LCD_complex_cutoff {YAPSA} | R Documentation

## LCD with a signature-specific cutoff on exposures

### Description

`LCD_cutoff` performs a mutational signatures decomposition by Linear Combination Decomposition (LCD) of a given mutational catalogue `V` with known signatures `W` by solving the minimization problem min(||W*H - V||) with additional constraints of non-negativity on H where W and V are known, but excludes signatures with an overall contribution less than a given signature-specific cutoff (and thereby accounting for a background model) over the whole cohort.

`LCD_complex_cutoff_perPID` is a wrapper for `LCD_complex_cutoff` and runs individually for every PID.

`LCD_extractCohort_callPerPID` runs `LCD_complex_cutoff` and takes the identified signatures as input for `LCD_complex_cutoff_perPID`.

`LCD_complex_cutoff_consensus` calls `LCD_complex_cutoff_combined` AND `LCD_complex_cutoff_perPID` and makes a consensus signature call set.

`LCD_complex_cutoff_combined` is a wrapper for `LCD_complex_cutoff`, `LCD_complex_cutoff_perPID`, `LCD_complex_cutoff_consensus` AND `LCD_extractCohort_callPerPID`.

### Usage

    LCD_complex_cutoff(
      in_mutation_catalogue_df,
      in_signatures_df,
      in_cutoff_vector = NULL,
      in_filename = NULL,
      in_method = "abs",
      in_per_sample_cutoff = 0,
      in_rescale = TRUE,
      in_sig_ind_df = NULL,
      in_cat_list = NULL
    )

    LCD_complex_cutoff_perPID(
      in_mutation_catalogue_df,
      in_signatures_df,
      in_cutoff_vector = NULL,
      in_filename = NULL,
      in_method = "abs",
      in_rescale = TRUE,
      in_sig_ind_df = NULL,
      in_cat_list = NULL,
      minimumNumberOfAlterations = 25
    )

    LCD_extractCohort_callPerPID(
      in_mutation_catalogue_df,
      in_signatures_df,
      in_cutoff_vector = NULL,
      in_filename = NULL,
      in_method = "abs",
      in_rescale = TRUE,
      in_sig_ind_df = NULL,
      in_cat_list = NULL,
      in_verbose = FALSE,
      minimumNumberOfAlterations = 25,
      cutoff_type = "adaptive"
    )

    LCD_complex_cutoff_consensus(
      in_mutation_catalogue_df = NULL,
      in_signatures_df = NULL,
      in_cutoff_vector = NULL,
      in_filename = NULL,
      in_method = "abs",
      in_rescale = TRUE,
      in_sig_ind_df = NULL,
      in_cat_list = NULL,
      in_cohort_LCDlist = NULL,
      in_perPID_LCDlist = NULL,
      addSigs_cohort_cutoff = 0.25,
      addSigs_perPID_cutoff = 0.25,
      addSigs_relAbs_cutoff = 0.01,
      keep.unassigned = FALSE,
      keep.all.cohort.sigs = TRUE,
      in_verbose = FALSE,
      minimumNumberOfAlterations = 25
    )

    LCD_complex_cutoff_combined(
      in_mutation_catalogue_df = NULL,
      in_signatures_df = NULL,
      in_cutoff_vector = NULL,
      in_filename = NULL,
      in_method = "abs",
      in_rescale = TRUE,
      in_sig_ind_df = NULL,
      in_cat_list = NULL,
      addSigs_cohort_cutoff = 0.25,
      addSigs_perPID_cutoff = 0.25,
      addSigs_relAbs_cutoff = 0.01,
      keep.all.cohort.sigs = TRUE,
      in_verbose = FALSE,
      minimumNumberOfAlterations = 25,
      cutoff_type = "adaptive"
    )

