velocity_S"].A,
)
if "protein" in adata.obsm.keys():
if "delta" in adata.var.columns:
gamma_P = adata.var.delta[genes].values
velocity_offset_P = (
[0] * n_cells
if (
not ("delta_b" in adata.var.columns)
or adata.var.delta_b.unique() is None
)
else adata.var.delta_b[genes].values
)
else:
raise Exception(
"adata does not seem to have velocity_gamma column. Velocity estimation is required before "
"running this function."
)
P = (
adata[:, genes].obsm[mapper["X_protein"]]
if (
["X_protein"] in adata.obsm.keys()
or [mapper["X_protein"]] in adata.obsm.keys()
)
else adata[:, genes].obsm["protein"]
)
uu, ul, su, sl, P = (
(uu.A, ul.A, su.A, sl.A, P.A) if issparse(uu) else (uu, ul, su, sl, P)
)
if issparse(P_vec):
P_vec = P_vec.A
vel_p = np.zeros_like(adata.obsm["velocity_P"][:, :])
# df = pd.DataFrame({"uu": uu.flatten(), "ul": ul.flatten(), "su": su.flatten(), "sl": sl.flatten(), "P": P.flatten(),
# 'gene': genes * n_cells, 'prediction': np.tile(gamma, n_cells) * uu.flatten() +
# np.tile(velocity_offset, n_cells), "velocity": genes * n_cells}, index=range(n_cells * n_genes))
df = pd.DataFrame(
{
"new": (ul + sl).flatten(),
"total": (uu + ul + sl + su).flatten(),
"S": (sl + su).flatten(),
"P": P.flatten(),
"gene": genes * n_cells,
"gamma": np.tile(gamma, n_cells),
"velocity_offset": np.tile(velocity_offset, n_cells),
"gamma_P": np.tile(gamma_P, n_cells),
"velocity_offset_P": np.tile(velocity_offset_P, n_cells),
"expression": E_vec.flatten(),
"velocity": V_vec.flatten(),
"velocity_protein": P_vec.flatten(),
"color": np.repeat(color_vec, n_genes),
"vel_u": vel_u.flatten(),
"vel_s": vel_s.flatten(),
"vel_p": vel_p.flatten(),
},
index=range(n_cells * n_genes),
)
else:
vel_u, vel_s = (
np.zeros_like(adata[:, genes].layers["velocity_S"].A),
adata[:, genes].layers["velocity_S"].A,
)
df = pd.DataFrame(
{
"new": (ul + sl).flatten(),
"total": (uu + ul + sl + su).flatten(),
"gene": genes * n_cells,
"gamma": np.tile(gamma, n_cells),
"velocity_offset": np.tile(velocity_offset, n_cells),
"expression": E_vec.flatten(),
"velocity": V_vec.flatten(),
"color": np.repeat(color_vec, n_genes),
"vel_u": vel_u.flatten(),
"vel_s": vel_s.flatten(),
},
index=range(n_cells * n_genes),
)
else:
raise Exception(
"Your adata is corrupted. Make sure that your layer has keys new, old for the labelling mode, "
"spliced, ambiguous, unspliced for the splicing model and uu, ul, su, sl for the full mode"
)
num_per_gene = 6 if ("protein" in adata.obsm.keys() and mode == "full") else 3
ncols = min([num_per_gene, ncols]) if ncols is not None else num_per_gene
nrow, ncol = int(np.ceil(num_per_gene * n_genes / ncols)), ncols
if figsize is None:
g = plt.figure(None, (3 * ncol, 3 * nrow), facecolor=_background) # , dpi=160
else:
g = plt.figure(None, (figsize[0] * ncol, figsize[1] * nrow), facecolor=_background) # , dpi=160
if discrete_continous_div_themes is None:
if _background in ["#ffffff", "black"]:
discrete_theme, continous_theme, divergent_theme = (
"glasbey_dark",
"inferno",
"div_blue_black_red",
)
else:
discrete_theme, continous_theme, divergent_theme = (
"glasbey_white",
"viridis",
"div_blue_red",
)
else:
discrete_theme, continous_theme, divergent_theme = discrete_continous_div_themes
discrete_cmap, discrete_color_key_cmap, discrete_background = (
_themes[discrete_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[0],
_themes[discrete_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
discrete_continous_div_color_key_cmap[0],
_themes[discrete_theme]["background"],
)
continous_cmap, continous_color_key_cmap, continous_background = (
_themes[continous_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[1],
_themes[continous_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
discrete_continous_div_color_key_cmap[1],
_themes[continous_theme]["background"],
)
divergent_cmap, divergent_color_key_cmap, divergent_background = (
_themes[divergent_theme]["cmap"] if discrete_continous_div_cmap is None else discrete_continous_div_cmap[2],
_themes[divergent_theme]["color_key_cmap"] if discrete_continous_div_color_key_cmap is None else
discrete_continous_div_color_key_cmap[2],
_themes[divergent_theme]["background"],
)
font_color = _select_font_color(discrete_background)
# the following code is inspired by https://github.com/velocyto-team/velocyto-notebooks/blob/master/python/DentateGyrus.ipynb
gs = plt.GridSpec(nrow, ncol, wspace=0.5, hspace=0.36)
for i, gn in enumerate(genes):
if num_per_gene == 3:
ax1, ax2, ax3 = (
plt.subplot(gs[i * 3]),
plt.subplot(gs[i * 3 + 1]),
plt.subplot(gs[i * 3 + 2]),
)
elif num_per_gene == 6:
ax1, ax2, ax3, ax4, ax5, ax6 = (
plt.subplot(gs[i * 3]),
plt.subplot(gs[i * 3 + 1]),
plt.subplot(gs[i * 3 + 2]),
plt.subplot(gs[i * 3 + 3]),
plt.subplot(gs[i * 3 + 4]),
plt.subplot(gs[i * 3 + 5]),
)
try:
ix = np.where(adata.var.index == gn)[0][0]
except:
continue
cur_pd = df.loc[df.gene == gn, :]
if cur_pd.color.isna().all():
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax1, color = _matplotlib_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax1,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax1, color = _datashade_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax1,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax1, color = _matplotlib_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax1,
labels=cur_pd.loc[:, "color"],
values=None,
highlights=highlights,
cmap=discrete_cmap,
color_key=discrete_continous_div_color_key[0],
color_key_cmap=discrete_color_key_cmap,
background=discrete_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax1, color = _datashade_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax1,
labels=cur_pd.loc[:, "color"],
values=None,
highlights=highlights,
cmap=discrete_cmap,
color_key=discrete_continous_div_color_key[0],
color_key_cmap=discrete_color_key_cmap,
background=discrete_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
ax1.set_title(gn)
ax1.set_xlabel("spliced (1st moment)")
ax1.set_ylabel("unspliced (1st moment)")
