Introduction
ProActive automatically detects regions of gapped and
elevated read coverage using a 2D pattern-matching algorithm.
ProActive can detect, characterize and visualize read
coverage patterns in both genomes and metagenomes. Optionally, users may
provide gene predictions associated with their genome or metagenome in
the form of a .gff file. In this case, ProActive will
generate an additional output table containing the gene predictions
found within the detected regions of gapped and elevated read
coverage.
Analyzing coverage data is important because gaps or elevations in coverage can provide insight into a community’s (metagenome) or organism’s (genome) genetic activity, for example-
Elevations in read coverage can be an indication of highly active and/or abundant mobile genetic elements (MGEs). MGEs that are actively replicating or are highly abundant may generate more sequencing reads than the rest of their host bacterium’s genome thus creating a region of elevated read coverage at the element’s insertion site(s).
Gaps in read coverage can indicate genetic heterogeneity in a bacterial population. Sub-populations of bacteria with and without specific gene sequences may form gaps or dips in mapped read coverages. Genetic regions with high mutation rates may also generate gaps in read coverage.
Note: The read coverage pattern-matching employed by
ProActive is only as good as the provided data. There are
other non-biological situations that can create gaps or elevations in
read coverage. For example, chimeric assemblies or contaminants can
create odd read coverage artifacts. Because of this, ProActive is best
used as a screening method to identify genetic regions for further
investigation.
Installation
CRAN install
install.packages("ProActive")
library(ProActive)GitHub install
if (!require("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("jlmaier12/ProActive")
library(ProActive)Input data
Pileups
ProActive detects read coverage patterns using a pattern-matching algorithm that operates on pileup files. A pileup file is a file format where each row summarizes the ‘pileup’ of reads at specific genomic locations. Pileup files can be used to generate a rolling mean of read coverages and associated base pair positions which reduces data size while preserving read coverage patterns. ProActive requires that input pileups files be generated using a 100 bp window/bin size.
Pileup files can be generated by mapping sequencing reads to a metagenome or genome fasta file. Read mapping should be performed using a high minimum identity (0.97 or higher) and random mapping of ambiguous reads. The pileup files needed for ProActive are generated using the .bam files produced during read mapping.
Some read mappers, like BBMap,
allow for the generation of pileup files in the bbmap.sh
command with use of the bincov output with the
covbinsize=100 parameter/argument. Otherwise,
BBMap’s pileup.sh
can convert .bam files produced by any read mapper to pileup
files compatible with ProActive using the
bincov output with binsize=100.
Pileup files must use a 100 bp window/bin size for the rolling mean!
The input pileup file for metagenomes must have the following format:
Dataframe with four columns:
- V1: Contig accession
- V2: Mapped read coverage values averaged over 100 bp windows
- V3: Starting position (bp) of each 100 bp window. Restarts from 100 at the start of each new contig.
- V4: Starting position (bp) of each 100 bp window. Does NOT restart at the start of each new contig.
| V1 | V2 | V3 | V4 |
|---|---|---|---|
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 54.66 | 100 | 175075473 |
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 59.13 | 200 | 175075573 |
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 53.99 | 300 | 175075673 |
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 54.69 | 400 | 175075773 |
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 54.40 | 500 | 175075873 |
| NODE_1911 length_44214_cov_4.82142_ID_9560073 | 52.11 | 600 | 175075973 |
Note that the format for a genome pileup will be slightly different! The third column (V3) does not restart and the fourth column (V4) starts from 0. ProActive accounts for the differences in pileup formats between genomes and metagenomes.
Users may also use the ‘sampleMetagenomePileup’ and ‘sampleGenomePileup’ files that come pre-loaded with ProActive as references for proper input file format.
gff TSV
Optionally, ProActive will accept a .gff file as additional input. The .gff file must be associated with the same metagenome or genome used to create your pileup file. The .gff file should be a TSV and should follow the same general format described here.
