5 from typing
import NamedTuple
7 PlatformTuple
= ('Illumina', 'Salus')
11 'sub' : 'Mouse Brain Sptial',
13 'prefix' : '/share/result/spatial/data/BoAo_sp',
14 'suffixOut': dict.fromkeys(PlatformTuple
,"outs"),
15 'suffixMtx': 'filtered_feature_bc_matrix',
16 'platforms': {PlatformTuple
[0]:'illumina', PlatformTuple
[1]: 'salus'},
17 'pattern': ('prefix', 'platformV', 'sid', 'suffixOutV', 'suffixMtx')
21 'sub' : 'Mouse Kindey Sptial',
23 'prefix' : '/share/result/spatial/data/BoAo_sp',
24 'suffixOut': dict.fromkeys(PlatformTuple
,"outs"),
25 'suffixMtx': 'filtered_feature_bc_matrix',
26 'platforms': {PlatformTuple
[0]:'illumina', PlatformTuple
[1]: 'salus'},
27 'pattern': ('prefix', 'platformV', 'sid', 'suffixOutV', 'suffixMtx')
31 'sub' : 'Human Single Cell',
33 'prefix' : '/share/result/spatial/data/MoZhuo_sc/FX20230913',
34 'suffixOut': {PlatformTuple
[0]: 'out/R22045213-220914-LYY-S11-R03-220914-LYY-S11-R03_combined_outs',
35 PlatformTuple
[1]: 'out_subset/20221124-LYY-S09-R03_AGGCAGAA_fastq_outs'},
36 'suffixMtx': 'filtered_cell_gene_matrix',
37 'platforms': {PlatformTuple
[0]:'illumina', PlatformTuple
[1]: 'sailu'},
38 'pattern': ('prefix', 'platformV', 'suffixOutV', 'suffixMtx')
42 def checkModules() -> None:
43 import importlib
.metadata
44 from packaging
import version
47 got_ver
= importlib
.metadata
.version(pkgname
)
48 if version
.parse(got_ver
) < version
.parse(min_ver
):
49 raise importlib
.VersionConflict(f
"{pkgname}>={min_ver} is needed, but found {pkgname}=={got_ver}")
51 if __name__
== "__main__":
54 if thisID
not in SamplesDict
:
55 print(f
"[x]sid can only be {SamplesDict.keys()}", file=sys
.stderr
)
59 print(sys
.argv
, file=sys
.stderr
)
64 import matplotlib
; matplotlib
.use("module://mplcairo.base")
65 from matplotlib
import pyplot
as plt
68 plt
.rcParams
['figure.figsize'] = (6.0, 6.0) # set default size of plots
69 font
= {'family' : 'STIX Two Text',
72 matplotlib
.rc('font', **font
)
76 import fast_matrix_market
85 class scDatItem(NamedTuple
):
91 def __repr__(self
) -> str:
92 return f
'[sc:{self.name}, Raw_BC*Gene={self.bgRaw[0]}x{self.bgRaw[1]}, NonZero_BC*Gene={self.bgFlt[0]}x{self.bgFlt[1]} ({self.annDat.n_obs}x{self.annDat.n_vars})]'
95 nfoDict
= SamplesDict
[thisID
]
96 print("[i]Start.", file=sys
.stderr
)
97 for platform
in PlatformTuple
:
98 nfoDict
['platformK'] = platform
99 nfoDict
['platformV'] = nfoDict
['platforms'][platform
]
100 nfoDict
['suffixOutV'] = nfoDict
['suffixOut'][platform
]
101 mtxPath
= os
.path
.join( *[nfoDict
[v
] for v
in nfoDict
['pattern']] )
102 print(f
"[i]Reading {mtxPath}", file=sys
.stderr
)
103 adata
=sc
.read_10x_mtx(mtxPath
, var_names
='gene_symbols', make_unique
=True, gex_only
=True)
104 adata
.var_names_make_unique() # this is necessary if using `var_names='gene_symbols'` in `sc.read_10x_mtx`