xnew = np.linspace(0, cur_pd.iloc[:, 1].max() * 0.80)
ax1.plot(
xnew,
xnew * cur_pd.loc[:, "gamma"].unique()
+ cur_pd.loc[:, "velocity_offset"].unique(),
dashes=[6, 2],
c=font_color,
)
X_array, V_array = (
cur_pd.iloc[:, [1, 0]].values,
cur_pd.loc[:, ["vel_s", "vel_u"]].values,
)
# add quiver:
if show_quiver:
V_array /= 3 * quiver_autoscaler(X_array, V_array)
if background is None:
background = rcParams.get("figure.facecolor")
if quiver_size is None:
quiver_size = 1
if background == "black":
edgecolors = "white"
else:
edgecolors = "black"
head_w, head_l, ax_l, scale = default_quiver_args(
quiver_size, quiver_length
)
quiver_kwargs = {
"angles": "xy",
"scale": scale,
"scale_units": "xy",
"width": 0.0005,
"headwidth": head_w,
"headlength": head_l,
"headaxislength": ax_l,
"minshaft": 1,
"minlength": 1,
"pivot": "tail",
"edgecolors": edgecolors,
"linewidth": 0.2,
"color": color,
"alpha": 1,
"zorder": 10,
}
quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
ax1.quiver(
X_array[:, 0],
X_array[:, 1],
V_array[:, 0],
V_array[:, 1],
**quiver_kwargs
)
ax1.set_xlim(0, np.max(X_array[:, 0]) * 1.02)
ax1.set_ylim(0, np.max(X_array[:, 1]) * 1.02)
despline(ax1) # sns.despline()
df_embedding = pd.concat([cur_pd, embedding], axis=1)
V_vec = cur_pd.loc[:, "velocity"]
# limit = np.nanmax(
# np.abs(np.nanpercentile(V_vec, [1, 99]))
# ) # upper and lowe limit / saturation
#
# V_vec = V_vec + limit # that is: tmp_colorandum - (-limit)
# V_vec = V_vec / (2 * limit) # that is: tmp_colorandum / (limit - (-limit))
# V_vec = np.clip(V_vec, 0, 1)
if show_arrowed_spines is None:
show_arrowed_spines_ = True if i == 0 else False
else:
show_arrowed_spines_ = show_arrowed_spines
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax2, _ = _matplotlib_points(
embedding.iloc[:, :2].values,
ax=ax2,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax2, _ = _datashade_points(
embedding.iloc[:, :2].values,
ax=ax2,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
ax2.set_title(gn + " (" + ekey + ")")
if show_arrowed_spines_:
ax2 = arrowed_spines(ax2, basis)
else:
despline_all(ax2)
deaxis_all(ax2)
v_max = np.max(np.abs(V_vec.values))
div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax3, _ = _matplotlib_points(
embedding.iloc[:, :2].values,
ax=ax3,
labels=None,
values=V_vec.values,
highlights=highlights,
cmap=divergent_cmap,
color_key=discrete_continous_div_color_key[2],
color_key_cmap=divergent_color_key_cmap,
background=divergent_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
sort='abs',
frontier=frontier,
**div_scatter_kwargs
)
else:
ax3, _ = _datashade_points(
embedding.iloc[:, :2].values,
ax=ax3,
labels=None,
values=V_vec.values,
highlights=highlights,
cmap=divergent_cmap,
color_key=discrete_continous_div_color_key[2],
color_key_cmap=divergent_color_key_cmap,
background=divergent_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
sort='abs',
frontier=frontier,
**div_scatter_kwargs
)
ax3.set_title(gn + " (" + vkey + ")")
if show_arrowed_spines_:
ax3 = arrowed_spines(ax3, basis)
else:
despline_all(ax3)
deaxis_all(ax3)
if (
"protein" in adata.obsm.keys()
and mode == "full"
and all([i in adata.layers.keys() for i in ["uu", "ul", "su", "sl"]])
):
if cur_pd.color.unique() != np.nan:
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax4, color = _matplotlib_points(
cur_pd.iloc[:, [3, 2]].values,
ax=ax4,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax4, color = _datashade_points(
cur_pd.iloc[:, [3, 2]].values,
ax=ax4,
labels=None,
values=cur_pd.loc[:, "expression"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax4, color = _matplotlib_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax4,
labels=cur_pd.loc[:, "color"].values,
values=None,
highlights=highlights,
cmap=discrete_cmap,
color_key=discrete_continous_div_color_key[0],
color_key_cmap=discrete_color_key_cmap,
background=discrete_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax4, color = _datashade_points(
cur_pd.iloc[:, [1, 0]].values,
ax=ax4,
labels=cur_pd.loc[:, "color"].values,
values=None,
highlights=highlights,
cmap=discrete_cmap,
color_key=discrete_continous_div_color_key[0],
color_key_cmap=discrete_color_key_cmap,
background=discrete_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
ax4.set_title(gn)
ax1.set_xlabel("spliced (1st moment)")
ax1.set_ylabel("protein (1st moment)")
xnew = np.linspace(0, cur_pd.iloc[:, 3].max())
ax4.plot(
xnew,
xnew * cur_pd.loc[:, "gamma_P"].unique()
+ cur_pd.loc[:, "velocity_offset_P"].unique(),
dashes=[6, 2],
c=font_color,
)
X_array, V_array = (
cur_pd.iloc[:, [3, 2]].values,
cur_pd.loc[:, ["vel_p", "vel_s"]].values,
)
# add quiver:
if show_quiver:
V_array /= 3 * quiver_autoscaler(X_array, V_array)
if background is None:
background = rcParams.get("figure.facecolor")
if quiver_size is None:
quiver_size = 1
if background == "black":
edgecolors = "white"
else:
edgecolors = "black"
head_w, head_l, ax_l, scale = default_quiver_args(
quiver_size, quiver_length
)
quiver_kwargs = {
"angles": "xy",
"scale": scale,
"scale_units": "xy",
"width": 0.0005,
"headwidth": head_w,
"headlength": head_l,
"headaxislength": ax_l,
"minshaft": 1,
"minlength": 1,
"pivot": "tail",
"edgecolors": edgecolors,
"linewidth": 0.2,
"color": color,
"alpha": 1,
"zorder": 10,
}
quiver_kwargs = update_dict(quiver_kwargs, q_kwargs_dict)
ax4.quiver(
X_array[:, 0],
X_array[:, 1],
V_array[:, 0],
V_array[:, 1],
**quiver_kwargs
)
ax4.set_ylim(0, np.max(X_array[:, 0]) * 1.02)
ax4.set_xlim(0, np.max(X_array[:, 1]) * 1.02)
despline(ax1) # sns.despline()
V_vec = df_embedding.loc[:, "velocity_p"]
limit = np.nanmax(
np.abs(np.nanpercentile(V_vec, [1, 99]))
) # upper and lowe limit / saturation
V_vec = V_vec + limit # that is: tmp_colorandum - (-limit)
V_vec = V_vec / (2 * limit) # that is: tmp_colorandum / (limit - (-limit))
V_vec = np.clip(V_vec, 0, 1)
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax5, _ = _matplotlib_points(
embedding.iloc[:, :2],
ax=ax5,
labels=None,
