## Version 0.1.11: January 27, 2019¶

General:

- Fixed a bug in the prototype declarations for the
`TargetingModel`

and`RegionDefinition`

S4 classes.

## Version 0.1.10: September 19, 2018¶

General:

- Added
`subsample`

argument to`distToNearest`

function. - Removed some internal utility functions in favor of importing them from
`alakazam`

. Specifically,`progressBar`

,`getBaseTheme`

and`checkColumns`

. - Removed
`clearConsole`

,`getnproc`

, and`getPlatform`

functions.

Distance Calculation:

- Changed default
`findThreshold`

method to`density`

. - Significantly reduced run time of the
`density`

method by retuning the bandwidth detection process. The`density`

method should now also yield more consistent thresholds, on average. - The
`subsample`

argument to`findThreshold`

now applies to both the`density`

and`gmm`

methods. Subsampling of distance is not performed by default. - Fixed a bug in
`plotDensityThreshold`

and`plotGmmThreshold`

wherein the`breaks`

argument was ignored when specifying`xmax`

and/or`xmin`

.

Selection Analsis:

- Fixed a plotting bug in
`plotBaselineDensity`

arising when the`groupColumn`

and`idColumn`

arguments were set to the same column. - Added the
`sizeElement`

argument to`plotBaselineDensity`

to control line size - Renamed the
`field_name`

argument to`field`

in`editBaseline`

.

## Version 0.1.9: March 30, 2018¶

Selection Analysis:

- Fixed a bug in
`plotBaselineDensity`

which caused an empty plot to be generated if there was only a single value in the`idColumn`

. - Fixed a bug in
`calcBaseline`

which caused a crash in`summarizeBaseline`

and`groupBaseline`

when input`baseline`

is based on only 1 sequence (i.e. when`nrow(baseline@db)`

is 1). - Set default
`plot`

call on a`Baseline`

object to`plotBaselineDensity`

. - Removed
`getBaselineStats`

function. - Added a
`summary`

method for`Baseline`

objects that calls`summarizeBaseline`

and returns a data.frame.

Mutation Profiling:

- Fixed a bug in
`shmulateSeq`

which caused a crash when the input sequence contains gaps (`.`

). - Renamed the argument
`mutations`

in`shmulateSeq`

to`numMutations`

. - Improved help documentation for
`shmulateSeq`

and`shmulateTree`

. - Added vignette for simulating mutated sequences.
`calcExpectedMutations`

will now treat non-ACTG characters as Ns rather than produce an error.- Added two new
`RegionDefinition`

objects for the full V segment as single region (`IMGT_V_BY_SEGMENTS`

) and the V segment with each codon as a separate region (`IMGT_V_BY_CODONS`

).

Targeting Models:

- Added the
`calculateMutability`

function which computes the aggregate mutability for sequences. - Fixed a bug that caused
`createSubstitutionMatrix`

to fail for data containing only a single V family. - Changed the default model to silent mutations only (
`model="S"`

) in`createSubstitutionMatrix`

,`createSubstitutionMatrix`

and`createTargetingModel`

- Set default
`plot`

call on a`TargetingModel`

object to`plotMutability`

.

## Version 0.1.8: June 30, 2017¶

General:

- Corrected several functions so that they accept both tibbles and data.frames.

Distance Calculation:

- Adding new fitting procedures to the
`"gmm"`

method of`findThreshold()`

that allows users to choose a mixture of two univariate density distribution functions among four available combinations:`"norm-norm"`

,`"norm-gamma"`

,

`"gamma-norm"`

, or`"gamma-gamma"`

. - Added the ability to choose the threshold selection criteria in the
`"gmm"`

method of`findThreshold()`

from the best average sensitivity and specificity, the curve intersection or user defined sensitivity or specificity. - Renamed the
`cutEdge`

argument of`findThreshold()`

to`edge`

.

Mutation Profiling:

- Redesigned
`collapseClones()`

, adding various deterministic and stochastic methods to obtain effective clonal sequences, support for including ambiguous IUPAC characters in output, as well as extensive documentation. Removed`calcClonalConsensus()`

from exported functions. - Added support for including ambiguous IUPAC characters in input for
`observedMutations()`

and`calcObservedMutations()`

. - Fixed a minor bug in calculating the denominator for mutation frequency in
`calcObservedMutations()`

for sequences with non-triplet overhang at the tail. - Renamed column names of observed mutations (previously
`OBSERVED`

) and expected mutations (previously`EXPECTED`

) returned by`observedMutations()`

and`expectedMutations()`

to`MU_COUNT`

and`MU_EXPECTED`

respectively.