105 nnRaw
= (adata
.n_obs
,adata
.n_vars
)
106 adata
.var
['mt'] = adata
.var_names
.str.startswith('MT-') | adata
.var_names
.str.startswith('mt-')
107 sc
.pp
.calculate_qc_metrics(adata
, qc_vars
=['mt'], percent_top
=None, log1p
=True, inplace
=True)
108 sc
.pp
.filter_cells(adata
, min_genes
=1)
109 sc
.pp
.filter_genes(adata
, min_cells
=1)
110 nnFlt
= (adata
.n_obs
,adata
.n_vars
)
111 scDat
.append(scDatItem(platform
,nnRaw
,nnFlt
,adata
))
113 print("\n".join(map(str,scDat
)))
115 with pd
.option_context("mode.copy_on_write", True):
116 obsmbi
= scDat
[0].annDat
.obs
[['n_genes_by_counts', 'total_counts']].copy(deep
=False)
117 obsmbs
= scDat
[1].annDat
.obs
[['n_genes_by_counts', 'total_counts']].copy(deep
=False)
118 p1df
= pd
.concat([obsmbi
.assign(Platform
=scDat
[0].name
), obsmbs
.assign(Platform
=scDat
[1].name
)], ignore_index
=True).replace([np
.inf
, -np
.inf
, 0], np
.nan
).dropna()
119 p2df
= obsmbi
.join(obsmbs
,lsuffix
='_'+scDat
[0].name
,rsuffix
='_'+scDat
[1].name
,how
='inner').replace([np
.inf
, -np
.inf
, 0], np
.nan
).dropna()
120 p3tuple
= (frozenset(scDat
[0].annDat
.var_names
), frozenset(scDat
[1].annDat
.var_names
))
122 print("[i]Begin fig A. 1D", file=sys
.stderr
)
123 custom_params
= {"axes.spines.right": False, "axes.spines.top": False}
124 sns
.set_theme(style
="ticks", rc
=custom_params
, font
="STIX Two Text")
125 figA
=sns
.JointGrid(data
=p1df
, x
="total_counts", y
="n_genes_by_counts", hue
='Platform', dropna
=True)
126 #figA.plot(sns.scatterplot, sns.histplot, alpha=.7, edgecolor=".2", linewidth=.5)
127 figA
.plot_joint(sns
.scatterplot
, s
=12.7, alpha
=.6)
128 figA
.plot_marginals(sns
.histplot
, kde
=True, alpha
=.618)
129 figA
.figure
.suptitle(f
"Gene to UMI plot - {nfoDict['sub']}")
130 figA
.set_axis_labels(xlabel
='UMIs per Barcode', ylabel
='Genes per Barcode')
131 figA
.savefig(f
"1D_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'Gene to UMI plot', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
133 print("[i]Begin fig B. 1E", file=sys
.stderr
)
134 figB
=sns
.JointGrid(data
=p2df
, x
="total_counts_Illumina", y
="total_counts_Salus", dropna
=True)
135 figB
.plot_joint(sns
.scatterplot
, s
=12.7, alpha
=.6)
136 figB
.plot_marginals(sns
.histplot
, kde
=True, alpha
=.618)
137 figB
.figure
.suptitle(f
"UMI per Barcode Counts Comparing - {nfoDict['sub']}")
138 figB
.set_axis_labels(xlabel
='UMI Counts from Illumina', ylabel
='UMI Counts from Salus')
139 figB
.savefig(f
"1E_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'UMI per Barcode Counts Comparing', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
141 print("[i]Begin fig . 1F", file=sys
.stderr
)
142 from matplotlib_venn
import venn2
143 plt
.figure(figsize
=(4,4))
144 plt
.title("Sample Venn diagram")
145 p3intersection
= p3tuple
[0] & p3tuple
