scPhyloX.tools

Submodules

• scPhyloX.tools.PhyloCircular
• scPhyloX.tools.data_factory
• scPhyloX.tools.utils

Attributes

population_size
tumor_data
tumor_size

Functions

corr_plot(x, y, ax[, stats, r0_x, r0_y, r1_x, r1_y, ...])	Draw a scatter plot of the two sets of data and show their correlation coefficients
colless_index(tree)	calculate Colless' index
colless_index_corrected(tree)	calculate corrected Colless' index
ext_gen(cell)	Extract generation with given cell name
ext_cid(cell)	Extract cell id with given cell name
reconstruct(lineage_info, sel_cells[, file_name])	Reconstruct phylogenetic tree of simulation data
cell_number_calc(w, l, r[, coef])	Calculate tumor cell number with given width, height and scale
get_branchlen([seqtab, sys])	Calculate LP distance with given mutation character matrix
get_mutnum([seqtab, sys, filter_trunk])	Calculate LR distance with given mutation character matrix
mutnum_fly(seqs, ref)	calculate LR distance for SMALT-fly dataset
branchlen_fly(seqs, ref[, rs])	calculate LP distance for SMALT-fly dataset
polar_plot(tree[, ax, arc, start, root_distance, ...])

Package Contents

corr_plot(x, y, ax, stats='pearson', r0_x=None, r0_y=None, r1_x=None, r1_y=None, line='fit', alternative='two-sided', fontsize=10)

Draw a scatter plot of the two sets of data and show their correlation coefficients

Args:

x:

data1

y:

data2

ax:

axes to draw scatter on

stats:

pearson or spearman

r0_x, r0_y, r1_x, r1_y:

locations to label the correlation coefficient and the p-value

line:

fit or diag, fit for linear regression

alternative:

greater, less or two-sided

fontsize:

fontsize

Return:

matplotlib.axes

colless_index(tree)

calculate Colless’ index

Args:

tree:

Phylogenetic tree

Return:

float:

Colless’ index

colless_index_corrected(tree)

calculate corrected Colless’ index

Args:

tree:

Phylogenetic tree

Return:

float:

corrected Colless’ index

ext_gen(cell)

Extract generation with given cell name

ext_cid(cell)

Extract cell id with given cell name

reconstruct(lineage_info, sel_cells, file_name=None)

Reconstruct phylogenetic tree of simulation data

Args:

lineage_info:

system.lineage_info

sel_cells:

cells to build phylogenetic tree

file_name:

path to save tree. if None, just return

Return:

text newick tree

population_size

cell_number_calc(w, l, r, coef=100000000.0)

Calculate tumor cell number with given width, height and scale

Args:

w:

width

l:

heigth

r:

scale

coef:

cell density

Return:

float:

cell number

tumor_data

tumor_size

get_branchlen(seqtab: numpy.ndarray = None, sys=None)

Calculate LP distance with given mutation character matrix

Args:

seqtab:

mutation character matrix

sys:

Gillespie simulator with results

Return:

np.array:

LP distance

get_mutnum(seqtab=None, sys=None, filter_trunk=True)

Calculate LR distance with given mutation character matrix

Args:

seqtab:

mutation character matrix

sys:

Gillespie simulator with results

filter_trunk:

Filter trunk mutations

Return:

np.array:

LR distance

mutnum_fly(seqs, ref)

calculate LR distance for SMALT-fly dataset

Args:

seqs:

DNA muataion character matrix

ref:

reference seuence

Return:

np.array:

LR distance

branchlen_fly(seqs, ref, rs=1)

calculate LP distance for SMALT-fly dataset

Args:

seqs:

DNA muataion character matrix

ref:

reference seuence

rs:

resampling ratio

Return:

np.array:

LP distance

polar_plot(tree, ax=None, arc=350, start=0, root_distance=0.1, label_leaf=True, patch_leaf=True, wedge=True, pad_label=0, pad_patch=0, pad_wedge=0, externals=[], lw=1)