From 3a4d3baef912aa081901f5e8b2dc979a1a29fc0c Mon Sep 17 00:00:00 2001 From: Yuuki Galaxy Date: Fri, 5 Jan 2024 11:57:27 +0800 Subject: [PATCH] modified: fig1.py modified: fig2.py --- python/salus/cmplatform/fig1.py | 8 +++++++ python/salus/cmplatform/fig2.py | 52 +++++++++++++++++++++++++++++++---------- 2 files changed, 48 insertions(+), 12 deletions(-) diff --git a/python/salus/cmplatform/fig1.py b/python/salus/cmplatform/fig1.py index d7336c0b0..a2093c669 100755 --- a/python/salus/cmplatform/fig1.py +++ b/python/salus/cmplatform/fig1.py @@ -79,6 +79,7 @@ import pandas as pd import fast_matrix_market import anndata as ad import scanpy as sc +sc._settings.ScanpyConfig.n_jobs = -1 #import squidpy as sq import seaborn as sns import scipy @@ -255,6 +256,13 @@ plt.show() 2、Q平均值小于20 3、Q<20和purity<0.6的比率大于18% +fastp --thread 4 -z -A --max_len1 28 --max_len2 0 --dont_eval_duplication -q 20 -u 30 -n 4 --average_qual 20 --length_required 28 -y -Y 30 -g -x + +fastp -w 4 -A -q 20 -u 30 -n 5 -l 28 -y -Y 30 -g -x --max_len1 28 --max_len2 1000 \ + -i ${prefix}_R1_001.fastq.gz -I ${prefix}_R2_001.fastq.gz \ + -o ./cleanfq/${basepx}_R1_001.fastq.gz -O ./cleanfq/${basepx}_R2_001.fastq.gz \ + -j ./cleanfq/${basepx}.json -h ./cleanfq/${basepx}.html 2>./cleanfq/${basepx}.log + import patchworklib as pw #from blend_modes import addition matplotlib_venn diff --git a/python/salus/cmplatform/fig2.py b/python/salus/cmplatform/fig2.py index 25b303068..30de5c106 100755 --- a/python/salus/cmplatform/fig2.py +++ b/python/salus/cmplatform/fig2.py @@ -14,11 +14,11 @@ def main(thisID) -> None: h5Path = f"{nfoDict['sid']}_{platform}.h5ad" print(f"[i]Reading {h5Path}", file=sys.stderr) adata = ad.read_h5ad(h5Path) - adata.layers["raw"] = adata.X.copy() - adata.layers["prnorm"] = adata.X.copy() - sc.experimental.pp.normalize_pearson_residuals(adata,layer='prnorm') - sc.pp.normalize_total(adata,target_sum=1e6,key_added='CPMnormFactor') - adata.layers["norm"] = adata.X.copy() + #adata.layers["raw"] = adata.X.copy() + #adata.layers["prnorm"] = adata.X.copy() + #sc.experimental.pp.normalize_pearson_residuals(adata,layer='prnorm') + #sc.pp.normalize_total(adata,target_sum=1e6,key_added='CPMnormFactor') + #adata.layers["norm"] = adata.X.copy() scDat[platform] = adata print(f"[i]Read {thisID}.{platform}: {adata.raw.shape} -> {adata.shape}", file=sys.stderr) #print(scDat) @@ -26,15 +26,25 @@ def main(thisID) -> None: adata=ad.concat(rawList, label='Platform', keys=PlatformTuple, index_unique='-') adata.var['mt'] = adata.var_names.str.startswith('MT-') | adata.var_names.str.startswith('mt-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=True, inplace=True) - adata.raw = adata - sc.pp.filter_cells(adata, min_genes=1) - sc.pp.filter_genes(adata, min_cells=1) + adata.obs['sqrt_inv_total_counts'] = 1 / np.sqrt(adata.obs['total_counts']) + adata.raw = adata.copy() + sc.pp.filter_cells(adata, min_counts=2000) # sqrt_inv_total_counts < 0.02236 按照样品均值的标准差考虑。 + sc.pp.filter_genes(adata, min_cells=1) # adata.var[adata.var['n_cells']<2].sort_values(by='sqrt_inv_total_counts') 有800个，就不过滤了。 