[1]
146 p3veen
= (p3tuple
[0]-p3intersection
, p3tuple
[1]-p3intersection
, p3intersection
)
147 GenesA
= scDat
[0].annDat
.var
.loc
[p3veen
[0]-p3veen
[2]]
148 GenesB
= scDat
[1].annDat
.var
.loc
[p3veen
[1]-p3veen
[2]]
149 GenesC
= scDat
[0].annDat
.var
.loc
[p3veen
[2]]
150 p3vd
=venn2(subsets
=tuple(map(len,p3veen
)), set_labels
=(scDat
[0].name
, scDat
[1].name
))
151 plt
.savefig(f
"1F_Genes_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'Veen of Genes', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
152 GenesA
.to_csv(f
"1F_Genes_{nfoDict['sid']}_{scDat[0].name}_only.csv",encoding
='utf-8')
153 GenesB
.to_csv(f
"1F_Genes_{nfoDict['sid']}_{scDat[1].name}_only.csv",encoding
='utf-8')
154 GenesC
.to_csv(f
"1F_Genes_{nfoDict['sid']}_intersection.csv.zst",encoding
='utf-8',compression
={'method': 'zstd', 'level': 9, 'write_checksum': True})
156 print("[i]Begin fig C. 2A", file=sys
.stderr
)
157 # https://www.kaggle.com/code/lizabogdan/top-correlated-genes?scriptVersionId=109838203&cellId=21
158 p4xdf
= scDat
[0].annDat
.to_df()
159 p4ydf
= scDat
[1].annDat
.to_df()
160 p4corraw
= p4xdf
.corrwith(p4ydf
,axis
=1)
161 p4corr
= p4corraw
.dropna()
162 plt
.figure(figsize
=(6,4))
163 plt
.title("Pearson correlation")
164 figC
=sns
.histplot(p4corr
,stat
='percent',binwidth
=0.01)
165 plt
.savefig(f
"2A_Correlation_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'Pearson correlation', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
167 print("[i]Begin fig D. 2B", file=sys
.stderr
)
168 var_names
= scDat
[0].annDat
.var_names
.intersection(scDat
[1].annDat
.var_names
)
169 xadata
= scDat
[0].annDat
[:, var_names
]
170 yadata
= scDat
[1].annDat
[:, var_names
]
171 xdf
=getOBSMdf(xadata
)
172 ydf
=getOBSMdf(yadata
)
173 #p4df = xdf.assign(Platform=scDat[0].name).join(ydf.assign(Platform=scDat[1].name),lsuffix='_'+scDat[0].name,rsuffix='_'+scDat[1].name,how='inner')
174 p4df
= pd
.concat([xdf
.assign(Platform
=scDat
[0].name
), ydf
.assign(Platform
=scDat
[1].name
)], ignore_index
=True).replace([np
.inf
, -np
.inf
, 0], np
.nan
).dropna()
175 figD
=sns
.JointGrid(data
=p4df
, x
="P1", y
="P2", hue
='Platform', dropna
=True)
176 figD
.plot_joint(sns
.scatterplot
, s
=12.7, alpha
=.6)
177 figD
.plot_marginals(sns
.histplot
, kde
=True, alpha
=.618)
178 figD
.figure
.suptitle(f
"PCA - {nfoDict['sub']}")
179 figD
.set_axis_labels(xlabel
='PC1', ylabel
='PC2')
180 figD
.savefig(f
"2B_PCA_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'PCA', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
181 print("[i]Begin fig E. 2C", file=sys
.stderr
)
182 xdf
=getOBSMdf(xadata
,'X_umap')
183 ydf
=getOBSMdf(yadata
,'X_umap')
184 p5df
= pd
.concat([xdf
.assign(Platform
=scDat
[0].name
), ydf
.assign(Platform
=scDat
[1].name
)], ignore_index
=True).replace([np
.inf