values=embedding.loc[:, "P"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
else:
ax5, _ = _datashade_points(
embedding.iloc[:, :2],
ax=ax5,
labels=None,
values=embedding.loc[:, "P"].values,
highlights=highlights,
cmap=continous_cmap,
color_key=discrete_continous_div_color_key[1],
color_key_cmap=continous_color_key_cmap,
background=continous_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
**scatter_kwargs
)
ax5.set_title(gn + " (protein expression)")
if show_arrowed_spines_:
ax5 = arrowed_spines(ax5, basis)
else:
despline_all(ax5)
deaxis_all(ax5)
v_max = np.max(np.abs(V_vec.values))
div_scatter_kwargs.update({"vmin": -v_max, "vmax": v_max})
if cur_pd.shape[0] <= figsize[0] * figsize[1] * 1000000:
ax6, _ = _matplotlib_points(
embedding.iloc[:, :2],
ax=ax6,
labels=None,
values=V_vec.values,
highlights=highlights,
cmap=divergent_cmap,
color_key=discrete_continous_div_color_key[2],
color_key_cmap=divergent_color_key_cmap,
background=divergent_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
sort='abs',
frontier=frontier,
**div_scatter_kwargs
)
else:
ax6, _ = _datashade_points(
embedding.iloc[:, :2],
ax=ax6,
labels=None,
values=V_vec.values,
highlights=highlights,
cmap=divergent_cmap,
color_key=discrete_continous_div_color_key[2],
color_key_cmap=divergent_color_key_cmap,
background=divergent_background,
width=figsize[0],
height=figsize[1],
show_legend=legend,
sort='abs',
frontier=frontier,
**div_scatter_kwargs
)
ax6.set_title(gn + " (protein velocity)")
if show_arrowed_spines_:
ax6 = arrowed_spines(ax6, basis)
else:
despline_all(ax6)
deaxis_all(ax6)
if save_show_or_return == "save":
s_kwargs = {"path": None, "prefix": 'phase_portraits', "dpi": None,
"ext": 'pdf', "transparent": True, "close": True, "verbose": True}
s_kwargs = update_dict(s_kwargs, save_kwargs)
save_fig(**s_kwargs)
elif save_show_or_return == "show":
with warnings.catch_warnings():
warnings.simplefilter("ignore")
plt.tight_layout()
plt.show()
elif save_show_or_return == "return":
return g
[docs]def dynamics(
adata,
vkey,
unit="hours",
log_unnormalized=True,
y_log_scale=False,
ci=None,
ncols=None,
figsize=None,
dpi=None,
boxwidth=None,
barwidth=None,
true_param_prefix=None,
show_moms_fit=False,
show_variance=True,
show_kin_parameters=True,
gene_order='column',
font_size_scale=1,
save_show_or_return='show',
save_kwargs={},
):
"""Plot the data and fitting of different metabolic labeling experiments.
Note that if non-smoothed data was used for kinetic fitting, you often won't see boxplot
but only the triangles for the mean values. This is because the raw data has a lot dropout,
thus the median is outside of the whiskers of the boxplot and the boxplot is then not drawn
by default.
Parameters
----------
adata: :class:`~anndata.AnnData`
an Annodata object.
vkey: list of `str`
key for variable or gene names.
unit: `str` (default: `hour`)
The unit of the labeling time, for example, `hours` or `minutes`.
y_log_scale: `bool` (default: `True`)
Whether or not to use log scale for y-axis.
ci: `str` (for example: "95%") or None (default: `None`)
The confidence interval to be drawed for the parameter fitting. Currently not used.
ncols: `int` or None (default: `None`)
The number of columns in the plot.
figsize: `[float, float]` or `(float, float)` or None
The size of the each panel in the figure.
dpi: `float` or None
Figure resolution.
boxwidth: `float`
The width of the box of the boxplot.
barwidth: `float`
The width of the bar of the barplot.
true_param_prefix: `str`
The prefix for the column names of true parameters in the .var attributes. Useful for the simulation data.
show_moms_fit: `bool` (default: `True`)
Whether to show fitting curves associated with the stochastic models, only works for non-deterministic models.
show_variance: `bool` (default: `True`)
Whether to add a boxplot to show the variance at each time point.
show_kin_parameters: `bool` (default: `True`)
Whether to include the estimated kinetic parameter values on the plot.
gene_order: `str` (default: `column`)
The order of genes to present on the figure, either row-major or column major.
font_size_scale: `float` (default: `1`)
A value that will be used for scaling
save_show_or_return: {'show', 'save', 'return'} (default: `show`)
Whether to save, show or return the figure.
save_kwargs: `dict` (default: `{}`)
A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function
will use the {"path": None, "prefix": 'dynamics', "dpi": None, "ext": 'pdf', "transparent": True, "close":
True, "verbose": True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys
according to your needs.
Returns
-------
Nothing but plot the figure of the metabolic fitting.
"""
import matplotlib.pyplot as plt
import matplotlib
params = {'font.size': 4 * font_size_scale,
'legend.fontsize': 4 * font_size_scale,
'legend.handlelength': 0.5,
'axes.labelsize': 6 * font_size_scale,
'axes.titlesize': 6 * font_size_scale,
'xtick.labelsize': 6 * font_size_scale,
'ytick.labelsize': 6 * font_size_scale,
'axes.titlepad': 1,
'axes.labelpad': 1,
'axes.spines.right': False,
'axes.spines.top': False
}
matplotlib.rcParams.update(params)
show_kin_parameters = True if true_param_prefix else show_kin_parameters
uns_keys = np.array(adata.uns_keys())
tmp = np.array([i.split("_dynamics")[0] if i.endswith('_dynamics') else None for i in uns_keys])
tmp1 = [False if i is None else True for i in tmp]
group = tmp[tmp1][0] if sum(tmp1) > 0 else None
if group is not None:
uns_key = group + "_dynamics"
_group, grp_len = np.unique(adata.obs[group]), len(np.unique(adata.obs[group]))
else:
uns_key = "dynamics"
_group, grp_len = ["_all_cells"], 1
(
filter_gene_mode,
T,
group,
X_data,
X_fit_data,
asspt_mRNA,
experiment_type,
normalized,
model,
has_splicing,
has_labeling,
has_protein,
use_smoothed,
NTR_vel,
log_unnormalized
) = adata.uns[uns_key].values()
if experiment_type == "conventional":
# run the phase_portraits plot
warnings.warn(
"dynamics plot doesn't support conventional experiment type, using phase_portraits function instead."