The input .gff file must have the following format exactly:| V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 |
|---|---|---|---|---|---|---|---|---|
| NODE_1911 | Prodigal:002006 | CDS | 318 | 1166 | . |
|
0 | ID=NJKKNKEE_164175;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164175;product=hypothetical protein |
| NODE_1911 | Prodigal:002006 | CDS | 1198 | 1938 | . |
|
0 | ID=NJKKNKEE_164176;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164176;product=hypothetical protein |
| NODE_1911 | Prodigal:002006 | CDS | 1938 | 2582 | . |
|
0 | ID=NJKKNKEE_164177;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164177;product=hypothetical protein |
| NODE_1911 | Prodigal:002006 | CDS | 2722 | 3561 | . |
|
0 | ID=NJKKNKEE_164178;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164178;product=hypothetical protein |
| NODE_1911 | Prodigal:002006 | CDS | 3671 | 4063 | . |
|
0 | ID=NJKKNKEE_164179;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164179;product=hypothetical protein |
| NODE_1911 | Prodigal:002006 | CDS | 4128 | 4670 | . |
|
0 | ID=NJKKNKEE_164180;eC_number=1.11.1.1;Name=rbr1_24;db_xref=COG:COG1592;gene=rbr1_24;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q97FZ9;locus_tag=NJKKNKEE_164180;product=Rubrerythrin-1 |
(Hint- if you are using a gff file output by PROKKA, you may need to remove some unnecessary (for ProActive) lines of text at the top of the file. There are various ways one can remove these additional lines, however, a nice command-line solution is:)
The ‘COMMONID’ should be a value that your contig or genome accessions start with that is the same across all the rows. For example, the ‘COMMONID’ for the contig accessions in the sampleMetagenomegffTSV displayed above could be “NODE” since all the accessions start with “NODE”.
ProActive()
ProActive() is the main function in the ProActive R
package. This function filters contigs/chunks based on length and read
coverage, performs pattern-matching to detect gaps and elevations in
read coverage, and identifies start and stop positions and sizes of
pattern-matches.
Function components
Chunking
Currently, ProActive() can only detect one gap or
elevation pattern per contig. Until ProActive() is able to
detect multiple read coverage patterns per contig, we rely on ‘chunking’
large contig into smaller subsets (defined by chunkSize) so
that pattern-matching can be performed on each chunk as if it were an
individual contig. The chunking mechanism is what allows
ProActive() to pattern-match on genomes. When
contigs/genomes are chunked, they are assigned a sequential value to
link chunks back together (i.e. “NODE_1_chunk_1, NODE_1_chunk_2,
NODE_1_chunk_3, …). Note that the remaining ‘chunk’ of a contig/genome
may not be long enough to perform pattern-matching on. Chunks too small
for pattern-matching will be put in the output FilteredOut table. If a
chunk splits a gap or elevation pattern in half,
ProActive() will attempt to detect this and report it to
the user as a ‘possible pattern-match continuity’ between contig/genome
chunks. Pattern-match continuity is detected when two sequential chunks
have a partial elevation/gap pattern going off the right and left side
of the chunks, respectively.
Filtering
Contigs/chunks that are too short or have little to no read coverage
are filtered out prior to pattern-matching. ProActive()
filters out contigs/chunks that do not have at least 10x coverage on a
total of 5,000 bp across the whole contig/chunk. The read coverage
filtering was done in this way to avoid filtering out long
contigs/chunks with small elevations in read coverage that might get
removed if filtering was done with read coverage averages or medians.
Additionally, contigs/chunks less than 30,000 bp are filtered out by
default, however this can be changed with the
minContigLength parameter which can be set to a minimum of
25,000 bp. If you would like to reduce the size of your input
metagenome pileup file for ProActive(),
consider pre-filtering your assembly for contigs greater than
25,000 bp prior to read mapping!
Changing pileup windowSize
The input pileup files have 100 bp windows in which the mapped read
coverage is averaged over. ProActive() increases the window
size prior to pattern-matching by averaging the read coverages over a
value specified with windowSize. In many cases, read
coverage patterns don’t require the resolution that 100 bp windows
provide, however, starting with a 100 bp windowSize means the higher
resolution is available if needed. While users can use the 100 bp
windowSize for ProActive(), the processing
time will be increased significantly and noisy data may
interfere with pattern-matching. We find that the default 1,000 bp
windowSize provides a nice balance between processing time
and read coverage pattern resolution. If you’d like more resolution than
the 1,000 bp windowSize provides, consider dropping the
windowSize to 500. If you’d like fine scale read coverage
resolution, consider viewing the contigs/genome with a software like
Integrative Genomics Viewer IGV.