Selection Analysis:

`calcBaseline()`

no longer calls`collapseClones()`

automatically if a`CLONE`

column is present. As indicated by the documentation for`calcBaseline()`

users are advised to obtain effective clonal sequences (for example, calling`collapseClones()`

) before running`calcBaseline()`

.- Updated vignette to reflect changes in
`calcBaseline()`

.

## Version 0.1.7: May 14, 2017¶

Mutation Profiling:

- Fixed a bug in
`collapseClones()`

that prevented it from running when`nproc`

is greater than 1.

## Version 0.1.6: May 12, 2017¶

General:

- Internal changes for compatibility with dplyr v0.6.0.
- Removed data.table dependency.

Mutation Profiling:

- Fixed a bug in
`collapseClones()`

that resulted in erroneous`CLONAL_SEQUENCE`

and`CLONAL_GERMLINE`

being returned. - Added a vignette describing basic mutational analysis.
- Remove console notification that
`observedMutations`

was running.

## Version 0.1.5: March 23, 2017¶

General:

- License changed to Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Selection Analysis:

- Fixed a bug in p-value calculation in
`summarizeBaseline()`

. The returned p-value can now be either positive or negative. Its magnitude (without the sign) should be interpreted as per normal. Its sign indicates the direction of the seLicense chalection detected. A positive p-value indicates positive selection, whereas a negative p-value indicates negative selection. - Added
`editBaseline()`

to exported functions, and a corresponding section in the vignette. - Fixed a bug in counting the total number of observed mutations when performing
a local test for codon-by-codon selection analysis in
`calcBaseline()`

.

Targeting Models:

- Added
`numMutationsOnly`

argument to`createSubstitutionMatrix()`

, enabling parameter tuning for`minNumMutations`

. - Added functions
`minNumMutationsTune()`

and`minNumSeqMutationsTune()`

to tune for parameters`minNumMutations`

and`minNumSeqMutations`

in functions`createSubstitutionMatrix()`

and`createMutabilityMatrix()`

respectively. Also added function`plotTune()`

which helps visualize parameter tuning using the abovementioned two new functions. - Added human kappa and lambda light chain, silent, 5-mer, functional targeting
model (
`HKL_S5F`

). - Renamed
`HS5FModel`

as`HH_S5F`

,`MRS5NFModel`

as`MK_RS5NF`

, and`U5NModel`

as`U5N`

. - Added human heavy chain, silent, 1-mer, functional substitution model (
`HH_S1F`

), human kappa and lambda light chain, silent, 1-mer, functional substitution model (`HKL_S1F`

), and mouse kappa light chain, replacement and silent, 1-mer, non-functional substitution model (`MK_RS1NF`

). - Added
`makeDegenerate5merSub`

and`makeDegenerate5merMut`

which make degenerate 5-mer substitution and mutability models respectively based on the 1-mer models. Also added`makeAverage1merSub`

and`makeAverage1merMut`

which make 1-mer substitution and mutability models respectively by averaging over the 5-mer models.

Mutation Profiling:

- Added
`returnRaw`

argument to`calcObservedMutations()`

, which if true returns the positions of point mutations and their corresponding mutation types, as opposed to counts of mutations (hence “raw”). - Added new functions
`slideWindowSeq()`

and`slideWindowDb()`

which implement a sliding window approach towards filtering a single sequence or sequences in a data.frame which contain(s) equal to or more than a given number of mutations in a given number of consecutive nucleotides. - Added new function
`slideWindowTune()`

which allows for parameter tuning for using`slideWindowSeq()`

and`slideWindowDb()`

. - Added new function
`slideWindowTunePlot()`

which visualizes parameter tuning by`slideWindowTune()`

.

Distance Calculation:

- Fixed a bug in
`distToNearest`

wherein`normalize="length"`

for 5-mer models was resulting in distances normalized by junction length squared instead of raw junction length. - Fixed a bug in
`distToNearest`

wherein`symmetry="min"`

was calculating the minimum of the total distance between two sequences instead of the minimum distance at each mutated position. - Added
`findThreshold`

function to infer clonal distance threshold from nearest neighbor distances returned by`distToNearest`

. - Renamed the
`length`

option for the`normalize`

argument of`distToNearest`

to`len`

so it matches Change-O. - Deprecated the
`HS1FDistance`

and`M1NDistance`

distance models, which have been renamed to`hs1f_compat`

and`m1n_compat`

in the`model`

argument of`distToNearest`

. These deprecated models should be used for compatibility with DefineClones in Change-O v0.3.3. These models have been replaced by replaced by`hh_s1f`

and`mk_rs1nf`

, which are supported by Change-O v0.3.4. - Renamed the
`hs5f`

model in`distToNearest`

to`hh_s5f`

. - Added support for
`MK_RS5NF`

models to`distToNearest`

. - Updated
`calcTargetingDistance()`

to enable calculation of a symmetric distance matrix given a 1-mer substitution matrix normalized by row, such as`HH_S1F`

. - Added a Gaussian mixture model (GMM) approach for threshold determination to
`findThreshold`

. The previous smoothed density method is available via the`method="density"`

argument and the new GMM method is available via`method="gmm"`

. - Added the functions
`plotGmmThreshold`

and`plotDensityThreshold`

to plot the threshold detection results from`findThreshold`

for the`"gmm"`

and`"density"`

methods, respectively.