)
phase_portraits(adata)
T_uniq = np.unique(T)
t = np.linspace(0, T_uniq[-1], 1000)
valid_genes = adata.var_names[get_valid_bools(adata, filter_gene_mode)]
valid_gene_names = valid_genes.intersection(vkey)
if len(valid_gene_names) == 0:
raise ValueError(f"no kinetic parameter fitting has performed for the gene {vkey} you provided. Try "
f"using dyn.tl.dynamics(adata, filter_gene_mode='final', ...) or use only fitted genes.")
valid_adata = adata[:, valid_gene_names]
gene_idx = np.array([np.where(valid_gene_names == gene)[0][0] for gene in vkey])
if boxwidth is None and len(T_uniq) > 1:
boxwidth = 0.8 * (np.diff(T_uniq).min() / 2)
if barwidth is None and len(T_uniq) > 1:
barwidth = 0.8 * (np.diff(T_uniq).min() / 2)
if has_splicing:
if model == "moment":
sub_plot_n = 4 # number of subplots for each gene
elif experiment_type == "kin":
if model == 'deterministic':
sub_plot_n = 2 if show_variance else 1
elif model == 'stochastic':
mom_n = 3 if show_moms_fit else 0
sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
elif model == 'mixture':
sub_plot_n = 4 if show_variance else 2
elif model == 'mixture_deterministic_stochastic':
mom_n = 3 if show_moms_fit else 0
sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
elif model == 'mixture_stochastic_stochastic':
mom_n = 6 if show_moms_fit else 0
sub_plot_n = 4 + mom_n if show_variance else 2 + mom_n
elif experiment_type == "deg":
if model == 'deterministic':
sub_plot_n = 2 if show_variance else 1
elif model == 'stochastic':
mom_n = 3 if show_moms_fit else 0
sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
elif experiment_type == "one_shot": # just the labeled RNA
sub_plot_n = 1
elif experiment_type == "mix_std_stm":
sub_plot_n = 5
else:
if model == "moment":
sub_plot_n = 2 # number of subplots for each gene
elif experiment_type == "kin":
if model == 'deterministic':
sub_plot_n = 1
elif model == 'stochastic':
mom_n = 1 if show_moms_fit else 0
sub_plot_n = 1 + mom_n if show_variance else 1 + mom_n
elif model == 'mixture':
sub_plot_n = 2 if show_variance else 1
elif model == 'mixture_deterministic_stochastic':
mom_n = 1 if show_moms_fit else 0
sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
elif model == 'mixture_stochastic_stochastic':
mom_n = 2 if show_moms_fit else 0
sub_plot_n = 2 + mom_n if show_variance else 1 + mom_n
elif experiment_type == "deg":
if model == 'deterministic':
sub_plot_n = 1
elif model == 'stochastic':
mom_n = 1 if show_moms_fit else 0
sub_plot_n = 1 + mom_n
elif experiment_type == "one_shot": # just the labeled RNA
sub_plot_n = 1
elif experiment_type == "mix_std_stm":
sub_plot_n = 3
ncols = ( # each column correspond to one gene
len(gene_idx) * grp_len
if ncols is None
else min(len(gene_idx) * grp_len, ncols)
)
nrows = int(np.ceil(len(gene_idx) * sub_plot_n * grp_len / ncols))
figsize = [7, 5] if figsize is None else figsize
g = plt.figure(None, (figsize[0] * ncols, figsize[1] * nrows), dpi=dpi)
gs = plt.GridSpec(
nrows,
ncols,
g,
wspace=0.12,
)
# there are bugs when fig is not a symmetric matrix
fig_mat = np.arange(nrows * ncols).reshape((nrows, ncols))
# we need to visualize gene in row-wise mode
for grp_idx, cur_grp in enumerate(_group):
if cur_grp == "_all_cells":
prefix = "" # kinetic_parameter_
else:
prefix = group + "_" + cur_grp + "_"
true_p = None;
true_params = [None, None, None]
logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
est_params_df = valid_adata.var.loc[
valid_gene_names,
[
prefix + "alpha",
prefix + "beta",
prefix + "gamma",
"half_life",
],
]
est_params = [est_params_df.loc[:, 'alpha'].values, est_params_df.loc[:, 'beta'].values,
est_params_df.loc[:, 'gamma'].values]
if experiment_type == "kin":
if model == 'deterministic':
gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_variance, show_kin_parameters, )
elif model == 'stochastic':
gs = plot_kin_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_moms_fit, show_variance, show_kin_parameters, )
elif model == 'mixture':
gs = plot_kin_mix(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_variance, show_kin_parameters, )
elif model == 'mixture_deterministic_stochastic':
gs = plot_kin_mix_det_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_moms_fit, show_variance, show_kin_parameters, )
elif model == 'mixture_stochastic_stochastic':
gs = plot_kin_mix_sto_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_moms_fit, show_variance, show_kin_parameters, )
elif experiment_type == "deg":
if model == 'deterministic':
gs = plot_deg_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_variance, show_kin_parameters, )
elif model == 'stochastic':
gs = plot_deg_sto(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_moms_fit, show_variance, show_kin_parameters, )
else:
for i, gene_name in enumerate(valid_genes):
if model == "moment":
a, b, alpha_a, alpha_i, beta, gamma = valid_adata.var.loc[
gene_name,
[
prefix + "a",
prefix + "b",
prefix + "alpha_a",
prefix + "alpha_i",
prefix + "beta",
prefix + "gamma",
],
]
params = {
"a": a,
"b": b,
"alpha_a": alpha_a,
"alpha_i": alpha_i,
"beta": beta,
"gamma": gamma,
} # "la": 1, "si": 0,
mom = moments(*list(params.values()))
mom.integrate(t)
mom_data = (
mom.get_all_central_moments()
if has_splicing
else mom.get_nosplice_central_moments()
)
if true_param_prefix is not None:
(
true_a,
true_b,
true_alpha_a,
true_alpha_i,
true_beta,
true_gamma,
) = (
valid_adata.var.loc[gene_name, true_param_prefix + "a"]
if true_param_prefix + "a" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "b"]
if true_param_prefix + "b" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "alpha_a"]
if true_param_prefix + "alpha_a" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "alpha_i"]
if true_param_prefix + "alpha_i" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
if true_param_prefix + "beta" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_params = {
"a": true_a,
"b": true_b,
"alpha_a": true_alpha_a,
"alpha_i": true_alpha_i,
"beta": true_beta,
"gamma": true_gamma,
} # "la": 1, "si": 0,
true_mom = moments(*list(true_params.values()))
true_mom.integrate(t)
true_mom_data = (
true_mom.get_all_central_moments()
if has_splicing
else true_mom.get_nosplice_central_moments()
)
# n_mean, n_var = x_data[:2, :], x_data[2:, :]
if has_splicing:
tmp = (
[
valid_adata[:, gene_name].layers["M_ul"].A.T,
valid_adata.layers["M_sl"].A.T,
]
if "M_ul" in valid_adata.layers.keys()
else [
valid_adata[:, gene_name].layers["ul"].A.T,
valid_adata.layers["sl"].A.T,
]
)
x_data = [tmp[0].A, tmp[1].A] if issparse(tmp[0]) else tmp
if log_unnormalized and "X_ul" not in valid_adata.layers.keys():
x_data = [np.log(tmp[0] + 1), np.log(tmp[1] + 1)]
title_ = [
"(unspliced labeled)",
"(spliced labeled)",
"(unspliced labeled)",
"(spliced labeled)",
]
Obs_m = [valid_adata.uns["M_ul"], valid_adata.uns["M_sl"]]
Obs_v = [valid_adata.uns["V_ul"], valid_adata.uns["V_sl"]]
j_species = 2 # number of species
else:
tmp = (
valid_adata[:, gene_name].layers["X_new"].T
if "X_new" in valid_adata.layers.keys()
else valid_adata[:, gene_name].layers["new"].T
)
x_data = [tmp.A] if issparse(tmp) else [tmp]
if log_unnormalized and "X_new" not in valid_adata.layers.keys():
x_data = [np.log(x_data[0] + 1)]
# only use new key for calculation, so we only have M, V
title_ = [" (labeled)", " (labeled)"]
Obs_m, Obs_v = [valid_adata.uns["M"]], [valid_adata.uns["V"]]
j_species = 1
for j in range(sub_plot_n):
row_ind = int(
np.floor(i / ncols)
) # make sure all related plots for the same gene in the same column.