, -np
.inf
, 0], np
.nan
).dropna()
185 figE
=sns
.JointGrid(data
=p5df
, x
="P1", y
="P2", hue
='Platform', dropna
=True)
186 figE
.plot_joint(sns
.scatterplot
, s
=12.7, alpha
=.6)
187 figE
.plot_marginals(sns
.histplot
, kde
=True, alpha
=.618)
188 figE
.figure
.suptitle(f
"UMAP - {nfoDict['sub']}")
189 figE
.set_axis_labels(xlabel
='UMAP1', ylabel
='UMAP2')
190 figE
.savefig(f
"2C_UMAP_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'UMAP', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
191 print("[i]Begin fig E. 2Cn", file=sys
.stderr
)
192 xdf
=getOBSMdf(xadata
,'X_draw_graph_fa')
193 ydf
=getOBSMdf(yadata
,'X_draw_graph_fa')
194 p5df
= pd
.concat([xdf
.assign(Platform
=scDat
[0].name
), ydf
.assign(Platform
=scDat
[1].name
)], ignore_index
=True).replace([np
.inf
, -np
.inf
, 0], np
.nan
).dropna()
195 figE
=sns
.JointGrid(data
=p5df
, x
="P1", y
="P2", hue
='Platform', dropna
=True)
196 figE
.plot_joint(sns
.scatterplot
, s
=12.7, alpha
=.6)
197 figE
.plot_marginals(sns
.histplot
, kde
=True, alpha
=.618)
198 figE
.figure
.suptitle(f
"ForceAtlas2 - {nfoDict['sub']}")
199 figE
.set_axis_labels(xlabel
='FA1', ylabel
='FA2')
200 figE
.savefig(f
"2C_ForceAtlas2_{nfoDict['sid']}.pdf", transparent
=True, dpi
=300, metadata
={'Title': 'ForceAtlas2', 'Subject': f
"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'})
203 def getOBSMdf(anndata
, obsmkey
='X_pca') -> pd
.DataFrame
:
204 if not obsmkey
in anndata
.obsm
:
206 sc
.tl
.pca(anndata
,zero_center
=True)
207 elif obsmkey
=='X_umap':
208 if not 'neighbors' in anndata
.uns
:
209 if not 'X_pca' in anndata
.obsm
:
210 sc
.pp
.pca(anndata
,zero_center
=True)
211 sc
.pp
.neighbors(anndata
)
213 elif obsmkey
=='X_draw_graph_fa':
214 if not 'neighbors' in anndata
.uns
:
215 if not 'X_pca' in anndata
.obsm
:
216 sc
.pp
.pca(anndata
,zero_center
=True)
217 sc
.pp
.neighbors(anndata
)
218 sc
.tl
.draw_graph(anndata
)
219 data
=anndata
.obsm
[obsmkey
][0:,0:2]
220 df
=pd
.DataFrame(data
=data
[0:,0:], index
=[anndata
.obs_names
[i
] for i
in range(data
.shape
[0])], columns
=['P'+str(1+i
) for i
in range(data
.shape
[1])])
223 if __name__
== "__main__":
224 main() # time (./fig1.py human; ./fig1.py mbrain ; ./fig1.py mkidney ) | tee plot.log
227 x1 = np.random.randn(1000)
228 y1 = np.random.randn(1000)
229 x2 = np.random.randn(1000) * 5
230 y2 = np.random.randn(1000)
231 fig, ax = plt.subplots()
232 # The figure and axes background must be made transparent.
233 fig.patch.set(alpha=0)
234 ax.patch.set(alpha=0)
235 pc1 = ax.scatter(x1, y1, c='b', edgecolors='none')
236 pc2 = ax.scatter(x2, y2, c='r', edgecolors='none')
237 mplcairo.operator_t.ADD.patch_artist(pc2) # Use additive blending.
242 3、Q<20和purity<0.6的比率大于18%
244 import patchworklib as pw
245 #from blend_modes import addition
248 * Try layers of annData.