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Run quality control checks

EZQC.Rd

EZQC() assesses multiple aspects of your NR-seq data and generates a number of plots visualizing dataset-wide trends.

Usage

EZQC(obj, ...)

Arguments

obj

EZbakRData or EZbakRFractions object.

...

Parameters passed to the class-specific method. If you have provided an EZbakRFractions object, then these can be (all play the same role as in EstimateKinetics(), that is they get passed to EZget() to find the fractions table you are interested in. See ?EstimateKinetics() for details.):

  • features

  • populations

  • fraction_design

If you have provided an EZbakRData object, then these can be (all same the same purpose as in EstimateFractions, so see ?EstimateFractions() for details):

  • mutrate_populations

  • features

  • filter_cols

  • filter_condition

  • remove_features

Value

A list of ggplot2 objects visualizing the various aspects of your data assessed by EZQC().

Details

EZQC() checks the following aspects of your NR-seq data. If you have passed an EZbakRData object, then EZQC() checks:

  • Raw mutation rates: In all sequencing reads, how many T's in the reference were a C in the read? The hope is that raw mutation rates are higher than -label controls in all +label samples. Higher raw mutation rates, especially when using standard label times (e.g., 2 hours or more in mammalian systems), are typically a sign of good label incorporation and low labeled RNA/read dropout. If you don't have -label samples, know that background mutation rates are typically less than 0.2%, so +label raw mutation rates several times higher than this would be preferable.

  • Mutation rates in labeled and unlabeled reads: The raw mutation rate counts all mutations in all reads. In a standard NR-seq experiment performed with a single metabolic label, there are typically two populations of reads:

    1. Those from labeled RNA, having higher mutation rates due to chemical conversion/recoding of the metabolic label and 2) those from unlabeled RNA, having lower, background levels of mutations. EZbakR fits a two component mixture model to estimate the mutation rates in these two populations separately. A successful NR-seq experiment should have a labeled read mutation rate of > 1% and a low background mutation rate of < 0.3%.

    2. Read count replicate correlation: Simply the log10 read count correlation for replicates, as inferred from your metadf.

If you have passed an EZbakRFractions object, i.e., the output of EstimateFractions(), then in addition to the checks in the EZbakRData input case, EZQC() also checks:

  • Fraction labeled distribution: This is the distribution of feature-wise fraction labeled's (or fraction high mutation content's) estimated by EstimateFractions(). The "ideal" is a distribution with mean around 0.5, as this maximizes the amount of RNA with synthesis and degradation kinetics within the dynamic range of the experiment. In practice, you will (and should) be at least a bit lower than this as longer label times risk physiological impacts of metabolic labeling.

  • Fraction labeled replicate correlation: This is the logit(fraction labeled) correlation between replicates, as inferred from your metadf.

Examples


# Simulate data to analyze
simdata <- EZSimulate(30)

# Create EZbakR input
ezbdo <- EZbakRData(simdata$cB, simdata$metadf)

# Estimate Fractions
ezbdo <- EstimateFractions(ezbdo)
#> Estimating mutation rates
#> Summarizing data for feature(s) of interest
#> Averaging out the nucleotide counts for improved efficiency
#> Estimating fractions
#> Processing output

# Run QC
QC <- EZQC(ezbdo)
#> CHECKING RAW MUTATION RATES...
#> CHECKING INFERRED MUTATION RATES...
#> CHECKING READ COUNT CORRELATIONS...
#> log10(read counts) correlation for each pair of replicates are:
#> # A tibble: 12 × 3
#>    sample_1 sample_2 correlation
#>    <chr>    <chr>          <dbl>
#>  1 sample1  sample2        0.990
#>  2 sample1  sample3        0.994
#>  3 sample1  sample7        0.987
#>  4 sample2  sample3        0.984
#>  5 sample2  sample7        0.981
#>  6 sample3  sample7        0.987
#>  7 sample4  sample5        0.986
#>  8 sample4  sample6        0.986
#>  9 sample4  sample8        0.991
#> 10 sample5  sample6        0.988
#> 11 sample5  sample8        0.985
#> 12 sample6  sample8        0.991
#> 
#> log10(read counts) correlations are high, suggesting good reproducibility!
#> 
#> CHECKING FRACTION LABELED DISTRIBUTIONS...
#> Average fractions for each sample are:
#> # A tibble: 6 × 3
#>   sample  avg_fraction fraction_type  
#>   <chr>          <dbl> <chr>          
#> 1 sample1        0.276 fraction_highTC
#> 2 sample2        0.288 fraction_highTC
#> 3 sample3        0.274 fraction_highTC
#> 4 sample4        0.279 fraction_highTC
#> 5 sample5        0.270 fraction_highTC
#> 6 sample6        0.282 fraction_highTC
#> 
#> Labeling rates (e.g., fraction labeled for single label experiments) look good!
#> 
#> CHECKING FRACTION LABELED CORRELATIONS...
#> logit(fraction_highTC) correlation for each pair of replicates are:
#> # A tibble: 6 × 3
#>   sample_1 sample_2 correlation
#>   <chr>    <chr>          <dbl>
#> 1 sample1  sample2        0.930
#> 2 sample1  sample3        0.967
#> 3 sample2  sample3        0.931
#> 4 sample4  sample5        0.924
#> 5 sample4  sample6        0.913
#> 6 sample5  sample6        0.930
#> 
#> logit(fraction_highTC) correlations are high, suggesting good reproducibility!
#>