col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
(i % ncols - 1) * grp_len + 1
row_i, col_i = np.where(fig_mat == col_loc)
ax = plt.subplot(
gs[col_loc]
) if gene_order == 'column' else \
plt.subplot(
gs[fig_mat[col_i, row_i]]
)
if true_param_prefix is not None and j == 0:
if has_splicing:
ax.text(
0.05,
0.92,
r"$a$"
+ ": {0:.2f}; ".format(true_a)
+ r"$\hat a$"
+ ": {0:.2f} \n".format(a)
+ r"$b$"
+ ": {0:.2f}; ".format(true_b)
+ r"$\hat b$"
+ ": {0:.2f} \n".format(b)
+ r"$\alpha_a$"
+ ": {0:.2f}; ".format(true_alpha_a)
+ r"$\hat \alpha_a$"
+ ": {0:.2f} \n".format(alpha_a)
+ r"$\alpha_i$"
+ ": {0:.2f}; ".format(true_alpha_i)
+ r"$\hat \alpha_i$"
+ ": {0:.2f} \n".format(alpha_i)
+ r"$\beta$"
+ ": {0:.2f}; ".format(true_beta)
+ r"$\hat \beta$"
+ ": {0:.2f} \n".format(beta)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="left",
va="top",
transform=ax.transAxes,
)
else:
ax.text(
0.05,
0.92,
r"$a$"
+ ": {0:.2f}; ".format(true_a)
+ r"$\hat a$"
+ ": {0:.2f} \n".format(a)
+ r"$b$"
+ ": {0:.2f}; ".format(true_b)
+ r"$\hat b$"
+ ": {0:.2f} \n".format(b)
+ r"$\alpha_a$"
+ ": {0:.2f}; ".format(true_alpha_a)
+ r"$\hat \alpha_a$"
+ ": {0:.2f} \n".format(alpha_a)
+ r"$\alpha_i$"
+ ": {0:.2f}; ".format(true_alpha_i)
+ r"$\hat \alpha_i$"
+ ": {0:.2f} \n".format(alpha_i)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="left",
va="top",
transform=ax.transAxes,
)
if j < j_species:
ax.boxplot(
x=[x_data[j][i][T == std] for std in T_uniq],
positions=T_uniq,
widths=boxwidth,
showfliers=False,
showmeans=True,
) # x=T.values, y= # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
# ax.scatter(T_uniq, Obs_m[j][i], c='r') # ax1.plot(T, u.T, linestyle='None', marker='o', markersize=10)
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)") #
else:
ax.set_ylabel("Expression")
ax.plot(t, mom_data[j], "k--")
if true_param_prefix is not None:
ax.plot(t, true_mom_data[j], "r--")
else:
ax.scatter(T_uniq, Obs_v[j - j_species][i]) # , c='r'
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Variance (log expression)") #
else:
ax.set_ylabel("Variance")
ax.plot(t, mom_data[j], "k--")
if true_param_prefix is not None:
ax.plot(t, true_mom_data[j], "r--")
ax.set_xlabel("time (" + unit + ")")
ax.set_title(gene_name + " " + title_[j])
elif experiment_type == "deg":
if has_splicing:
layers = (
["X_uu", "X_ul", "X_su", "X_sl"]
if "X_ul" in valid_adata.layers.keys()
else ["uu", "ul", "su", "sl"]
)
uu, ul, su, sl = (
valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[1]],
valid_adata[:, gene_name].layers[layers[2]],
valid_adata[:, gene_name].layers[layers[3]],
)
uu, ul, su, sl = (
(
uu.toarray().squeeze(),
ul.toarray().squeeze(),
su.toarray().squeeze(),
sl.toarray().squeeze(),
)
if issparse(uu)
else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
)
if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
uu, ul, su, sl = (
np.log(uu + 1),
np.log(ul + 1),
np.log(su + 1),
np.log(sl + 1),
)
alpha, beta, gamma, ul0, sl0, uu0, half_life = valid_adata.var.loc[
gene_name,
[
prefix + "alpha",
prefix + "beta",
prefix + "gamma",
prefix + "ul0",
prefix + "sl0",
prefix + "uu0",
"half_life",
],
]
# $u$ - unlabeled, unspliced
# $s$ - unlabeled, spliced
# $w$ - labeled, unspliced
# $l$ - labeled, spliced
#
beta = sys.float_info.epsilon if beta == 0 else beta
gamma = sys.float_info.epsilon if gamma == 0 else gamma
u = sol_u(t, uu0, alpha, beta)
su0 = np.mean(su[T == np.min(T)]) # this should also be estimated
s = sol_s(t, su0, uu0, alpha, beta, gamma)
w = sol_u(t, ul0, 0, beta)
l = sol_s(t, sl0, ul0, 0, beta, gamma)
if true_param_prefix is not None:
true_alpha, true_beta, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
if true_param_prefix + "beta" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_u = sol_u(t, uu0, true_alpha, true_beta)
true_s = sol_s(t, su0, uu0, true_alpha, true_beta, true_gamma)
true_w = sol_u(t, ul0, 0, true_beta)
true_l = sol_s(t, sl0, ul0, 0, true_beta, true_gamma)
true_p = np.vstack((true_u, true_w, true_s, true_l))
title_ = [
"(unspliced unlabeled)",
"(unspliced labeled)",
"(spliced unlabeled)",
"(spliced labeled)",
]
Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
else:
layers = (
["X_new", "X_total"]
if "X_new" in valid_adata.layers.keys()
else ["new", "total"]
)
uu, ul = (
valid_adata[:, gene_name].layers[layers[1]]
- valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[0]],
)
uu, ul = (
(uu.toarray().squeeze(), ul.toarray().squeeze())
if issparse(uu)
else (uu.squeeze(), ul.squeeze())
)
if log_unnormalized and layers == ["new", "total"]:
uu, ul = np.log(uu + 1), np.log(ul + 1)
alpha, gamma, uu0, ul0, half_life = valid_adata.var.loc[
gene_name,
[
prefix + "alpha",
prefix + "gamma",
prefix + "uu0",
prefix + "ul0",
"half_life",
],
]
# require no beta functions
u = sol_u(t, uu0, alpha, gamma)
s = None # sol_s(t, su0, uu0, 0, 1, gamma)
w = sol_u(t, ul0, 0, gamma)
l = None # sol_s(t, 0, 0, alpha, 1, gamma)
title_ = ["(unlabeled)", "(labeled)"]
if true_param_prefix is not None:
true_alpha, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_u = sol_u(t, uu0, true_alpha, true_gamma)
true_w = sol_u(t, ul0, 0, true_gamma)
true_p = np.vstack((true_u, true_w))
Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))
for j in range(sub_plot_n):
row_ind = int(
np.floor(i / ncols)
) # make sure unlabled and labeled are in the same column.