Pattern-matching
ProActive() detects read coverage patterns using a 2D
pattern-matching algorithm. Several predefined patterns, described
below, are built using the specific length and read coverage values of
the contig/chunk being assessed. Patterns are translated across each
contig/chunk in 1,000 bp sliding windows and at each translation, a
pattern-match score is calculated by taking the mean absolute difference
of the read coverage and the pattern values. The smaller the
match-score, the better the pattern-match. After a pattern is fully
translated across a contig/chunk, certain aspects of the pattern are
changed (i.e. height, base, width) and translation is repeated. This
process of translation and pattern re-scaling is repeated until a large
number of pattern variations are tested. After pattern-matching is
complete, the pattern associated with the best match-score is used for
contig/chunk classification. Contigs/chunks are classified as
‘Elevation’, ‘Gap’, or ‘NoPattern’ during pattern-matching.
Elevation pattern:
The ‘elevation’ class is defined by a ‘block’ pattern which
represents the read covarage pattern formed when reads from a highly
active/abundant MGE map back to its respective integration site in the
host/originating genome. During pattern-matching, the height (max), base
(min) and width are altered and all pattern variations are translated
across the contig/chunk. The block pattern width never get smaller than
10,000 bp by default, however this can be changed with the
minSize parameter.
Gap pattern:
The ‘gap’ class is essentially the reverse of the ‘elevation’ class. It is defined by a gapped block pattern which represents a depression in read coverage caused by genetic heterogeneity at a specific region leading to poor read mapping. The same pattern-matching steps (alteration of pattern and pattern translation) used for the elevation pattern are used for the gap pattern.
Elevation/Gap pattern:
Elevations and gaps that trail off one side of a contig/chunk are hard to classify as the read coverage could be seen as a gap or elevation depending on how you’re looking at it. Our solution to this problem was to classify the contig/chunk as ‘gap’ if the elevated region was greater than 50% of the length of the contig/chunk. Otherwise the classification is ‘elevated’.
noPattern:
Since the best pattern-match for each contig/chunk is determined by comparing match-scores amongst all pattern-variations from all pattern classes, we needed a ‘negative control’ pattern to compare against. The ‘NoPattern’ ‘pattern’ serves as a negative control by matching to contigs/chunks with no read coverage patterns. We made two NoPattern patterns which consist of a horizontal line the same length as the contig/chunk being assessed at either the contig/chunk’s average or median read coverage. This pattern is not re-scaled or translated in any way.
Calculating elevation ratios
Every gap and elevation classification receives an elevation ratio which is simply the pattern-match’s maximum value divided by the minimum value. For elevation classifications, you can think of the elevation ratio as ‘the elevated region has X times greater read coverage than the non-elevated region’. Conversely, for gap classifications, the elevation ratio can be thought of as ‘the gapped region has X times less read coverage than the non-gapped region’.
Extracting gene annotations in elevated/gapped regions
If a .gff file is provided, then ProActive() will
extract the gene annotations found within the gapped and elevated
pattern-match regions and provide them to the user in an output table
(GeneAnnotTable). An additional column will be added with the
classification information (gap or elevation) associated with the gene
annotations. If the input .gff file contains a gene ‘product’ field in
the attributes column (9th column in the dataframe), then
ProActive() will extract the product information into a
separate column for easy visualization and filtering of annotations of
interest.
Usage
Default arguments in metagenome mode:
ProActiveOutputMetagenome <- ProActive(
pileup = sampleMetagenomePileup,
mode = "metagenome",
gffTSV = sampleMetagenomegffTSV
)
#> Preparing input file for pattern-matching...
#> Starting pattern-matching...
#> A quarter of the way done with pattern-matching
#> Half of the way done with pattern-matching
#> Almost done with pattern-matching!
