Package: MICSQTL 1.11.0

Qian Li

MICSQTL: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)

Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.

Authors:Yue Pan [aut], Qian Li [aut, cre], Iain Carmichael [ctb]

MICSQTL_1.11.0.tar.gz
MICSQTL_1.11.0.zip(r-4.7)MICSQTL_1.11.0.zip(r-4.6)MICSQTL_1.11.0.zip(r-4.5)
MICSQTL_1.11.0.tgz(r-4.6-any)MICSQTL_1.11.0.tgz(r-4.5-any)
MICSQTL_1.11.0.tar.gz(r-4.7-any)MICSQTL_1.11.0.tar.gz(r-4.6-any)
MICSQTL_1.11.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MICSQTL/json (API)

# Install 'MICSQTL' in R:

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

Bug tracker:https://github.com/yuepan027/micsqtl/issues

Datasets:

se - Example data

On BioConductor:MICSQTL-1.11.0(bioc 3.24)MICSQTL-1.10.0(bioc 3.23)

geneexpression genetics proteomics rnaseq sequencing singlecell software visualization cellbasedassays coverage

4.00 score 4 scripts 4 exports 155 dependencies

Last updated from:03fcc72bd3. Checks:1 NOTE, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	206
linux-devel-x86_64	OK	474
source / vignettes	OK	284
linux-release-x86_64	OK	465
macos-release-arm64	OK	363
macos-oldrel-arm64	OK	330
windows-devel	OK	478
windows-release	OK	522
windows-oldrel	OK	545
wasm-release	OK	221

Exports:ajive_decomp csQTL deconv feature_filter

Dependencies:abind annotate AnnotationDbi askpass backports BH Biobase BiocGenerics BiocParallel Biostrings bit bit64 blob boot broom cachem car carData class cli codetools colorspace config corpcor corrplot cowplot cpp11 crayon curl data.table DBI DelayedArray Deriv dirmult doBy doParallel dplyr e1071 EpiDISH farver fastmap forcats foreach forecast formatR Formula fracdiff futile.logger futile.options gdata genefilter generics GenomicRanges GGally ggplot2 ggpubr ggrepel ggridges ggsci ggsignif ggstats glue gmodels gridExtra gtable gtools hms httr IRanges isoband iterators jsonlite KEGGREST labeling lambda.r lattice lifecycle limma lme4 lmtest locfdr magrittr MASS Matrix matrixcalc MatrixGenerics MatrixModels matrixStats memoise mgcv mime minqa modelr nlme nloptr nnet nnls numDeriv openssl patchwork pbapply pbkrtest pillar pkgconfig png polynom pracma prettyunits progress proxy purrr quadprog quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang RSQLite rstatix rsvd S4Arrays S4Vectors S7 scales Seqinfo snow SparseArray SparseM statmod stringi stringr SummarizedExperiment survival sys TCA tibble tidyr tidyselect timeDate TOAST urca utf8 vctrs viridisLite withr XML xtable XVector yaml zoo

MICSQTL: Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci

Rendered fromMICSQTL.Rmdusingknitr::rmarkdown

Last update: 2024-03-08
Started: 2023-04-21

Help page	Topics
Integrative analysis for modes of joint variation	ajive_decomp
Cell-type-specific differential expression (csQTL)	csQTL
Estimation of cellular composition in high-throughput data from heterogeneous tissues	deconv
Feature filtering	feature_filter
Example data	se

Package: MICSQTL 1.11.0

MICSQTL: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative Trait Loci)

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