col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
(i % ncols - 1) * grp_len + 1
row_i, col_i = np.where(fig_mat == col_loc)
ax = plt.subplot(
gs[col_loc]
) if gene_order == 'column' else \
plt.subplot(
gs[fig_mat[col_i, row_i]]
)
if true_param_prefix is not None and j == 0:
if has_splicing:
ax.text(
0.75,
0.50,
r"$\alpha$"
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\beta$"
+ ": {0:.2f}; ".format(true_beta)
+ r"$\hat \beta$"
+ ": {0:.2f} \n".format(beta)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="right",
va="top",
transform=ax.transAxes,
)
else:
ax.text(
0.75,
0.50,
r"$\alpha$"
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="right",
va="top",
transform=ax.transAxes,
)
ax.boxplot(
x=[Obs[j][T == std] for std in T_uniq],
positions=T_uniq,
widths=boxwidth,
showfliers=False,
showmeans=True,
)
ax.plot(t, Pred[j], "k--")
if true_param_prefix is not None:
ax.plot(t, true_p[j], "r--")
if j == sub_plot_n - 1:
ax.text(
0.8,
0.8,
r"$t_{1/2} = $" + "{0:.2f}".format(half_life) + unit[0],
ha="right",
va="top",
transform=ax.transAxes,
)
ax.set_xlabel("time (" + unit + ")")
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)")
else:
ax.set_ylabel("Expression")
ax.set_title(gene_name + " " + title_[j])
elif experiment_type == "kin":
if model == 'deterministic':
logLL = valid_adata.var.loc[valid_gene_names, prefix + 'logLL']
alpha, beta, gamma, half_life = valid_adata.var.loc[
gene_name,
[
prefix + "alpha",
prefix + "beta",
prefix + "gamma",
"half_life",
],
]
est_params = [alpha, beta, gamma]
gs = plot_kin_det(valid_adata, valid_gene_names, has_splicing, use_smoothed, log_unnormalized,
t, T, T_uniq, unit, X_data, X_fit_data, logLL, true_p,
grp_len, sub_plot_n, ncols, boxwidth, gs, fig_mat, gene_order, y_log_scale,
true_param_prefix, true_params, est_params,
show_variance, show_kin_parameters, )
elif model == 'stochastic':
if has_splicing:
pass
else:
pass
elif model == 'mixture':
if has_splicing:
pass
else:
pass
elif model == 'mixture_deterministic_stochastic':
if has_splicing:
pass
else:
pass
elif model == 'mixture_stochastic_stochastic':
if has_splicing:
pass
else:
pass
else:
if has_splicing:
layers = (
["X_uu", "X_ul", "X_su", "X_sl"]
if "X_ul" in valid_adata.layers.keys()
else ["uu", "ul", "su", "sl"]
)
uu, ul, su, sl = (
valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[1]],
valid_adata[:, gene_name].layers[layers[2]],
valid_adata[:, gene_name].layers[layers[3]],
)
uu, ul, su, sl = (
(
uu.toarray().squeeze(),
ul.toarray().squeeze(),
su.toarray().squeeze(),
sl.toarray().squeeze(),
)
if issparse(uu)
else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
)
if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
uu, ul, su, sl = (
np.log(uu + 1),
np.log(ul + 1),
np.log(su + 1),
np.log(sl + 1),
)
alpha, beta, gamma, uu0, su0 = valid_adata.var.loc[
gene_name,
[
prefix + "alpha",
prefix + "beta",
prefix + "gamma",
prefix + "uu0",
prefix + "su0",
],
]
# $u$ - unlabeled, unspliced
# $s$ - unlabeled, spliced
# $w$ - labeled, unspliced
# $l$ - labeled, spliced
#
beta = sys.float_info.epsilon if beta == 0 else beta
gamma = sys.float_info.epsilon if gamma == 0 else gamma
u = sol_u(t, uu0, 0, beta)
s = sol_s(t, su0, uu0, 0, beta, gamma)
w = sol_u(t, 0, alpha, beta)
l = sol_s(t, 0, 0, alpha, beta, gamma)
if true_param_prefix is not None:
true_alpha, true_beta, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
if true_param_prefix + "beta" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_u = sol_u(t, uu0, 0, true_beta)
true_s = sol_s(t, su0, uu0, 0, true_beta, true_gamma)
true_w = sol_u(t, 0, true_alpha, true_beta)
true_l = sol_s(t, 0, 0, true_alpha, true_beta, true_gamma)
true_p = np.vstack((true_u, true_w, true_s, true_l))
title_ = [
"(unspliced unlabeled)",
"(unspliced labeled)",
"(spliced unlabeled)",
"(spliced labeled)",
]
Obs, Pred = np.vstack((uu, ul, su, sl)), np.vstack((u, w, s, l))
else:
layers = (
["X_new", "X_total"]
if "X_new" in valid_adata.layers.keys()
else ["new", "total"]
)
uu, ul = (
valid_adata[:, gene_name].layers[layers[1]]
- valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[0]],
)
uu, ul = (
(uu.toarray().squeeze(), ul.toarray().squeeze())
if issparse(uu)
else (uu.squeeze(), ul.squeeze())
)
if log_unnormalized and layers == ["new", "total"]:
uu, ul = np.log(uu + 1), np.log(ul + 1)
alpha, gamma, uu0 = valid_adata.var.loc[
gene_name, [prefix + "alpha", prefix + "gamma", prefix + "uu0"]
]
# require no beta functions
u = sol_u(t, uu0, 0, gamma)
s = None # sol_s(t, su0, uu0, 0, 1, gamma)
w = sol_u(t, 0, alpha, gamma)
l = None # sol_s(t, 0, 0, alpha, 1, gamma)
if true_param_prefix is not None:
true_alpha, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_u = sol_u(t, uu0, 0, true_gamma)
true_w = sol_u(t, 0, true_alpha, true_gamma)
true_p = np.vstack((true_u, true_w))
title_ = ["(unlabeled)", "(labeled)"]
Obs, Pred = np.vstack((uu, ul)), np.vstack((u, w))
for j in range(sub_plot_n):
row_ind = int(
np.floor(i / ncols)
) # make sure unlabled and labeled are in the same column.
