Getting started¶
Generating ChronQC plots¶
ChronQC plots can be generated from,
1. A custom SQLite database.
This example demonstrates generating ChronQC plots from a custom database:
cd examples/custom_db_example
Run following command to generate interactive plots in html:
chronqc plot -db chronqc_custom_db.sqlite -json config.json -panel Somatic
The types of created plots and their properties are specified in “config.json” file. For details on creating the config file visit documentation.
Interactive html report is created under chronqc_output directory
2. The output of MultiQC.
For creating ChronQC database and plots, see the example below
This example demonstrates generating a ChronQC database and creating ChronQC graphs using the database:
cd examples/multiqc_example_1
Step 1: Create a ChronQC database:
chronqc database --create -multiqc_stats multiqc_data/multiqc_general_stats.txt -run_date_info run_date_info.csv -panel SOMATIC -o
A sqlite database chronqc.stats.sqlite and chronqc.stats.cols.txt file are created in chronqc_db folder under the . (current) directory.
Step 2: Create ChronQC plots:
chronqc plot -db chronqc_db/chronqc.stats.sqlite -json sample.json -panel SOMATIC -o .
The types of created plots and their properties are specified in “sample.json” file. For details on creating the config file visit documentation.
Interactive html report is created in chronqc_output under the . (current) directory.
For creating, updating ChronQC database and plots, see the example below
This example demonstrates generating a ChronQC database, updating the generated database with new data and creating ChronQC graphs using the database:
cd examples/multiqc_example_2
Step 1: Create a ChronQC database:
chronqc database --create -multiqc_stats year_2016/multiqc_data/multiqc_general_stats.txt -run_date_info year_2016/run_date_info.csv -panel Germline -o .
A sqlite database chronqc.stats.sqlite and chronqc.stats.cols.txt file are created in chronqc_db folder under the . (current) directory.
Step 2: Update existing ChronQC database:
chronqc database --update -db chronqc_db/chronqc.stats.sqlite -multiqc_stats year_2017/multiqc_data/multiqc_general_stats.txt -run_date_info year_2017/run_date_info.csv -panel Germline
Step 3: Create ChronQC plots:
chronqc plot -db chronqc_db/chronqc.stats.sqlite -json sample.json -panel Germline -o .
The types of created plots and their properties are specified in “sample.json” file. For details on creating the config file visit documentation.
Interactive html report is created in chronqc_output under the . (current) directory.
Using ChronQC plots¶
ChronQC is designed to be interactive. ChronQC plots can be adjusted to a time period and are zoomable. Mousing over a point displays its associated data such as run ID, sample IDs, and corresponding values. To use the annotation feature of ChronQC plots, start the annotation database connectivity by using chronqc annotation command. Then open the ChronQC output html in a recent browser (tested on: Google Chrome and Mozilla Firefox). Users can record notes and corrective actions on the plots by clicking on a point or selecting a date. User notes and corrective actions are stored for long-term recordkeeping in the SQLite ChronQC annotations database. The plots are interlinked so that when an individual point or date is annotated in one graph, the same annotation appears on other graphs. By linking plots with the ChronQC annotations database, users can see the notes and corrective actions recorded previously.
ChronQC config files¶
chronqc.stats.cols.txtfile generated during the ChronQC stats database creation can be used to get column names present in the database.- Using the statistics database and a configuration file (JSON), ChronQC generates time series plots for various metrics to create an interactive, self-contained HTML file.
- Plots should be mentioned simultaneously in JSON, if are generated from same SQLite table. This ensures proper grouping in sidebar of HTML report.
- Special characters in the title or y-axis label must be specified as Unicode.