#> Summarizing pattern-matching results
#> Finding gene predictions in elevated or gapped regions of read coverage...
#> Finalizing output
#> Execution time: 1.93secs
#> 0 contigs were filtered out based on low read coverage
#> 0 contigs were filtered out based on length (< minContigLength)
#>
#> Elevation Gap NoPattern
#> 3 3 1Default arguments in genome mode:
ProActiveOutputGenome <- ProActive(
pileup = sampleGenomePileup,
mode = "genome",
gffTSV = sampleGenomegffTSV
)
#> Preparing input file for pattern-matching...
#> Starting pattern-matching...
#> A quarter of the way done with pattern-matching
#> Half of the way done with pattern-matching
#> Almost done with pattern-matching!
#> Summarizing pattern-matching results
#> Finding gene predictions in elevated or gapped regions of read coverage...
#> Finalizing output
#> Execution time: 29.79secs
#> 0 contigs were filtered out based on low read coverage
#> 0 contigs were filtered out based on length (< minContigLength)
#>
#> Elevation Gap NoPattern
#> 25 3 21Note that ProActive can be run without the gffTSV file!
Arguments/parameters
ProActive(pileup,
mode,
gffTSV,
windowSize = 1000,
minSize = 10000,
maxSize = Inf,
minContigLength = 30000,
chunkSize = 50000,
chunkContigs = FALSE,
IncludeNoPatterns = FALSE,
verbose = TRUE,
saveFilesTo
)-
pileup: A .txt file containing mapped sequencing read coverages averaged over 100 bp windows/bins. -
mode: Either “genome” or “metagenome” -
gffTSV: Optional, a .gff file (TSV) containing gene predictions associated with the .fasta file used to generate the pileup. -
windowSize: The number of basepairs to average read coverage values over. Options are 100, 200, 500, 1000 ONLY. Default is 1000. -
minSize: The minimum size (in bp) of elevation or gap patterns. Default is 10000. -
maxSize: The maximum size (in bp) of elevation or gap patterns. Default is NA (i.e. no maximum). -
minContigLength: The minimum contig/chunk size (in bp) to perform pattern-matching on. Default is 25000. -
chunkSize: Ifmode=“genome” OR ifmode=“metagenome” andchunkContigs=TRUE, chunk the genome or contigs, respectively, into smaller subsets for pattern-matching.chunkSizedetermines the size (in bp) of each ‘chunk’. Default is 100000. -
chunkContigs: TRUE or FALSE, If TRUE andmode=“metagenome”, contigs longer than thechunkSizewill be ‘chunked’ into smaller subsets and pattern-matching will be performed on each subset. Default is FALSE. -
IncludeNoPatterns: TRUE or FALSE, If TRUE the noPattern pattern-matches will be included in the ProActive PatternMatches output list. If you would like to visualize the noPattern pattern-matches inplotProActiveResults(), this should be set to TRUE. -
verbose: TRUE or FALSE. Print progress messages to console. Default is TRUE. -
saveFilesTo: Optional, Provide a path to the directory you wish to save output to. A folder will be made within the provided directory to store results.
Output
The output of ProActive is a list containing six
objects:
- SummaryTable: A table containing all pattern-matching classifications
- CleanSummaryTable: A table containing only gap and elevation pattern-match classifications (i.e. noPattern classifications removed)
- PatternMatches: A list object containing information needed to
visualize the pattern-matches in
plotProActiveResults() - FilteredOut: A table containing contigs/chunks that were filtered out for being too small or having too low read coverage
- Arguments: A list object containing arguments used for pattern-matching (windowSize, mode, chunkSize, chunkContigs)
- GeneAnnotTable: A table containing gene predictions associated with elevated or gapped regions in pattern-matches
Save the desired list item to a new variable using its associated name.
