Changes in version 2.6.2
- Corrected a bug in the two-group interaction (without pairing)
functionality, when the groups were imbalanced, as identified by
Samuel Chen. Fixes GitHub issue #35.
Changes in version 2.5.4
- As CellRanger 7 includes both spliced and unspliced counts in their
count matrix, we want to mimic this behavior by adding more
pre-defined output formats in the loadFry function. We added "all"
and "U+S+A" to include all counts in the count matrix. Moreover, now
the "scRNA" output format has an "unspliced" field, which contains
the unspliced count matrix.
Changes in version 2.5.3
- Fix bug where salmonEC did not correct equivalence class names for
going from 0-indexing to 1-indexing internally. In prior
versions, to correctly link equivalence classes to gene names, users
would have needed to manually add a value of 1 to the equivalence
class names, which was erroneously not mentioned in the man files.
After this bug fix, if the equivalence class identifier reads 1|2|8,
then the equivalence class is immediatly compatible with the
transcripts and their respective genes in rows 1, 2 and 8 of
'tx2gene_matched', without any further user intervention.
Changes in version 2.5.1
- Fix plotAllelicGene() so that when samples have an allele with no
expression at the gene level, it doesn't throw an error trying to
divide by 0.
Changes in version 2.4.0
- For simple paired swish analysis, adding a fast=1 method which uses
a one-sample z-score on the paired LFCs (averaged over samples, then
median over inferential replicates). The permutation is computed by
changing the signs of the LFC matrix and recomputing z-scores.
Testing on the vignette example, but using all the transcripts, the
one-sample z-score method takes < 20 seconds while the signed rank
method takes > 200 seconds (12x speedup), while they have a high
rate of agreement on the detected set (30:1 in common vs
discordant).
- Removed the fast=0 methods that were previously implemented where
ranks could optionally be recomputed for every permutation. This was
much slower and didn't have any appreciable benefit.
- readEDS() has moved to the eds package, such that fishpond no longer
requires Rcpp and C++ code compilation.
- Fix bug identified by GitHub user @JosephLalli, where
importAllelicCounts would find a1 and a2 strings internal to gene
IDs, instead of at the suffix.
Changes in version 2.3.22
- Fix bug identified by GitHub user @JosephLalli, where
importAllelicCounts would find a1 and a2 strings internal to gene
IDs, instead of at the suffix.
Changes in version 2.3.14
- For simple paired swish analysis, adding a fast=1 method which uses
a one-sample z-score on the paired LFCs (averaged over samples, then
median over inferential replicates). The permutation is computed by
changing the signs of the LFC matrix and recomputing z-scores.
Testing on the vignette example, but using all the transcripts, the
one-sample z-score method takes <20 seconds while the signed rank
method takes >200 seconds (12x speedup), while they have a high rate
of agreement on the detected set ( 30:1 in common vs discordant).
- Removed the fast=0 methods that were previously implemented where
ranks could optionally be recomputed for every permutation. This was
much slower and didn't have any appreciable benefit.
Changes in version 2.3.7
- readEDS() has moved to the eds package.
Changes in version 2.2.0
- New vignette demonstrating allelic analysis at isoform, TSS, or
gene-level. See more details below.
- New import functions for equivalence class analysis of Salmon or
alevin data, written by Jeroen Gilis. See salmonEC() and alevinEC()
man pages.
- New plotAllelicGene() and plotAllelicHeatmap() functions for
plotting results from allelic expression analysis.
- New makeTx2Tss() helper function for allelic analysis.
- Now importFromAllelicCounts() can take a GRanges object as the
tx2gene argument, so that ranges will be distributed to the
rowRanges of the output SummarizedExperiment.
- Adding shiftX argument to plotInfReps() for numeric x variable, to
help with overplotting.
- Re-organized package for new pkgdown homepage:
https://mikelove.github.io/fishpond
Changes in version 2.0.0
- New loadFry() function, written by Dongze He with contributions from
Steve Lianoglou and Wes Wilson. loadFry() helps users to import and
process alevin-fry quantification results. Can process spliced,
unspliced and ambiguous counts separately and flexibly. Has specific
output formats designed for downstream use with scVelo or velocity
analysis. See ?loadFry for more details.
- Adding correlation tests: Spearman or Pearson correlations of a
numeric covariate with the log counts, or with the log fold changes
across pairs. The Spearman correlation test with counts was already
implemented in the original SAMseq method as response type =
"Quantitative". For new functionality see 'cor' argument in the
?swish man page.
- Adding importAllelicCounts() to facilitate importing Salmon
quantification data against a diploid transcriptome. Can import
either as a 'wide' format or as 'assays'. Leverages tximeta(). For
gene-level summarization, importAllelicCounts() can create an
appropriate tx2gene table with the necessary a1 and a2 suffices, and
it will automatically set txOut=FALSE, see ?importAllelicCounts for
more details.
- Added a 'q' argument to plotInfReps to change the intervals when
making point and line plots.
- Switched the legend of plotInfReps so that reference levels will now
be on the bottom, and non-reference (e.g. treatment) on top.
Changes in version 1.99.18
- Added helper functionality to importAllelicCounts, so it will create
an appropriate tx2gene table with the necessary a1 and a2 suffices,
and it will automatically set txOut=FALSE.