Region Definition:

- Deleted
`IMGT_V_NO_CDR3`

and`IMGT_V_BY_REGIONS_NO_CDR3`

. Updated`IMGT_V`

and`IMGT_V_BY_REGIONS`

so that neither includes CDR3 now.

## Version 0.1.4: August 5, 2016¶

Selection Analysis:

- Fixed a bug in calcBaseline wherein the germline column was incorrected hardcoded, leading to erroneous mutation counts for some clonal consensus sequences.

Targeting Models:

- Added
`numSeqMutationsOnly`

argument to`createMutabilityMatrix()`

, enabling parameter tuning for`minNumSeqMutations`

.

## Version 0.1.3: July 31, 2016¶

General:

- Added ape and igraph dependency
- Removed the
`InfluenzaDb`

data object, in favor of the updated`ExampleDb`

provided in alakazam 0.2.4. - Added conversion of sequence to uppercase for several functions to support data that was not generated via Change-O.

Distance Calculation:

- Added the
`cross`

argument to`distToNearest()`

which allows restriction of distances to only distances across samples (ie, excludes within-sample distances). - Added
`mst`

flag to`distToNearest()`

, which will return all distances to neighboring nodes in a minimum spanning tree. - Updated single nucleotide distance models to use the new C++ distance methods in alakazam 0.2.4 for better performance.
- Fixed a bug leading to failed distance calculations for the
`aa`

model of`distToNearest()`

. - Fixed a bug wherein gap characters where being translated into Ns (Asn)
rather than Xs within the
`aa`

model of`distToNearest()`

.

Mutation Profiling:

- Added the
`MutationDefinition`

`VOLUME_MUTATIONS`

. - Added the functions
`shmulateSeq()`

and`shmulateTree()`

to simulate mutations on sequences and lineage trees, respectively, using a 5-mer targeting model. - Renamed
`collapseByClone`

,`calcDbExpectedMutations`

and`calcDbObservedMutations`

to`collapseClones`

,`expectedMutations`

, and`observedMutations`

, respectively.

Selection Analysis:

- Fixed a bug wherein passing a
`Baseline`

object through`groupBaseline()`

multiple times resulted in incorrect normalization. - Added
`title`

options to`plotBaselineSummary()`

and`plotBaselineDensity()`

. - Added more control over colors and group ordering to
`plotBaselineSummary()`

and`plotBaselineDensity()`

. - Added the
`testBaseline()`

function to test the significance of differences between two selection distributions. - Improved selection analysis vignette.

## Version 0.1.2: February 20, 2016¶

General:

- Renamed package from shm to shazam.
- Internal changes to conform to CRAN policies.
- Compressed and moved example database to the data object
`InfluenzaDb`

. - Fixed several bugs where functions would not work properly when passed
a
`dplyr::tbl_df`

object instead of a`data.frame`

. - Changed R dependency to R >= 3.1.2.
- Added stringi dependency.

Distance Calculation:

- Fixed a bug wherein
`distToNearest()`

did not return the nearest neighbor with a non-zero distance.

Targeting Models:

- Performance improvements to
`createSubstitutionMatrix()`

,

`createMutabilityMatrix()`

, and`plotMutability()`

. - Modified color scheme in
`plotMutability()`

. - Fixed errors in the targeting models vignette.

Mutation Profiling:

- Added the
`MutationDefinition`

objects`MUTATIONS_CHARGE`

,`MUTATIONS_HYDROPATHY`

,`MUTATIONS_POLARITY`

providing alternate approaches to defining replacement and silent annotations to mutations when calling`calcDBObservedMutations()`

and`calcDBExpectedMutations()`

. - Fixed a few bugs where column names, region definitions or mutation models were not being recognized properly when non-default values were used.
- Made the behavior of
`regionDefinition=NULL`

consistent for all mutation profiling functions. Now the entire sequence is used as the region and calculations are made accordingly. `calcDBObservedMutations()`

returns R and S mutations also when`regionDefinition=NULL`

. Older versions reported the sum of R and S mutations. The function will add the columns`OBSERVED_SEQ_R`

and`OBSERVED_SEQ_S`

when`frequency=FALSE`

, and`MU_FREQ_SEQ_R`

and`MU_FREQ_SEQ_R`

when`frequency=TRUE`

.

