PALMO (Platform for Analyzing Longitudinal Multi-omics data)
Suhas Vasaikar, PhD
Last compiled on 17 August, 2022
library(PALMO)
#> Loading required package: gridIntroduction
PALMO
(Platform for Analyzing Longitudinal Multi-omics data) is a
platform for anayzing longitudinal data from bulk as well as single
cell. It allows to identify inter-, intra-donor variations in genes over
longitudinal time points. The analysis can be done on bulk expression
dataset without known celltype information or single cell with
celltype/user-defined groups. It allows to infer stable and variable
features in given donor and each celltype (or user defined group). The
outlier analysis can be performed to identify technical/biological
perturbed samples in donor/participant. Further, differential analysis
can be performed to decipher time-wise changes in gene expression in a
celltype.
General
workflow and analysis schema of PALMO. It can work with
longitudinal data obtained from bulk such as clinical, bulk RNAseq,
proteomic or single cell dataset from scRNAseq, and scATACseq.
Install package and load library
To install library, simply run
library("devtools")
install_github("aifimmunology/PALMO")
library("PALMO")Tutorials
There are couple of tutorials presented to help users to run PALMO on bulk and single cell data. The tutorials can be found at [https://github.com/aifimmunology/PALMO/blob/main/ReferenceManual-PALMO-v0.1.1.pdf]. The examples includes:
- Tutorial-1: Plasma proteome longitudinal data
- Tutorial-2: scRNA longitudinal data
- Tutorial-3: scATAC longitudinal data
- Tutorial-4: Multi-modal data integration
- Tutorial-5: Longitudinal COVID dataset (CNP0001102)
- Tutorial-6: Differential Gene analysis in longitudinal data
- Tutorial-7: Mouse brain dataset (GSE129788)
- Tutorial-8: TCRB profiling dataset(GSE156980)
License
PALM is licensed under the MIT-License.
Session info
sessionInfo()
#> R version 4.0.3 (2020-10-10)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] PALMO_0.1.2
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.3.1 backports_1.2.0
#> [3] circlize_0.4.11 plyr_1.8.6
#> [5] igraph_1.2.8 lazyeval_0.2.2
#> [7] splines_4.0.3 listenv_0.8.0
#> [9] scattermore_0.7 GenomeInfoDb_1.24.2
#> [11] ggplot2_3.3.5 digest_0.6.28
#> [13] htmltools_0.5.2 fansi_0.5.0
#> [15] magrittr_2.0.1 tensor_1.5
#> [17] cluster_2.1.0 ROCR_1.0-11
#> [19] readr_1.4.0 ComplexHeatmap_2.4.3
#> [21] globals_0.14.0 modelr_0.1.8
#> [23] matrixStats_0.61.0 colorspace_2.0-2
#> [25] rvest_0.3.6 blob_1.2.1
#> [27] ggrepel_0.9.1 haven_2.3.1
#> [29] xfun_0.30 dplyr_1.0.7
#> [31] RCurl_1.98-1.2 crayon_1.4.2
#> [33] jsonlite_1.7.2 lme4_1.1-25
#> [35] spatstat_1.64-1 spatstat.data_2.1-0
#> [37] survival_3.2-7 zoo_1.8-9
#> [39] glue_1.6.2 polyclip_1.10-0
#> [41] gtable_0.3.0 zlibbioc_1.34.0
#> [43] XVector_0.28.0 leiden_0.3.9
#> [45] DelayedArray_0.14.1 GetoptLong_1.0.4
#> [47] SingleCellExperiment_1.10.1 future.apply_1.8.1
#> [49] shape_1.4.5 BiocGenerics_0.34.0
#> [51] abind_1.4-5 scales_1.1.1
#> [53] pheatmap_1.0.12 DBI_1.1.0
#> [55] miniUI_0.1.1.1 Rcpp_1.0.7
#> [57] viridisLite_0.4.0 xtable_1.8-4
#> [59] clue_0.3-57 reticulate_1.22
#> [61] stats4_4.0.3 htmlwidgets_1.5.4
#> [63] httr_1.4.2 RColorBrewer_1.1-2
#> [65] ellipsis_0.3.2 Seurat_4.0.0
#> [67] factoextra_1.0.7.999 ica_1.0-2
#> [69] farver_2.1.0 pkgconfig_2.0.3
#> [71] sass_0.4.0 uwot_0.1.10
#> [73] dbplyr_1.4.4 deldir_1.0-6
#> [75] utf8_1.2.2 tidyselect_1.1.1
#> [77] rlang_1.0.2 reshape2_1.4.4
#> [79] later_1.3.0 cellranger_1.1.0
#> [81] munsell_0.5.0 tools_4.0.3
#> [83] cli_3.2.0 generics_0.1.3
#> [85] broom_0.7.2 ggridges_0.5.3
#> [87] evaluate_0.15 stringr_1.4.0
#> [89] fastmap_1.1.0 yaml_2.2.1
#> [91] goftest_1.2-3 fs_1.5.0
#> [93] knitr_1.39 fitdistrplus_1.1-6
#> [95] purrr_0.3.4 RANN_2.6.1
#> [97] pbapply_1.5-0 future_1.23.0
#> [99] nlme_3.1-149 mime_0.12
#> [101] xml2_1.3.2 compiler_4.0.3
#> [103] rstudioapi_0.13 plotly_4.10.0
#> [105] png_0.1-7 spatstat.utils_2.2-0
#> [107] reprex_0.3.0 tweenr_1.0.1
#> [109] tibble_3.1.6 statmod_1.4.35
#> [111] bslib_0.3.1 stringi_1.7.5
#> [113] forcats_0.5.0 lattice_0.20-41
#> [115] Matrix_1.3-4 nloptr_1.2.2.2
#> [117] vctrs_0.4.0 pillar_1.7.0
#> [119] lifecycle_1.0.1 lmtest_0.9-39
#> [121] jquerylib_0.1.4 GlobalOptions_0.1.2
#> [123] RcppAnnoy_0.0.19 bitops_1.0-7
#> [125] data.table_1.14.2 cowplot_1.1.1
#> [127] irlba_2.3.3 GenomicRanges_1.40.0
#> [129] httpuv_1.6.3 patchwork_1.1.1
#> [131] R6_2.5.1 promises_1.2.0.1
#> [133] KernSmooth_2.23-17 gridExtra_2.3
#> [135] IRanges_2.22.2 parallelly_1.28.1
#> [137] codetools_0.2-16 boot_1.3-25
#> [139] MASS_7.3-53 assertthat_0.2.1
#> [141] SummarizedExperiment_1.18.2 MAST_1.14.0
#> [143] rjson_0.2.20 SeuratObject_4.0.2
#> [145] sctransform_0.3.2 GenomeInfoDbData_1.2.3
#> [147] S4Vectors_0.26.1 mgcv_1.8-33
#> [149] parallel_4.0.3 hms_0.5.3
#> [151] rpart_4.1-15 tidyverse_1.3.0
#> [153] tidyr_1.1.4 minqa_1.2.4
#> [155] rmarkdown_2.15 Rtsne_0.15
#> [157] ggforce_0.3.2 Biobase_2.48.0
#> [159] lubridate_1.7.9 shiny_1.7.1