### Arguments

`in_mutation_catalogue_df` | A numeric data frame `V` with `n` rows and `m` columns, `n` being the number of features and `m` being the number of samples
`in_signatures_df` | A numeric data frame `W` with `n` rows and `l` columns, `n` being the number of features and `l` being the number of signatures
`in_cutoff_vector` | A numeric vector of values less than 1. Signatures from within `W` with an overall exposure less than the respective value in `in_cutoff_vector` will be discarded.
`in_filename` | A path to generate a histogram of the signature exposures if non-NULL
`in_method` | Indicate to which data the cutoff shall be applied: absolute exposures, relative exposures
`in_per_sample_cutoff` | A numeric value less than 1. Signatures from within `W` with an exposure per sample less than `in_cutoff` will be discarded.
`in_rescale` | Boolean, if TRUE (default) the exposures are rescaled such that colSums over exposures match colSums over mutational catalogue
`in_sig_ind_df` | Data frame of type signature_indices_df, i.e. indicating name, function and meta-information of the signatures. Default is NULL.
`in_cat_list` | List of categories for aggregation. Have to be among the column names of `in_sig_ind_df`. Default is NULL.
`minimumNumberOfAlterations` | The perPID part of the analysis issues a warning if one sample has less mutations than this minimum cutoff.
`in_verbose` | Verbose if `in_verbose=1`
`cutoff_type` | If chosen to be "adaptive", the default, then signature-specific cutoffs are used for the the per-PID analysis in `LCD_extractCohort_callPerPID`, otherwise, no cutoffs are used.
`in_cohort_LCDlist` | Optional, if not provided, the cohort-wide exposures are recalculated by calling `LCD_complex_cutoff`
`in_perPID_LCDlist` | Optional, if not provided, the per sample exposures are recalculated by calling `LCD_complex_cutoff_perPID`
`addSigs_cohort_cutoff` | Numeric value for a cutoff: signatures which are detected in a fraction of the samples of the cohort greater than this cutoff are kept for the consensus set of signatures
`addSigs_perPID_cutoff` | Numeric value for a cutoff: signatures which are detected in one sample with exposure greater than this cutoff are kept for the consensus set of signatures
`addSigs_relAbs_cutoff` | Numeric value for a cutoff: signatures which are detected with at least this fraction of all variants cohort wide are kept for the consensus set of signatures
`keep.unassigned` | Boolean, if TRUE the exposures from the signatures which don't fulfill the criteria to be kept will be added and stored in the exposures as "unassigned", otherwise the exposures are rescaled.
`keep.all.cohort.sigs` | If TRUE (default), all signatures extracted cohort wide are kept, if FALSE, the function reevaluates whether the signatures extracted cohort wide still fulfill their criteria (i.e. exposures > cutoff) after perPID extraction.

### Value

A list with entries:

- `exposures`: The exposures `H`, a numeric data frame with `l` rows and `m` columns, `l` being the number of signatures and `m` being the number of samples

- `norm_exposures`: The normalized exposures `H`, a numeric data frame with `l` rows and `m` columns, `l` being the number of signatures and `m` being the number of samples

- `signatures`: The reduced signatures that have exposures bigger than `in_cutoff`

- `choice`: Index vector of the reduced signatures in the input signatures

- `order`: Order vector of the signatures by exposure

- `residual_catalogue`: Numerical data frame (matrix) of the difference between fit (product of signatures and exposures) and input mutational catalogue

- `rss`: Residual sum of squares (i.e. sum of squares of the residual catalogue)

- `cosDist_fit_orig_per_matrix`: Cosine distance between the fit (product of signatures and exposures) and input mutational catalogue computed after putting the matrix into vector format (i.e. one scaler product for the whole matrix)

- `cosDist_fit_orig_per_col`: Cosine distance between the fit (product of signatures and exposures) and input mutational catalogue computed per column (i.e. per sample, i.e. as many scaler products as there are samples in the cohort)

- `sum_ind`: Decreasing order of mutational loads based on the input mutational catalogue

- `out_sig_ind_df`: Data frame of the type `signature_indices_df`, i.e. indicating name, function and meta-information of the signatures. Default is NULL, non-NULL only if `in_sig_ind_df` is non-NULL.

- `aggregate_exposures_list`: List of exposure data frames aggregated over different categories. Default is NULL, non-NULL only if `in_sig_ind_df` and `in_cat_list` are non-NULL and if the categories specified in `in_cat_list` are among the column names of `in_sig_ind_df`.

### See Also

`LCD`

`aggregate_exposures_by_category`

`lsei`

### Examples

     NULL

[Package YAPSA version 1.18.0 Index]