MPRAsnakeflow assignment QC report

Overview

The Assignment QC (Quality Control) Report is a comprehensive document designed to evaluate and ensure the quality of data or processes involved in a specific assignment. This report is generated for the assocBasic assignment with settings defined in the config default. The barcode length for this experiment is 15 bases.

Forward: data/SRR10800986_1.fastq.gz
Reverse: data/SRR10800986_3.fastq.gz
Barcode: data/SRR10800986_2.fastq.gz
Design File: design.fa

Overall quality metrics

Table explanation

median assigned barcodes: Median number of barcodes assigned to tested sequences in mapping as a quality control measure for the assignment step, whether there is sufficient barcode to oligo coverage.
fraction assigned oligos: Fraction of assigned tested sequences in mapping to determine if the library during the assignment step was sufficiently recovered.

median assigned barcodes	fraction assigned oligos
437	1.00

BC distribution over oligos

Counts of barcodes and oligos

Table explanation

BCs are the different observed barcodes in the sequencing data (not the overall barcode count/number of BC reads).
Differences in row BCs between Raw data and Filtered data are due to barcodes that do not match the length of 15 bases defined in the default config of the config file.
Other BCs are those barcodes where no oligo could be assigned due to mapping (e.g., due to MAPQ filter or due to multiple matches).
Ambiguous BCs are those that map to a designed oligo but failed the minimum required barcodes per oligo of 3 and/or the minimum required fraction of 0.75 assigned to one unique oligo.

	Design	Raw data	Filtered data
Oligos	2440	2440	2439
BCs	0	4427032	4427032
Assigned BCs	0	0	1144171
Other BCs	0	0	4485
Ambiguous BCs	0	0	3278376

--- title: "MPRAsnakeflow assignment QC report" page-layout: full format: html: code-fold: true code-tools: true embed-resources: true anchor-sections: true smooth-scroll: true toc: true jupyter: python3 --- ```{python} #| tags: [parameters] #| echo: false ``` ```{python} #| echo: false # Imports from IPython.display import display, Image, Markdown import pandas as pd import json ``` # Overview The Assignment QC (Quality Control) Report is a comprehensive document designed to evaluate and ensure the quality of data or processes involved in a specific assignment. This report is generated for the `{python} assignment` assignment with settings defined in the config `{python} configs`. The barcode length for this experiment is `{python} bc_length` bases. - **Forward:** `{python} fw` - **Reverse:** `{python} rev` - **Barcode:** `{python} bc` - **Design File:** `{python} design_file` ```{python} #| echo: false with open(f"{workdir}/{design_file_checked}") as f: seq_num_in_design = 0 lines = f.readlines() for line in lines: if line.startswith(">"): seq_num_in_design += 1 ``` # Overall quality metrics <details> <summary>Table explanation</summary> - **median assigned barcodes:** Median number of barcodes assigned to tested sequences in mapping as a quality control measure for the assignment step, whether there is sufficient barcode to oligo coverage. - **fraction assigned oligos:** Fraction of assigned tested sequences in mapping to determine if the library during the assignment step was sufficiently recovered. </details> ```{python} #| echo: false with open(f"{workdir}/{qc_metrics_file}", 'r') as file: qc_data = json.load(file) df_qc = pd.DataFrame([qc_data]) df_qc.columns = df_qc.columns.str.replace("_", " ", regex=False) #df_qc.columns = ['Value'] df_qc.style.hide(axis="index").format(precision=2) ``` # BC distribution over oligos ```{python} #| echo: false display(Image(f"{workdir}/{plot_file}")) ``` # Counts of barcodes and oligos <details> <summary>Table explanation</summary> - **BCs** are the different observed barcodes in the sequencing data (not the overall barcode count/number of BC reads). - Differences in row `BCs` between `Raw data` and `Filtered data` are due to barcodes that do not match the length of `{python} bc_length` bases defined in the `{python} configs` config of the config file. - **Other BCs** are those barcodes where no oligo could be assigned due to mapping (e.g., due to MAPQ filter or due to multiple matches). - **Ambiguous BCs** are those that map to a designed oligo but failed the minimum required barcodes per oligo of `{python} min_support` and/or the minimum required fraction of `{python} fraction` assigned to one unique oligo. </details> ```{python} #| echo: false import pandas as pd from IPython.display import HTML, display df_design = pd.DataFrame({' ': ['Oligos'], 'Design': int(seq_num_in_design)}) df_total = pd.read_csv(f"{workdir}/{statistic_all_file}", sep='\t').rename(columns={'Unnamed: 0': ' ', 'Counts': 'Raw data'}) df_filter = pd.read_csv(f"{workdir}/{statistic_filter_file}", sep='\t').rename(columns={'Unnamed: 0': ' ', 'Counts': 'Filtered data'}) # Concatenate the new row DataFrame with the original DataFrame df = df_design.merge(df_total, on=' ', how="right") df = df.merge(df_filter, on=' ', how="right").reset_index(drop=True).fillna(0).astype({"Design": int, "Raw data": int, "Filtered data": int}) df.style.hide(axis="index").format( precision=2) ```