col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
(i % ncols - 1) * grp_len + 1
row_i, col_i = np.where(fig_mat == col_loc)
ax = plt.subplot(
gs[col_loc]
) if gene_order == 'column' else \
plt.subplot(
gs[fig_mat[col_i, row_i]]
)
if true_param_prefix is not None and j == 0:
if has_splicing:
ax.text(
0.75,
0.90,
r"$\alpha$"
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\beta$"
+ ": {0:.2f}; ".format(true_beta)
+ r"$\hat \beta$"
+ ": {0:.2f} \n".format(beta)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="right",
va="top",
transform=ax.transAxes,
)
else:
ax.text(
0.75,
0.90,
r"$\alpha$"
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="right",
va="top",
transform=ax.transAxes,
)
ax.boxplot(
x=[Obs[j][T == std] for std in T_uniq],
positions=T_uniq,
widths=boxwidth,
showfliers=False,
showmeans=True,
)
ax.plot(t, Pred[j], "k--")
if true_param_prefix is not None:
ax.plot(t, true_p[j], "k--")
ax.set_xlabel("time (" + unit + ")")
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)")
else:
ax.set_ylabel("Expression")
ax.set_title(gene_name + " " + title_[j])
elif experiment_type == "one_shot":
if has_splicing:
layers = (
["X_uu", "X_ul", "X_su", "X_sl"]
if "X_ul" in valid_adata.layers.keys()
else ["uu", "ul", "su", "sl"]
)
uu, ul, su, sl = (
valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[1]],
valid_adata[:, gene_name].layers[layers[2]],
valid_adata[:, gene_name].layers[layers[3]],
)
uu, ul, su, sl = (
(
uu.toarray().squeeze(),
ul.toarray().squeeze(),
su.toarray().squeeze(),
sl.toarray().squeeze(),
)
if issparse(uu)
else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
)
if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
uu, ul, su, sl = (
np.log(uu + 1),
np.log(ul + 1),
np.log(su + 1),
np.log(sl + 1),
)
alpha, beta, gamma, U0, S0 = valid_adata.var.loc[
gene_name,
[
prefix + "alpha",
prefix + "beta",
prefix + "gamma",
prefix + "U0",
prefix + "S0",
],
]
# $u$ - unlabeled, unspliced
# $s$ - unlabeled, spliced
# $w$ - labeled, unspliced
# $l$ - labeled, spliced
#
beta = sys.float_info.epsilon if beta == 0 else beta
gamma = sys.float_info.epsilon if gamma == 0 else gamma
U_sol = sol_u(t, U0, 0, beta)
S_sol = sol_u(t, S0, 0, gamma)
l = sol_u(t, 0, alpha, beta) + sol_s(t, 0, 0, alpha, beta, gamma)
L = sl + ul
if true_param_prefix is not None:
true_alpha, true_beta, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "beta"]
if true_param_prefix + "beta" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_l = sol_u(t, 0, true_alpha, true_beta) + sol_s(
t, 0, 0, true_alpha, true_beta, true_gamma
)
title_ = ["labeled"]
else:
layers = (
["X_new", "X_total"]
if "X_new" in valid_adata.layers.keys()
else ["new", "total"]
)
uu, ul = (
valid_adata[:, gene_name].layers[layers[1]]
- valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[0]],
)
uu, ul = (
(uu.toarray().squeeze(), ul.toarray().squeeze())
if issparse(uu)
else (uu.squeeze(), ul.squeeze())
)
if log_unnormalized and layers == ["new", "total"]:
uu, ul = np.log(uu + 1), np.log(ul + 1)
alpha, gamma, total0 = valid_adata.var.loc[
gene_name,
[prefix + "alpha", prefix + "gamma", prefix + "total0"],
]
# require no beta functions
old = sol_u(t, total0, 0, gamma)
s = None # sol_s(t, su0, uu0, 0, 1, gamma)
w = None
l = sol_u(t, 0, alpha, gamma) # sol_s(t, 0, 0, alpha, 1, gamma)
L = ul
if true_param_prefix is not None:
true_alpha, true_gamma = (
valid_adata.var.loc[gene_name, true_param_prefix + "alpha"]
if true_param_prefix + "alpha" in valid_adata.var_keys()
else -np.inf,
valid_adata.var.loc[gene_name, true_param_prefix + "gamma"]
if true_param_prefix + "gamma" in valid_adata.var_keys()
else -np.inf,
)
true_l = sol_u(
t, 0, true_alpha, true_gamma
) # sol_s(t, 0, 0, alpha, 1, gamma)
title_ = ["labeled"]
Obs, Pred = np.hstack((np.zeros(L.shape), L)), np.hstack(l)
if true_param_prefix is not None:
true_p = np.hstack(true_l)
row_ind = int(
np.floor(i / ncols)
) # make sure unlabled and labeled are in the same column.
col_loc = (row_ind * sub_plot_n) * ncols * grp_len + \
(i % ncols - 1) * grp_len + 1
row_i, col_i = np.where(fig_mat == col_loc)
ax = plt.subplot(
gs[col_loc]
) if gene_order == 'column' else \
plt.subplot(
gs[fig_mat[col_i, row_i]]
)
if true_param_prefix is not None:
if has_splicing:
ax.text(
0.05,
0.90,
r"$\alpha$ "
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\beta$"
+ ": {0:.2f}; ".format(true_beta)
+ r"$\hat \beta$"
+ ": {0:.2f} \n".format(beta)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="left",
va="top",
transform=ax.transAxes,
)
else:
ax.text(
0.05,
0.90,
r"$\alpha$"
+ ": {0:.2f}; ".format(true_alpha)
+ r"$\hat \alpha$"
+ ": {0:.2f} \n".format(alpha)
+ r"$\gamma$"
+ ": {0:.2f}; ".format(true_gamma)
+ r"$\hat \gamma$"
+ ": {0:.2f} \n".format(gamma),
ha="left",
va="top",
transform=ax.transAxes,
)
ax.boxplot(
x=[
Obs[np.hstack((np.zeros_like(T), T)) == std]
for std in [0, T_uniq[0]]
],
positions=[0, T_uniq[0]],
widths=boxwidth,
showfliers=False,
showmeans=True,
)
ax.plot(t, Pred, "k--")
if true_param_prefix is not None:
ax.plot(t, true_p, "r--")
ax.set_xlabel("time (" + unit + ")")
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)")
else:
ax.set_ylabel("Expression")
ax.set_title(gene_name + " " + title_[0])
elif experiment_type == "mix_std_stm":
if has_splicing:
layers = (
["X_uu", "X_ul", "X_su", "X_sl"]
if "X_ul" in valid_adata.layers.keys()
else ["uu", "ul", "su", "sl"]
)
uu, ul, su, sl = (
valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[1]],
valid_adata[:, gene_name].layers[layers[2]],
valid_adata[:, gene_name].layers[layers[3]],
)
uu, ul, su, sl = (
(
uu.toarray().squeeze(),
ul.toarray().squeeze(),
su.toarray().squeeze(),
sl.toarray().squeeze(),
)
if issparse(uu)
else (uu.squeeze(), ul.squeeze(), su.squeeze(), sl.squeeze())
)
if log_unnormalized and layers == ["uu", "ul", "su", "sl"]:
uu, ul, su, sl = (
np.log(uu + 1),
np.log(ul + 1),
np.log(su + 1),
np.log(sl + 1),
)
beta, gamma, alpha_std = valid_adata.var.loc[
gene_name,
[prefix + "beta", prefix + "gamma", prefix + "alpha_std"],
]
alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]
alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)
# $u$ - unlabeled, unspliced
# $s$ - unlabeled, spliced
# $w$ - labeled, unspliced
# $l$ - labeled, spliced
#
# calculate labeled unspliced and spliced mRNA amount
u1, s1, u1_, s1_ = (
np.zeros(len(t) - 1),
np.zeros(len(t) - 1),
np.zeros(len(T_uniq) - 1),
np.zeros(len(T_uniq) - 1),
)
for ind in np.arange(1, len(t)):
t_i = t[ind]
u0 = sol_u(np.max(t) - t_i, 0, alpha_std, beta)
alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
u1[ind - 1], s1[ind - 1] = (
sol_u(t_i, u0, alpha_stm_t_i, beta),
sol_u(np.max(t), 0, beta, gamma),
)
for ind in np.arange(1, len(T_uniq)):
t_i = T_uniq[ind]
u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, beta)
alpha_stm_t_i = alpha_stm[ind - 1]
u1_[ind - 1], s1_[ind - 1] = (
sol_u(t_i, u0, alpha_stm_t_i, beta),
sol_u(np.max(T_uniq), 0, beta, gamma),
)
Obs, Pred, Pred_ = (
np.vstack((ul, sl, uu, su)),
np.vstack((u1.reshape(1, -1), s1.reshape(1, -1))),
np.vstack((u1_.reshape(1, -1), s1_.reshape(1, -1))),
)
j_species, title_ = (
4,
[
"unspliced labeled (new)",
"spliced labeled (new)",
"unspliced unlabeled (old)",
"spliced unlabeled (old)",
"alpha (steady state vs. stimulation)",
],
)
else:
layers = (
["X_new", "X_total"]
if "X_new" in valid_adata.layers.keys()
else ["new", "total"]
)
uu, ul = (
valid_adata[:, gene_name].layers[layers[1]]
- valid_adata[:, gene_name].layers[layers[0]],
valid_adata[:, gene_name].layers[layers[0]],
)
uu, ul = (
(uu.toarray().squeeze(), ul.toarray().squeeze())
if issparse(uu)
else (uu.squeeze(), ul.squeeze())
)
if log_unnormalized and layers == ["new", "total"]:
uu, ul = np.log(uu + 1), np.log(ul + 1)
gamma, alpha_std = valid_adata.var.loc[
gene_name, [prefix + "gamma", prefix + "alpha_std"]
]
alpha_stm = valid_adata[:, gene_name].varm[prefix + "alpha"].flatten()[1:]
alpha_stm0, k, _ = solve_first_order_deg(T_uniq[1:], alpha_stm)
# require no beta functions
u1, u1_ = (
np.zeros(len(t) - 1),
np.zeros(len(T_uniq) - 1),
) # interpolation or original time point
for ind in np.arange(1, len(t)):
t_i = t[ind]
u0 = sol_u(np.max(t) - t_i, 0, alpha_std, gamma)
alpha_stm_t_i = alpha_stm0 * np.exp(-k * t_i)
u1[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)
for ind in np.arange(1, len(T_uniq)):
t_i = T_uniq[ind]
u0 = sol_u(np.max(T_uniq) - t_i, 0, alpha_std, gamma)
alpha_stm_t_i = alpha_stm[ind - 1]
u1_[ind - 1] = sol_u(t_i, u0, alpha_stm_t_i, gamma)
Obs, Pred, Pred_ = (
np.vstack((ul, uu)),
np.vstack((u1.reshape(1, -1))),
np.vstack((u1_.reshape(1, -1))),
)
j_species, title_ = (
2,
[
"labeled (new)",
"unlabeled (old)",
"alpha (steady state vs. stimulation)",
],
)
group_list = [np.repeat(alpha_std, len(alpha_stm)), alpha_stm]
for j in range(sub_plot_n):
row_ind = int(
np.floor(i / ncols)
) # make sure all related plots for the same gene in the same column.
col_loc = (row_ind * sub_plot_n + j) * ncols * grp_len + \
(i % ncols - 1) * grp_len + 1
row_i, col_i = np.where(fig_mat == col_loc)
ax = plt.subplot(
gs[col_loc]
) if gene_order == 'column' else \
plt.subplot(
gs[fig_mat[col_i, row_i]]
)
if j < j_species / 2:
ax.boxplot(
x=[Obs[j][T == std] for std in T_uniq[1:]],
positions=T_uniq[1:],
widths=boxwidth,
showfliers=False,
showmeans=True,
)
ax.plot(t[1:], Pred[j], "k--")
ax.scatter(T_uniq[1:], Pred_[j], s=20, c="red")
ax.set_xlabel("time (" + unit + ")")
ax.set_title(gene_name + ": " + title_[j])
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)")
else:
ax.set_ylabel("Expression")
elif j < j_species:
ax.boxplot(
x=[Obs[j][T == std] for std in T_uniq],
positions=T_uniq,
widths=boxwidth,
showfliers=False,
showmeans=True,
)
ax.set_xlabel("time (" + unit + ")")
ax.set_title(gene_name + ": " + title_[j])
if y_log_scale:
ax.set_yscale("log")
if log_unnormalized:
ax.set_ylabel("Expression (log)")
else:
ax.set_ylabel("Expression")
else:
x = T_uniq[1:] # the label locations
group_width = barwidth / 2
bar_coord, group_name, group_ind = (
[-1, 1],
["steady state", "stimulation"],
0,
)
for group_ind in range(len(group_list)):
cur_group = group_list[group_ind]
ax.bar(
x + bar_coord[group_ind] * group_width,
cur_group,
barwidth,
label=group_name[group_ind],
)
# Add gene name, experimental type, etc.
ax.set_xlabel("time (" + unit + ")")
ax.set_ylabel("alpha (translation rate)")
ax.set_xticks(x)
ax.set_xticklabels(x)
group_ind += 1
ax.legend()
ax.set_xlabel("time (" + unit + ")")
ax.set_title(gene_name + ": " + title_[j])
elif experiment_type == "multi_time_series":
pass # group by different groups
elif experiment_type == "coassay":
pass # show protein velocity (steady state and the Gamma distribution model)
g.autofmt_xdate(rotation=-30, ha='right')
if save_show_or_return == "save":
s_kwargs = {"path": None, "prefix": 'dynamics', "dpi": None,
"ext": 'pdf', "transparent": True, "close": True, "verbose": True}
s_kwargs = update_dict(s_kwargs, save_kwargs)
save_fig(**s_kwargs)
elif save_show_or_return == "show":
plt.tight_layout()
plt.show()
elif save_show_or_return == "return":
return g
def dynamics_(
adata,
gene_names,
color,
dims=[0, 1],
current_layer="spliced",
use_raw=False,
Vkey="S",
Ekey="spliced",
basis="umap",
mode="all",
cmap=None,
gs=None,
**kwargs
):
"""
Parameters
----------
adata
basis
mode: `str` (default: all)
Support mode includes: phase, expression, velocity, all
Returns
-------
"""
import matplotlib.pyplot as plt
genes = list(set(gene_names).intersection(adata.var.index))
for i, gn in enumerate(genes):
ax = plt.subplot(gs[i * 3])
try:
ix = np.where(adata.var["Gene"] == gn)[0][0]
except:
continue
scatters(
adata,
gene_names,
color,
dims=[0, 1],
current_layer="spliced",
use_raw=False,
Vkey="S",
Ekey="spliced",
basis="umap",
mode="all",
cmap=None,
gs=None,
**kwargs
)
scatters(
adata,
gene_names,
color,
dims=[0, 1],
current_layer="spliced",
use_raw=False,
Vkey="S",
Ekey="spliced",
basis="umap",
mode="all",
cmap=None,
gs=None,
**kwargs
)
scatters(
adata,
gene_names,
color,
dims=[0, 1],
current_layer="spliced",
use_raw=False,
Vkey="S",
Ekey="spliced",
basis="umap",
mode="all",
cmap=None,
gs=None,
**kwargs
)
plt.tight_layout()