Below is an example of ChronQC config file:
[
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "% of Duplicates in On-target Sites (per run)",
"y_value": "Duplicates",
"y_label": "% of Duplicates"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "Average Mapping Quality of On-target Sites (per run)",
"y_value": "MappingQuality",
"y_label": "MappingQuality"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Average Base Quality Scores in On-target Sites (per run)",
"y_value": "BaseQuality",
"lower_threshold": 30,
"y_label": "Average Base Quality Score"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "Number of Bases in Reads within On-target Sites (per run)",
"y_value": "BasesOfReads",
"y_label": "Bases Of Reads"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "% of Bases in Reads within On-target Sites (per run)",
"y_value": "%BasesofReads",
"y_label": "% of Bases of Reads"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Depth Median (per run)",
"y_value": "Depth",
"lower_threshold": 200,
"y_label": "Depth Median (per run)"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "HCT",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Depth Median (HCT)",
"y_value": "Depth",
"lower_threshold": 200,
"y_label": "Depth Median"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "GC Content % (per run)",
"y_value": "GCContent",
"y_label": "GC Content % (per run)"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_percentage_category",
"chart_properties": {
"chart_title": "% of Samples that passed VCS QC (per run)",
"y_value": "vcs_coverage_qc",
"y_label": "% Samples in library",
"category": "PASS"
}
},
{
"table_name": "Production_Run_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_percentage_of_samples_above_threshold",
"chart_properties": {
"chart_title": "% of Samples in a run with >= 200 depth (per run)",
"y_value": "Depth",
"threshold": 200,
"y_label": "% Samples in library"
}
},
{
"table_name": "SNPs_Indels_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_box_whisker_plot",
"chart_properties": {
"chart_title": "Number of SNPs found in Samples Over Time",
"y_value": "Number",
"Type": "SNPs",
"y_label": "Number of SNPs found in each sample"
}
},
{
"table_name": "SNPs_Indels_Stats_Summary",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_box_whisker_plot",
"chart_properties": {
"chart_title": "Number of indels found in Samples Over Time",
"y_value": "Number",
"Type": "Indels",
"y_label": "Number of indels found in each sample"
}
},
{
"table_name": "Ti_Tv_Ratio_Stats",
"include_samples": "all",
"exclude_samples": "HCT, NTC",
"chart_type": "time_series_with_mean_and_stdev",
"chart_properties": {
"chart_title": "Transition to Transversion Ratio of Samples Over Time (per run)",
"y_value": "Number",
"y_label": "Ti/Tv Ratio"
}
},
{
"table_name": "Ti_Tv_Ratio_Stats",
"include_samples": "HCT",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Transition to Transversion Ratio of Positive Control (HCT) Over Time (per run)",
"y_value": "Number",
"y_label": "Positive Control (HCT) Ti/Tv Ratio",
"lower_threshold": 1.4,
"upper_threshold": 1.78
}
},
{
"table_name": "SNPs_Indels_Stats_Summary",
"include_samples": "HCT",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Numbers of SNPs in Positive Control (HCT) Over Time",
"y_value": "Number",
"lower_threshold": 6580,
"upper_threshold": 9728,
"Type": "SNPs",
"y_label": "Numbers of SNPs in Positive Control (HCT) Over Time"
}
},
{
"table_name": "SNPs_Indels_Stats_Summary",
"include_samples": "HCT",
"chart_type": "time_series_with_absolute_threshold",
"chart_properties": {
"chart_title": "Numbers of Indels in Positive Control (HCT) Over Time",
"y_value": "Number",
"lower_threshold": 1521,
"upper_threshold": 1960,
"Type": "Indels",
"y_label": "Numbers of Indels in Positive Control (HCT) Over Time"
}
},
{
"table_name": "VCS_Stats_Summary",
"include_samples": "all",
"chart_type": "time_series_with_bar_line_plot",
"chart_properties": {
"y_value": "Gene",
"categories": "KRAS, KIT, BRAF, PDGFRA, NRAS"
}
},
{
"table_name": "VCS_Stats_Summary",
"include_samples": "all",
"chart_type": "time_series_with_stacked_bar_plot",
"chart_properties": {
"y_value": "Gene",
"categories": "KRAS, KIT, BRAF, PDGFRA, NRAS"
}
}
]