Deleted:How to preprocess and visualize beautifully scRNA-seq with omicverse?
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Omicverse is the fundamental package for multi omics included bulk and single cell RNA-seq analysis with Python.

To get started with omicverse, check out the Installation and Tutorials.

For more details about the omicverse framework, please check out our publication.

The count table, a numeric matrix of genes\u2009\u00d7\u2009cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range.

Suitable methods to preprocess the scRNA-seq is important. Here, we introduce some preprocessing step to help researchers can perform downstream analysis easyier.

User can compare our tutorial with scanpy'tutorial to learn how to use omicverse well

import omicverse as ov
import scanpy as sc
ov.ov_plot_set()

The data consist of 3k PBMCs from a Healthy Donor and are freely available from 10x Genomics (here from this webpage). On a unix system, you can uncomment and run the following to download and unpack the data. The last line creates a directory for writing processed data.

# !mkdir data
# !wget http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz -O data/pbmc3k_filtered_gene_bc_matrices.tar.gz
# !cd data; tar -xzf pbmc3k_filtered_gene_bc_matrices.tar.gz
# !mkdir write
adata = sc.read_10x_mtx(
    'data/filtered_gene_bc_matrices/hg19/',  # the directory with the `.mtx` file
    var_names='gene_symbols',                # use gene symbols for the variable names (variables-axis index)
    cache=True)                              # write a cache file for faster subsequent reading
adata.var_names_make_unique()
adata.obs_names_make_unique()

Preprocessing

Quantity control

For single-cell data, we require quality control prior to analysis, including the removal of cells containing double cells, low-expressing cells, and low-expressing genes. In addition to this, we need to filter based on mitochondrial gene ratios, number of transcripts, number of genes expressed per cell, cellular Complexity, etc. For a detailed description of the different QCs please see the document: https://hbctraining.github.io/scRNA-seq/lessons/04_SC_quality_control.html

adata=ov.pp.qc(adata,
              tresh={'mito_perc': 0.05, 'nUMIs': 500, 'detected_genes': 250})
adata
Calculate QC metrics
End calculation of QC metrics.
Original cell number: 2700
Begin of post doublets removal and QC plot
Running Scrublet
filtered out 19024 genes that are detected in less than 3 cells
normalizing counts per cell
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
normalizing counts per cell
    finished (0:00:00)
normalizing counts per cell
    finished (0:00:00)
Embedding transcriptomes using PCA...
Automatically set threshold at doublet score = 0.31
Detected doublet rate = 1.4%
Estimated detectable doublet fraction = 35.1%
Overall doublet rate:
    Expected   = 5.0%
    Estimated  = 4.0%
    Scrublet finished (0:00:02)
Cells retained after scrublet: 2662, 38 removed.
End of post doublets removal and QC plots.
Filters application (seurat or mads)
Lower treshold, nUMIs: 500; filtered-out-cells: 0
Lower treshold, n genes: 250; filtered-out-cells: 3
Lower treshold, mito %: 0.05; filtered-out-cells: 56
Filters applicated.
Total cell filtered out with this last --mode seurat QC (and its chosen options): 59
Cells retained after scrublet and seurat filtering: 2603, 97 removed.
filtered out 19107 genes that are detected in less than 3 cells

High variable Gene Detection

Here we try to use Pearson's method to calculate highly variable genes. This is the method that is proposed to be superior to ordinary normalisation. See Article in Nature Method for details.

Sometimes we need to recover the original counts for some single-cell calculations, but storing them in the layer layer may result in missing data, so we provide two functions here, a store function and a release function, to save the original data.

We set layers=counts, the counts will be stored in adata.uns['layers_counts']

adata=ov.pp.preprocess(adata,mode='shiftlog|pearson',n_HVGs=2000,)
adata
Begin robust gene identification
After filtration, 13631/13631 genes are kept. Among 13631 genes, 13631 genes are robust.
End of robust gene identification.
Begin size normalization: shiftlog and HVGs selection pearson
normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
[]
    finished (0:00:00)
extracting highly variable genes
--> added
    'highly_variable', boolean vector (adata.var)
    'highly_variable_rank', float vector (adata.var)
    'highly_variable_nbatches', int vector (adata.var)
    'highly_variable_intersection', boolean vector (adata.var)
    'means', float vector (adata.var)
    'variances', float vector (adata.var)
    'residual_variances', float vector (adata.var)
End of size normalization: shiftlog and HVGs selection pearson

Set the .raw attribute of the AnnData object to the normalized and logarithmized raw gene expression for later use in differential testing and visualizations of gene expression. This simply freezes the state of the AnnData object.

adata.raw = adata
adata = adata[:, adata.var.highly_variable_features]
adata

We find that the adata.X matrix is normalized at this point, including the data in raw, but we want to get the unnormalized data, so we can use the retrieve function ov.utils.retrieve_layers

adata_counts=adata.copy()
ov.utils.retrieve_layers(adata_counts,layers='counts')
print('normalize adata:',adata.X.max())
print('raw count adata:',adata_counts.X.max())
......The X of adata have been stored in raw
......The layers counts of adata have been retreved
normalize adata: 11.381063
raw count adata: 419.0