## Version 0.1.1: December 18, 2015¶

General:

- Swapped dependency on doSNOW for doParallel.
- Swapped dependency on plyr for dplyr.
- Swapped dependency on reshape2 for tidyr.
- Documentation clean up.

Distance Calculation:

- Changed underlying method of calcTargetingDistance to be negative log10 of the probability that is then centered at one by dividing by the mean distance.
- Added
`symmetry`

parameter to distToNearest to change behavior of how asymmetric distances (A->B != B->A) are combined to get distance between A and B. - Updated error handling in distToNearest to issue warning when unrecognized character is in the sequence and return an NA.
- Fixed bug in ‘aa’ model in distToNearest that was calculating distance incorrectly when normalizing by length.
- Changed behavior to return nearest nonzero distance neighbor.

Mutation Profiling:

- Renamed calcDBClonalConsensus to collapseByClone Also, renamed argument collapseByClone to expandedDb.
- Fixed a (major) bug in calcExpectedMutations. Previously, the targeting calculation was incorrect and resulted in incorrect expected mutation frequencies. Note, that this also resulted in incorrect BASELINe Selection (Sigma) values.
- Changed denominator in calcObservedMutations to be based on informative (unambiguous) positions only.
- Added nonTerminalOnly parameter to calcDBClonalConsensus indicating whether to consider mutations at leaves or not (defaults to false).

Selection Analysis:

- Updated groupBaseline. Now when regrouping a Baseline object (i.e. grouping previously grouped PDFs) weighted convolution is performed.
- Added “imbalance” test statistic to the Baseline selection calculation.
- Extended the Baseline Object to include binomK, binomN and binomP Similar to numbOfSeqs, each of these are a matrix. They contain binomial inputs for each sequence and region.

Targeting Models:

- Added
`minNumMutations`

parameter to createSubstitutionMatrix. This is the minimum number of observed 5-mers required for the substituion model. The substitution rate of 5-mers with fewer number of observed mutations will be inferred from other 5-mers. - Added
`minNumSeqMutations`

parameter to createMutabilityMatrix. This is the minimum number of mutations required in sequences containing the 5-mers of interest. The mutability of 5-mers with fewer number of observed mutations in the sequences will be inferred. - Added
`returnModel`

parameter to createSubstitutionMatrix. This gives user the option to return 1-mer or 5-mer model. - Added
`returnSource`

parameter to createMutabilityMatrix. If TRUE, the code will return a data frame indicating whether each 5-mer mutability is observed or inferred. - In createSubstitutionMatrix and createMutabilityMatrix, fixed a bug when multipleMutation is set to “ignore”.
- Changed inference procedure for the 5-mer substitution model.
- Added inference procedure for 5-mers without enough observed mutations in the mutability model.
- Fixed a bug in background 5-mer count for the RS model.
- Fixed a bug in IMGT gap handling in createMutabilityMatrix.
- Fixed a bug that occurs when sequences are in lower cases.

## Version 0.1.0: June 18, 2015¶

Initial public release.

General:

- Restructured the S4 class documentation.
- Fixed bug wherein example
`Influenza.tab`

file did not load on Mac OS X. - Added citations for
`citation("shazam")`

command. - Added dependency on data.table >= 1.9.4 to fix bug that occured with earlier versions of data.table.

Distance Calculation:

- Added a human 1-mer substitution matrix,
`HS1FDistance`

, based on the Yaari et al, 2013 data. - Set the
`hs1f`

as the default distance model for`distToNearest()`

. - Added conversion of sequences to uppercase in
`distToNearest()`

. - Fixed a bug wherein unrecongized (including lowercase) characters would lead to silenting returning a distance of 0 to the neared neighbor. Unrecognized characters will now raise an error.

Mutation Profiling:

- Fixed bug in
`calcDBClonalConsensus()`

so that the function now works correctly when called with the argument`collapseByClone=FALSE`

. - Added the
`frequency`

argument to`calcObservedMutations()`

and`calcDBObservedMutations()`

, which enables return of mutation frequencies rather the default of mutation counts.

Targeting Models:

- Removed
`M3NModel`

and all options for using said model. - Fixed bug in
`createSubstitutionMatrix()`

and`createMutabilityMatrix()`

where IMGT gaps were not being handled.

## Version 0.1.0.beta-2015-05-30: May 30, 2015¶

General:

- Added more error checking.

Targeting Models:

- Updated the targeting model workflow to include a clonal consensus step.

## Version 0.1.0.beta-2015-05-11: May 11, 2015¶

Targeting Models:

- Added the
`U5NModel`

, which is a uniform 5-mer model. - Improvements to
`plotMutability()`

output.

## Version 0.1.0.beta-2015-05-05: May 05, 2015¶

Prerelease for review.