Package: blacksheepr 1.27.0

RugglesLab

blacksheepr: Outlier Analysis for pairwise differential comparison

Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare.

Authors:MacIntosh Cornwell [aut], RugglesLab [cre]

blacksheepr_1.27.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
blacksheepr/json (API)

# Install 'blacksheepr' in R:

install.packages('blacksheepr', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ruggleslab/blacksheepr/issues

Datasets:

sample_annotationdata - Sample_annotationdata
sample_phosphodata - Sample_phosphodata
sample_rnadata - Sample_rnadata

On BioConductor:blacksheepr-1.27.0(bioc 3.24)blacksheepr-1.26.0(bioc 3.23)

sequencing rnaseq geneexpression transcription differentialexpression transcriptomics

4.34 score 11 scripts 11 exports 115 dependencies

Last updated from:3390f3ce2f. Checks:8 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	220
linux-devel-x86_64	NOTE	432
source / vignettes	OK	361
linux-release-x86_64	NOTE	380
macos-release-arm64	NOTE	290
macos-oldrel-arm64	NOTE	208
windows-devel	NOTE	1227
windows-release	NOTE	1262
windows-oldrel	NOTE	1122
wasm-release	OK	223

Exports:annotationlist_builder comparison_groupings count_outliers create_heatmap deva deva_normalization deva_results make_comparison_columns make_outlier_table outlier_analysis outlier_heatmap

Dependencies:abind annotate AnnotationDbi askpass BH Biobase BiocFileCache BiocGenerics BiocParallel biomaRt Biostrings bit bit64 bitops blob cachem circlize cli clue cluster codetools colorspace ComplexHeatmap cpp11 crayon curl DBI dbplyr DelayedArray DESeq2 DEXSeq digest doParallel dplyr farver fastmap filelock foreach formatR futile.logger futile.options genefilter geneplotter generics GenomicRanges GetoptLong ggplot2 GlobalOptions glue gridExtra gtable hms httr httr2 hwriter IRanges isoband iterators jsonlite KEGGREST labeling lambda.r lattice lifecycle locfit magrittr Matrix MatrixGenerics matrixStats memoise mime openssl pasilla pillar pkgconfig png prettyunits progress purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo Rhtslib rjson rlang Rsamtools RSQLite S4Arrays S4Vectors S7 scales Seqinfo shape snow SparseArray statmod stringi stringr SummarizedExperiment survival sys tibble tidyr tidyselect utf8 vctrs viridis viridisLite withr XML xml2 xtable XVector

Outlier Analysis using blacksheepr

Rendered fromblacksheepr_vignette.Rmdusingknitr::rmarkdown

Last update: 2019-10-11
Started: 2019-10-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Create the annotation object for plotting in a heatmap	annotationlist_builder
Create all of the groups based on the input metadata	comparison_groupings
Count up the outlier information for each of the groups you have made. If aggregating then you will have to turn the parameter on, but you still input the outliertable. Aggregate will count the total number of outliers AND nonoutliers in its operation, so it needs the original outlier table made by the <make_outlier_table> function.	count_outliers
Plot out a heatmap	create_heatmap
Run the entire blacksheep Function from Start to finish	deva
Normalization of data to prepare for deva. Uses a Median of Ratio method followed by a log2 transformation.	deva_normalization
Utility function that allows easier grabbing of data	deva_results
Utility function that will take in columns with several subcategories, and output several columns each with binary classifications. ex) col1: A,B,C >> colA: A,notA,notA; colB: notB,B,notB; colC: notC,notC,C	make_comparison_columns
Separate out the "i"th gene, take the bounds, and then create a column that says whether or not this gene is high, low, or none in a sample with regards to the other samples in the dataset. Repeat this for every gene to create a reference table.	make_outlier_table
With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons	outlier_analysis
With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons	outlier_heatmap
sample_annotationdata	sample_annotationdata
sample_phosphodata	sample_phosphodata
sample_rnadata	sample_rnadata