`SiFT` pipeline#

This code presents the pipeline for running SiFT for filtering biological signals from single cell data.

We apply SiFT to remove cell cycle and sex signal, which add nuisance noise, in Drosophila wing disc development single cell data [1].

We show that that conditioning on the gene expression from cell sex and cell cycle marker genes SiFT is able to deconfound cell sex and cell cycle effects while preserving signal from the other genes.

** NOTE: this analysis is inspired by the analysis provided in scvi-tools-reproducibility, scvi_covariates.ipynb

[1] Everetts, N. J., Worley, M. I., Yasutomi, R., Yosef, N., & Hariharan, I. K. (2021). Single-cell transcriptomics of the Drosophila wing disc reveals instructive epithelium-to-myoblast interactions. Elife, 10, e61276.

[1]:

import numpy as np
import scanpy as sc

import sift

Import data#

download pre-processed data drosopila.annotated.h5ad

[2]:

DATA_DIR = ".../drosophila.annotated.h5ad"  # set path to anndata

[3]:

adata = sc.read(DATA_DIR)

[4]:

adata_ref = adata.copy()

We can visualize the data and observe the nuisance effect with respect to the cell cycle, sex, cc \& sex labels. We also consider nuisance marker genea along-with a gene of interest:

cell cycle marker: PCNA
sex marker: lncRNA:roX1
biological marker: Nrt

[ ]:

sc.tl.pca(adata_ref)
sc.pp.neighbors(adata_ref)
sc.tl.umap(adata_ref)

[18]:

sc.pl.umap(
    adata_ref,
    color=["phase", "sex", "phase_sex", "PCNA", "lncRNA:roX1", "Nrt"],
    title=[
        "cell cycle phase",
        "sex",
        "cc phase & sex",
        "PCNA\n(cc marker)",
        "lncRNA:roX1\n(sex marker)",
        "Nrt\n(biological marker)",
    ],
    frameon=False,
    wspace=0.15,
    ncols=3,
)

Apply `SiFT`#

We set up adata for SiFT filtering based on nuisance genes, copying the expression of the nuisance genes (sex and cell_cycle genes) into adata.obsm and removing them from the expression data.

[7]:

nuisance_genes = np.concatenate((adata.uns["sex_genes"], adata.uns["cell_cycle_genes"]))
adata.obsm["nuisance_genes"] = adata[:, nuisance_genes].X
adata.uns["nuisance_genes"] = nuisance_genes

gene_subset = [g for g in adata.var_names if g not in nuisance_genes]
adata = adata[:, gene_subset].copy()

Set the parameters#

metric: the metric used for the SiFT kernel. Here we consider the k-NN kernel.
n_neighbors: for a k-NN kernel we can specify the number of neighbors used.
kernel_key: the key in the anndata used for defining the kernel.

[8]:

metric_ = "knn"
n_neighbors_ = 5
kernel_key_ = "nuisance_genes"

Define a `SiFT` object#

Using copy we can specify if the adata object is modified in-place or copied.

[9]:

sft = sift.SiFT(
    adata=adata,
    kernel_key=kernel_key_,
    n_neighbors=n_neighbors_,
    metric=metric_,
    copy=True,
)

INFO     sift: initialized a SiFTer with knn kernel.

We can visualize the kernel using plot_kernel().

groupby: the key in the anndata we can group the cells by.
color_palette: colors for the groupby groups.

[15]:

sft.plot_kernel(
    groupby="phase_sex", color_palette=adata.uns["phase_sex_colors"], ncol=3
)

Perform filtering#

To filter the object we use the filter() function.

embedding_key: the key in the anndata for the data object we want to use.
pseudocount: add a pseudocount to the filtered object to ensure non-negativity of X.

[11]:

adata = sft.filter(embedding_key="X", pseudocount=False)

INFO     sift: Filtering cell-cell similarity kernel using projection on `X`.
INFO     sift: The data is `SiFTed`!
         The filtered embedding is stored in `adata.X`
             Finish

Visualize the results#

We observe the removal of the nuisance effect with respect to the cell cycle, sex, cc \& sex labels along-with the marker genes PCNA (cell cycle marker), and lncRNA:roX1 (sex marker).

Further, for the biological marker gene, Nrt, we see a pattern is preserved.

[12]:

sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)

[17]:

sc.pl.umap(
    adata,
    color=["phase", "sex", "phase_sex", "PCNA", "lncRNA:roX1", "Nrt"],
    title=[
        "cell cycle phase",
        "sex",
        "cc phase & sex",
        "PCNA\n(cc marker)",
        "lncRNA:roX1\n(sex marker)",
        "Nrt\n(biological marker)",
    ],
    frameon=False,
    wspace=0.15,
    ncols=3,
)

[ ]:

SiFT pipeline#