If we wish to recover the original count matrix at the whole gene level, we can try the following code

adata_counts=adata.raw.to_adata().copy()
ov.utils.retrieve_layers(adata_counts,layers='counts')
print('normalize adata:',adata.X.max())
print('raw count adata:',adata_counts.X.max())
adata_counts
......The X of adata have been stored in raw
......The layers counts of adata have been retreved
normalize adata: 11.381063
raw count adata: 419.0

Principal component analysis

In contrast to scanpy, we do not directly scale the variance of the original expression matrix, but store the results of the variance scaling in the layer, due to the fact that scale may cause changes in the data distribution, and we have not found scale to be meaningful in any scenario other than a principal component analysis

ov.pp.scale(adata)
adata

If you want to perform pca in normlog layer, you can set layer=normlog, but we think scaled is necessary in PCA.

adata.obsm['X_pca']=adata.obsm['scaled|original|X_pca']
ov.utils.embedding(adata,
                  basis='X_pca',
                  color='CST3',
                  frameon='small')

PCA

Embedding the neighborhood graph

We suggest embedding the graph in two dimensions using UMAP (McInnes et al., 2018), see below. It is potentially more faithful to the global connectivity of the manifold than tSNE, i.e., it better preserves trajectories. In some ocassions, you might still observe disconnected clusters and similar connectivity violations. They can usually be remedied by running:

sc.pp.neighbors(adata, n_neighbors=15, n_pcs=50,
               use_rep='scaled|original|X_pca')

To visualize the PCA\u2019s embeddings, we use the pymde package wrapper in omicverse. This is an alternative to UMAP that is GPU-accelerated.

adata.obsm["X_mde"] = ov.utils.mde(adata.obsm["scaled|original|X_pca"])
ov.utils.embedding(adata,
                basis='X_mde',
                color='CST3',
                frameon='small')

enter image description here

You also can use umap to visualize the neighborhood graph

sc.tl.umap(adata)
ov.utils.embedding(adata,
                basis='X_umap',
                color='CST3',
                frameon='small')

enter image description here

Clustering the neighborhood graph

As with Seurat and many other frameworks, we recommend the Leiden graph-clustering method (community detection based on optimizing modularity) by Traag et al. (2018). Note that Leiden clustering directly clusters the neighborhood graph of cells, which we already computed in the previous section.

sc.tl.leiden(adata)

We redesigned the visualisation of embedding to distinguish it from scanpy's embedding by adding the parameter fraemon='small', which causes the axes to be scaled with the colourbar

ov.utils.embedding(adata,
                basis='X_mde',
                color=['leiden', 'CST3', 'NKG7'],
                frameon='small')

enter image description here

If you have too many labels, e.g. too many cell types, and you are concerned about cell overlap, then consider trying the ov.utils.gen_mpl_labels function, which improves text overlap. In addition, we make use of the patheffects function, which makes our text have outlines

  • adjust_kwargs: it could be found in package adjusttext
  • text_kwargs: it could be found in class plt.texts
from matplotlib import patheffects
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(4,4))

ov.utils.embedding(adata,
                  basis='X_mde',
                  color=['leiden'],
                   show=False, legend_loc=None, add_outline=False, 
                   frameon='small',legend_fontoutline=2,ax=ax
                 )

ov.utils.gen_mpl_labels(
    adata,
    'leiden',
    exclude=("None",),  
    basis='X_mde',
    ax=ax,
    adjust_kwargs=dict(arrowprops=dict(arrowstyle='-', color='black')),
    text_kwargs=dict(fontsize= 12 ,weight='bold',
                     path_effects=[patheffects.withStroke(linewidth=2, foreground='w')] ),
)

enter image description here

Finding marker genes

Let us compute a ranking for the highly differential genes in each cluster. For this, by default, the .raw attribute of AnnData is used in case it has been initialized before. The simplest and fastest method to do so is the t-test.

sc.tl.dendrogram(adata,'leiden',use_rep='scaled|original|X_pca')
sc.tl.rank_genes_groups(adata, 'leiden', use_rep='scaled|original|X_pca',
                        method='t-test',use_raw=False,key_added='leiden_ttest')
sc.pl.rank_genes_groups_dotplot(adata,groupby='leiden',
                                cmap='Spectral_r',key='leiden_ttest',
                                standard_scale='var',n_genes=3)

enter image description here

cosg is also considered to be a better algorithm for finding marker genes. Here, omicverse provides the calculation of cosg

Paper: Accurate and fast cell marker gene identification with COSG

Code: https://github.com/genecell/COSG

sc.tl.rank_genes_groups(adata, groupby='leiden', 
                        method='t-test',use_rep='scaled|original|X_pca',)
ov.single.cosg(adata, key_added='leiden_cosg', groupby='leiden')
sc.pl.rank_genes_groups_dotplot(adata,groupby='leiden',
                                cmap='Spectral_r',key='leiden_cosg',
                                standard_scale='var',n_genes=3)

enter image description here

scRNA-seq seurat scanpy Python • 999 views
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