Metagenome results summary table:
MetagenomeCleanSummaryTable <- ProActiveOutputMetagenome$CleanSummaryTable| refName | classification | elevRatio | startPos | endPos | matchSize |
|---|---|---|---|---|---|
| NODE_1911 | Elevation | 3.349296 | 1000 | 17000 | 16000 |
| NODE_1583 | Elevation | 2.450013 | 42000 | 51000 | 9000 |
| NODE_1884 | Gap | 3.319514 | 36000 | 44000 | 8000 |
| NODE_1255 | Gap | 5.318072 | 1000 | 20000 | 19000 |
| NODE_368 | Gap | 2.690172 | 26000 | 56000 | 30000 |
| NODE_617 | Elevation | 1.784556 | 34000 | 82000 | 48000 |
Subset of genome results summary table:
GenomeCleanSummaryTable <- head(ProActiveOutputGenome$CleanSummaryTable)| refName | classification | elevRatio | startPos | endPos | matchSize | |
|---|---|---|---|---|---|---|
| 3 | NC_003197.2_chunk_3 | Gap | 1.609512 | 83000 | 100000 | 17000 |
| 4 | NC_003197.2_chunk_4 | Elevation | 1.351684 | 39000 | 51000 | 12000 |
| 7 | NC_003197.2_chunk_7 | Elevation | 1.343521 | 92000 | 100000 | 8000 |
| 8 | NC_003197.2_chunk_8 | Elevation | 1.723227 | 84000 | 94000 | 10000 |
| 10 | NC_003197.2_chunk_10 | Elevation | 1.887571 | 64000 | 100000 | 36000 |
| 12 | NC_003197.2_chunk_12 | Elevation | 1.293144 | 34000 | 44000 | 10000 |
Subset of GeneAnnotTable for metagenome results:
MetagenomeResultsGenePredictTable <- head(ProActiveOutputMetagenome$GeneAnnotTable)| seqid | source | type | start | end | score | strand | phase | attributes | geneproduct | Classification |
|---|---|---|---|---|---|---|---|---|---|---|
| NODE_1911 | Prodigal:002006 | CDS | 318 | 1166 | . |
|
0 | ID=NJKKNKEE_164175;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164175;product=hypothetical protein | hypothetical protein | Elevation |
| NODE_1911 | Prodigal:002006 | CDS | 1198 | 1938 | . |
|
0 | ID=NJKKNKEE_164176;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164176;product=hypothetical protein | hypothetical protein | Elevation |
| NODE_1911 | Prodigal:002006 | CDS | 1938 | 2582 | . |
|
0 | ID=NJKKNKEE_164177;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164177;product=hypothetical protein | hypothetical protein | Elevation |
| NODE_1911 | Prodigal:002006 | CDS | 2722 | 3561 | . |
|
0 | ID=NJKKNKEE_164178;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164178;product=hypothetical protein | hypothetical protein | Elevation |
| NODE_1911 | Prodigal:002006 | CDS | 3671 | 4063 | . |
|
0 | ID=NJKKNKEE_164179;inference=ab initio prediction:Prodigal:002006;locus_tag=NJKKNKEE_164179;product=hypothetical protein | hypothetical protein | Elevation |
| NODE_1911 | Prodigal:002006 | CDS | 4128 | 4670 | . |
|
0 | ID=NJKKNKEE_164180;eC_number=1.11.1.1;Name=rbr1_24;db_xref=COG:COG1592;gene=rbr1_24;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:Q97FZ9;locus_tag=NJKKNKEE_164180;product=Rubrerythrin-1 | Rubrerythrin-1 | Elevation |
Subset of GeneAnnotTable for genome results:
GenomeResultsGenePredictTable <- head(ProActiveOutputGenome$GeneAnnotTable)| seqid | source | type | start | end | score | strand | phase | attributes | geneproduct | Classification |
|---|---|---|---|---|---|---|---|---|---|---|
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 82564 | 84117 | . |
|
0 | ID=LFLNNMPD_00070;eC_number=6.2.1.48;Name=caiC;db_xref=COG:COG0318;gene=caiC;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P31552;locus_tag=LFLNNMPD_00070;product=Crotonobetaine/carnitine–CoA ligase | Crotonobetaine/carnitine–CoA ligase | Gap |
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 84180 | 85397 | . |
|
0 | ID=LFLNNMPD_00071;eC_number=2.8.3.21;Name=caiB;db_xref=COG:COG1804;gene=caiB;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P31572;locus_tag=LFLNNMPD_00071;product=L-carnitine CoA-transferase | L-carnitine CoA-transferase | Gap |