- Added a 'q' argument to plotInfReps to change the intervals when
making point and line plots.
- Switched the legend of plotInfReps so that reference levels will now
be on the bottom, and non-reference (e.g. treatment) on top.
- Added loadFry() to process alevin-fry quantification result. Can
process spliced, unspliced and ambiguous counts separately and
flexibly.
Changes in version 1.99.15
- Adding correlation tests: Spearman or Pearson correlations of a
numeric covariate with the log counts, or with the log fold changes
across pairs. The Spearman correlation test with counts was already
implemented in the original SAMseq method as response type =
"Quantitative". For new functionality see 'cor' argument in the
?swish man page.
- Adding importAllelicCounts() to facilitate importing Salmon
quantification data against a diploid transcriptome. Can import
either as a 'wide' format or as 'assays'. Leverages tximeta().
Changes in version 1.9.6
- Specifying ties.method in matrixStats::rowRanks.
Changes in version 1.9.1
- Added importAllelicCounts() with options for importing Salmon
quantification on diploid transcriptomes.
Changes in version 1.8.0
- Added note in vignette about how to deal with estimated batch
factors, e.g. from RUVSeq or SVA. Two strategies are outlined:
either discretizing the estimate batch factors and performing
stratified analysis, or regressing out the batch-associated
variation using limma's removeBatchEffect. Demonstation code is
included.
Changes in version 1.6.0
- Added makeInfReps() to create pseudo-inferential replicates via
negative binomial simulation from mean and variance matrices. Note:
the mean and the variance provide the inferential distribution per
element of the count matrix. See preprint for details,
doi: 10.1101/2020.07.06.189639.
- Added splitSwish() and addStatsFromCSV(), which can be used to
distribute running of Swish across a number of jobs managed by
Snakemake. See vignette for description of a suggested workflow. For
a large single-cell dataset with mean and variance summaries of
inferential uncertainty, splitSwish() avoids generating the
inferential replicate counts until the data has been split into
smaller pieces and sent to different jobs, then only the necessary
summary statistics are gathered and q-values computed by
addStatsFromCSV().
- plotInfReps() gains many new features to facilitate plotting of
inferential count distributions for single cells, as quantified with
alevin and imported with tximport. E.g. allow for numeric x argument
plus grouping with cov for showing counts over pseudotime across
groups of cells. Also added applySF argument which can be used to
divide out a size factor, and the reorder argument which will
re-order the samples/cells within groups by the count. plotInfReps()
will draw boxplots with progressively thinner visual features as the
number of cells grows to make the plots still legible.
Changes in version 1.5.2
- First version of makeInfReps(), to create pseudo-infReps via
negative binomial simulation from set of mean and variance matrices
in the assays of the SummarizedExperiment.
Changes in version 1.4.0
- Added isoformProportions(), which can be run after scaleInfReps()
and optionally after filtering out transcripts using labelKeep().
Running swish() after isoformProportions() will produce differential
transcript usage (DTU) results, instead of differential transcript
expression (DTE) results. Example in vignette.
- Default number of permutations increased from 30 to 100. It was
observed that there was too much fluctuation in the DE called set
for nperms=30 across different seeds, and setting to 100 helped to
stabilize results across seeds, without increasing running time too
much. For further reduced dependence on the seed, even higher values
of nperms (e.g. 200, 300) can be used.
Changes in version 1.3.8
- Added isoformProportions(), which can be run after scaleInfReps()
and optionally after filtering out transcripts using labelKeep().
Running swish() after isoformProportions() will produce differential
transcript usage (DTU) results, instead of differential transcript
expression (DTE) results. Example in vignette.
Changes in version 1.3.4
- Default number of permutations increased from 30 to 100. It was
observed that there was too much fluctuation in the DE called set
for nperms=30 across different seeds, and setting to 100 helped to
stabilize results across seeds, without increasing running time too
much. For further reduced dependence on the seed, even higher values
of nperms (e.g. 200, 300) can be used.
Changes in version 1.2.0
- Switching to a faster version of Swish which only computes the ranks
of the data once, and then re-uses this for the permutation
distribution. This bypasses the addition of uniform noise per
permutation and is 10x faster. Two designs which still require
re-computation of ranks per permutation are the paired analysis and
the general interaction analysis. Two-group, stratified two-group,
and the paired interaction analysis now default to the new fast
method, but the original, slower method can be used by setting
fast=0 in the call to swish().
- Adding Rcpp-based function readEDS() written by Avi Srivastava which
imports the sparse counts stored in Alevin's Efficient Data Storage
(EDS) format.
- Changed the vignette so that it (will) use a linkedTxome, as
sometime the build would break if the Bioc build machine couldn't
access ftp.ebi.ac.uk.
- Add 'computeInfRV' function. InfRV is not used in the Swish methods,
only for visualization purposes in the Swish paper.
- removed 'samr' from Imports, as it required source installation,
moved to Suggests, for optional qvalue calculation
Changes in version 0.99.30
- added two interaction tests, described in ?swish
- incorporate qvalue package for pvalue, locfdr and qvalue
- added plotMASwish() to facilitate plotting
- wilcoxP is removed, and the mean is used instead
Changes in version 0.99.0
- fishpond getting ready for submission to Bioc