Poola
Python package to analyze the results of pooled CRISPR screens
Install / Use
/learn @gpp-rnd/PoolaREADME
poola
Python package for pooled screen analysis
Install
Install from github for the latest development release:
pip install git+git://github.com/gpp-rnd/poola.git#egg=poola
Or install the most recent distribution from PyPi:
pip install poola
How to use
Additional packages required for this tutorial can be install using pip install -r requirements.txt
from poola import core as pool
import pandas as pd
import seaborn as sns
import gpplot
import matplotlib.pyplot as plt
To demonstrate the functionality of this module we'll use read counts from Sanson et al. 2018.
supp_reads = 'https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-018-07901-8/MediaObjects/41467_2018_7901_MOESM4_ESM.xlsx'
read_counts = pd.read_excel(supp_reads,
sheet_name = 'A375_orig_tracr raw reads',
header = None,
skiprows = 3,
names = ['sgRNA Sequence', 'pDNA', 'A375_RepA', 'A375_RepB'],
engine='openpyxl')
guide_annotations = pd.read_excel(supp_reads,
sheet_name='sgRNA annotations',
engine='openpyxl')
The input data has three columns with read counts and one column with sgRNA annotations
read_counts.head()
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lognorms = pool.lognorm_columns(reads_df=read_counts, columns=['pDNA', 'A375_RepA', 'A375_RepB'])
filtered_lognorms = pool.filter_pdna(lognorm_df=lognorms, pdna_cols=['pDNA'], z_low=-3)
print('Filtered ' + str(lognorms.shape[0] - filtered_lognorms.shape[0]) + ' columns due to low pDNA abundance')
Filtered 576 columns due to low pDNA abundance
Note that the column names for the lognorms remain the same
lognorms.head()
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lfc_df = pool.calculate_lfcs(lognorm_df=filtered_lognorms, ref_col='pDNA', target_cols=['A375_RepA', 'A375_RepB'])
We drop the pDNA column after calculating log-fold changes
lfc_df.head()
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Since we only have two conditions it's easy to visualize replicates as a point densityplot using gpplot
plt.subplots(figsize=(4,4))
gpplot.point_densityplot(data=lfc_df, x='A375_RepA', y='A375_RepB')
gpplot.add_correlation(data=lfc_df, x='A375_RepA', y='A375_RepB')
sns.despine()
Since we see a strong correlation, we'll average the log-fold change of each sgRNA across replicates
avg_replicate_lfc_df = pool.average_replicate_lfcs(lfcs=lfc_df, guide_col='sgRNA Sequence', condition_indices=[0],
sep='_')
After averaging log-fold changes our dataframe is melted, so the condition column specifies the experimental condition (A375 here) and the n_obs specifies the number of replicates
avg_replicate_lfc_df.head()
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It's sometimes helpful to group controls into pseudo-genes so they're easier to compare with target genes. Our annotation file maps from sgRNA sequences to gene symbols
remapped_annotations = pool.group_pseudogenes(annotations=guide_annotations, pseudogene_size=4,
gene_col='Annotated Gene Symbol',
control_regex=['NO_CURRENT'])
remapped_annotations.head()
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We provide two methods for scaling log-fold change values to controls:
- Z-score from a set of negative controls
- Scale scores between a set of negative and positive controls
For both scoring methods, you can input either a regex or a list of genes to define control sets
For our set of negative controls, we'll use nonessential genes
nonessential_genes = (pd.read_table('https://raw.githubusercontent.com/gpp-rnd/genesets/master/human/non-essential-genes-Hart2014.txt',
names=['gene'])
.gene)
annot_guide_lfcs = pool.annotate_guide_lfcs(avg_replicate_lfc_df, remapped_annotations, 'Annotated Gene Symbol',
merge_on='sgRNA Sequence', z_score_neg_ctls=True,