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 85509 | 86651 | . |
|
0 | ID=LFLNNMPD_00072;eC_number=1.3.8.13;Name=caiA_1;db_xref=COG:COG1960;gene=caiA_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P60584;locus_tag=LFLNNMPD_00072;product=Crotonobetainyl-CoA reductase | Crotonobetainyl-CoA reductase | Gap |
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 86686 | 88203 | . |
|
0 | ID=LFLNNMPD_00073;Name=caiT;db_xref=COG:COG1292;gene=caiT;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P31553;locus_tag=LFLNNMPD_00073;product=L-carnitine/gamma-butyrobetaine antiporter | L-carnitine/gamma-butyrobetaine antiporter | Gap |
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 88684 | 89454 | . |
|
0 | ID=LFLNNMPD_00074;Name=fixA_1;db_xref=COG:COG2086;gene=fixA_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P60566;locus_tag=LFLNNMPD_00074;product=Protein FixA | Protein FixA | Gap |
| NC_003197.2_chunk_3 | Prodigal:002006 | CDS | 89470 | 90411 | . |
|
0 | ID=LFLNNMPD_00075;Name=fixB_1;db_xref=COG:COG2025;gene=fixB_1;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P31574;locus_tag=LFLNNMPD_00075;product=Protein FixB | Protein FixB | Gap |
plotProActiveResults()
plotProActiveResults() allows users to visualize both
the read coverage and the pattern-match associated with each gap or
elevation classification.
Function components
Re-building pattern-matches
The ProActive() output contains information needed to
re-build each pattern-match used for classification. To re-build a
complete pattern-match for visualization,
plotProActiveResults() uses the pattern-match’s minimum and
maximum values and the start and stop positions.
Plotting read coverage and associated pattern-matches
By default, the read coverage is plotted for each contig/chunk
classified as having a gap or elevation in read coverage. If you wish to
see the read coverage for noPattern classifications, be sure to set
IncludeNoPatterns = TRUE when running
ProActive(). The pattern-match associated with each
classification is overlaid on the coverage plot.
Usage
Default arguments:
MetagenomeResultsPlots <- plotProActiveResults(
pileup = sampleMetagenomePileup,
ProActiveResults = ProActiveOutputMetagenome
)
GenomeResultsPlots <- plotProActiveResults(
pileup = sampleGenomePileup,
ProActiveResults = ProActiveOutputGenome
)Note- There is no need to set ‘genome’ or
‘metagenome’ mode. plotProActiveResults() will get this
information from the ProActive() output.
Arguments/parameters
plotProActiveResults(pileup,
ProActiveResults,
elevFilter,
saveFilesTo
)-
pileup: A .txt file containing mapped sequencing read coverages averaged over 100 bp windows/bins. -
ProActiveResults: The output fromProActive(). -
elevFilter: Optional, only plot results with pattern-matches that achieved an elevation ratio (max/min) greater than the specified value. Default is no filter. -
saveFilesTo: Optional, Provide a path to the directory you wish to save output to. A folder will be made within the provided directory to store results.
Session Information
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] dplyr_1.1.4 stringr_1.5.1 ggplot2_3.5.1 kableExtra_1.4.0
#> [5] ProActive_0.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 jsonlite_1.8.9 compiler_4.4.2 tidyselect_1.2.1
#> [5] xml2_1.3.6 jquerylib_0.1.4 systemfonts_1.1.0 scales_1.3.0
#> [9] textshaping_0.4.1 yaml_2.3.10 fastmap_1.2.0 R6_2.5.1
#> [13] labeling_0.4.3 generics_0.1.3 knitr_1.49 tibble_3.2.1
#> [17] desc_1.4.3 munsell_0.5.1 svglite_2.1.3 bslib_0.8.0
#> [21] pillar_1.10.1 rlang_1.1.4 cachem_1.1.0 stringi_1.8.4
#> [25] xfun_0.50 fs_1.6.5 sass_0.4.9 viridisLite_0.4.2
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