print(f"[i]Filtered: {adata.raw.shape} -> {adata.shape}", file=sys.stderr) + + #adata.layers["raw"] = adata.X.copy() + adata.layers["prnorm"] = adata.X.copy() + sc.experimental.pp.normalize_pearson_residuals(adata,layer='prnorm') + sc.pp.normalize_total(adata,target_sum=1e6, key_added='CPMnormFactor') + adata.layers["norm"] = adata.X.copy() + sc.pp.log1p(adata) + sc.pp.pca(adata) sc.pp.neighbors(adata) sc.tl.umap(adata,random_state=369) sc.tl.draw_graph(adata,random_state=369) sc.tl.tsne(adata,random_state=369) + sc.tl.leiden(adata,random_state=0) ''' fig1, ax1 = plt.subplots() ax1.plot(x, y) @@ -43,14 +53,14 @@ def main(thisID) -> None: ''' print("[i]Begin fig E. 2Ca", file=sys.stderr) plt.figure(1) - ax=sc.pl.pca(adata, color='Platform', show=False, title=f"PCA - {nfoDict['sub']}") + ax=sc.pl.pca(adata, color='Platform', show=False, title=f"PCA - {nfoDict['sub']}", annotate_var_explained=True) plt.savefig(f"2C_PCA_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 'PCA', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) plt.figure(2) ax=sc.pl.umap(adata,color='Platform', show=False, title=f"UMAP - {nfoDict['sub']}") plt.savefig(f"2C_UMAP_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 'UMAP', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) plt.figure(3) ax=sc.pl.tsne(adata, color='Platform', show=False, title=f"t-SNE - {nfoDict['sub']}") - plt.savefig(f"2C_tsne_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 't-SNE', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) + plt.savefig(f"2C_tSNE_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 't-SNE', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) plt.figure(4) ax=sc.pl.draw_graph(adata, color='Platform', show=False, title=f"ForceAtlas2 - {nfoDict['sub']}") plt.savefig(f"2C_ForceAtlas2_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 'ForceAtlas2', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) @@ -58,12 +68,30 @@ def main(thisID) -> None: fig, ax = plt.subplots() fig.patch.set(alpha=0) ax.patch.set(alpha=0) - sc.pl.pca(adata, color='Platform', show=False, title=f"PCA - {nfoDict['sub']}", ax=ax) - arts=ax.findobj() + palette = ['#CC0000','#00CC00','#CCCC00'] + sc.pl.pca(adata, color='Platform', show=False, title=f"PCA - {nfoDict['sub']}", ax=ax, palette=palette, annotate_var_explained=True) + arts=ax.findobj(matplotlib.collections.PathCollection) for art in arts: mplcairo.operator_t.ADD.patch_artist(art) + newlabels = adata.obs['Platform'].unique().tolist() + ['Both'] + legend_elements = [plt.scatter([],[],s=0, marker='o', label=label, color=color) for label, color in zip(newlabels, palette)] + ax.legend(handles=legend_elements, loc = 'right margin') plt.savefig(f"2C_mPCA_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 'PCA', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) + fig, ax = plt.subplots() + fig.patch.set(alpha=0) + ax.patch.set(alpha=0) + palette = ['#CC0000','#00CC00','#CCCC00'] + sc.pl.tsne(adata, color='Platform', show=False, title=f"PCA - {nfoDict['sub']}", ax=ax, palette=palette) + arts=ax.findobj(matplotlib.collections.PathCollection) + for art in arts: + mplcairo.operator_t.ADD.patch_artist(art) + newlabels = adata.obs['Platform'].unique().tolist() + ['Both'] + legend_elements = [plt.scatter([],[],s=0, marker='o', label=label, color=color) for label, color in zip(newlabels, palette)] + ax.legend(handles=legend_elements, loc = 'right margin') + plt.savefig(f"2C_mtSNE_{nfoDict['sid']}.pdf", bbox_extra_artists=(ax.get_legend(),), metadata={'Title': 't-SNE', 'Subject': f"{nfoDict['sub']} Data", 'Author': 'HU Xuesong'}) + + adata = None print("[i]Begin fig E. 2Cb", file=sys.stderr) for platform in PlatformTuple: -- 2.11.4.GIT