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‎R/geneProgramSignificance.R

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#' @param pt A vector of pseudotime values for each cell. May contain NAs, which are handled internally. Defaults to NULL.
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#' @param program.labels (Optional) A character vector specifying a label for each gene cluster. Defaults to NULL.
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#' @param p.adj.method (Optional) The method used to adjust \emph{p}-values for multiple hypothesis testing. Defaults to "holm".
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#' @details
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#' \itemize{
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#' \item This function assumes that the gene program scores have been min-max normalized to (0, 1) i.e., \emph{not} including the values 0 or 1. This is necessary to fit the Beta distribution additive model. This normalization can be easily generated by setting the argument \code{minmax.norm = TRUE} in the \code{\link{geneProgramScoring}} function.
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#' }
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#' @return A table of statistical output showing the significance of the association between pseudotime and program scores.
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#' @seealso \code{\link{geneProgramScoring}}
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#' @export

‎R/theme_scLANE.R

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@@ -31,7 +31,8 @@ theme_scLANE <- function(base.size = 12,
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base_line_size = base.lwd,
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base_rect_size = base.lwd) +
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ggplot2::theme(strip.clip = "off",
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strip.background = ggplot2::element_rect(linewidth = base.lwd))
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strip.background = ggplot2::element_rect(linewidth = base.lwd),
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axis.line = ggplot2::element_line(lineend = "square"))
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if (umap) {
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scLANE_theme <- scLANE_theme +
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ggplot2::theme(axis.ticks = ggplot2::element_blank(),

‎README.Rmd

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![last commit](https://img.shields.io/github/last-commit/jr-leary7/scLANE/main?color=darkgreen)
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[![codecov](https://codecov.io/gh/jr-leary7/scLANE/branch/main/graph/badge.svg?token=U2U5RTF2VW)](https://codecov.io/gh/jr-leary7/scLANE)
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[![CodeFactor](https://www.codefactor.io/repository/github/jr-leary7/sclane/badge)](https://www.codefactor.io/repository/github/jr-leary7/sclane)
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[![DOI](https://img.shields.io/static/v1?label=DOI&message=10.5281/zenodo.10182497&color=blue)](https://doi.org/10.5281/zenodo.10182497)
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[![DOI](https://img.shields.io/static/v1?label=DOI&message=10.1101/2023.12.19.572477&color=blue)](https://doi.org/10.1101/2023.12.19.572477)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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<!-- badges: end -->
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pt = order_df,
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genes = gene_sample,
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size.factor.offset = cell_offset,
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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```
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is.gee = TRUE,
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id.vec = sim_data$subject,
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cor.structure = "ar1",
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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```
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size.factor.offset = cell_offset,
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n.potential.basis.fns = 3,
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is.glmm = TRUE,
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glmm.adaptive = TRUE,
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id.vec = sim_data$subject,
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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```
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‎README.md

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commit](https://img.shields.io/github/last-commit/jr-leary7/scLANE/main?color=darkgreen)
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[![codecov](https://codecov.io/gh/jr-leary7/scLANE/branch/main/graph/badge.svg?token=U2U5RTF2VW)](https://codecov.io/gh/jr-leary7/scLANE)
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[![CodeFactor](https://www.codefactor.io/repository/github/jr-leary7/sclane/badge)](https://www.codefactor.io/repository/github/jr-leary7/sclane)
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[![DOI](https://img.shields.io/static/v1?label=DOI&message=10.5281/zenodo.10182497&color=blue)](https://doi.org/10.5281/zenodo.10182497)
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[![DOI](https://img.shields.io/static/v1?label=DOI&message=10.1101/2023.12.19.572477&color=blue)](https://doi.org/10.1101/2023.12.19.572477)
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[![License:
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MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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<!-- badges: end -->
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pt = order_df,
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genes = gene_sample,
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size.factor.offset = cell_offset,
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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#> Registered S3 method overwritten by 'bit':
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#> method from
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#> print.ri gamlss
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#> scLANE testing completed for 100 genes across 1 lineage in 45.702 secs
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#> scLANE testing completed for 100 genes across 1 lineage in 35.37 secs
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```
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After the function finishes running, we use `getResultsDE()` to generate
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col.names = c("Gene", "Lineage", "LRT stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))
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```
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| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
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|:---------|:--------|----------:|--------:|-------------:|-------------------------:|
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| MFSD2B | A | 216.750 | 0.000 | 0.000 | 1 |
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| RPL29 | A | 5.632 | 0.018 | 0.353 | 0 |
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| UAP1L1 | A | 9.880 | 0.007 | 0.157 | 0 |
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| TMCO3 | A | 167.709 | 0.000 | 0.000 | 1 |
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| GOLGA8EP | A | 4.359 | 0.037 | 0.487 | 0 |
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| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
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|:-----------|:--------|----------:|--------:|-------------:|-------------------------:|
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| RAB1B | A | 219.950 | 0.000 | 0.000 | 1 |
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| LY75.CD302 | A | 4.858 | 0.028 | 0.541 | 0 |
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| UAP1L1 | A | 9.894 | 0.007 | 0.163 | 0 |
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| TMCO3 | A | 167.311 | 0.000 | 0.000 | 1 |
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| GOLGA8EP | A | 4.201 | 0.040 | 0.567 | 0 |
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### GEE mode
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is.gee = TRUE,
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id.vec = sim_data$subject,
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cor.structure = "ar1",
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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#> scLANE testing completed for 100 genes across 1 lineage in 2.201 mins
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#> scLANE testing completed for 100 genes across 1 lineage in 1.525 mins
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```
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We again generate the table of DE test results. The variance of the
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| Gene | Lineage | Wald stat. | P-value | Adj. p-value | Predicted dynamic status |
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|:---------|:--------|-----------:|--------:|-------------:|-------------------------:|
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| DGUOK | A | 64351.893 | 0 | 0 | 1 |
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| TBCC | A | 32.151 | 0 | 0 | 1 |
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| DGUOK | A | 200675.460 | 0 | 0 | 1 |
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| TBCC | A | 40.399 | 0 | 0 | 1 |
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| GOLGA8EP | A | NA | NA | NA | 0 |
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| TMC6 | A | 5052.168 | 0 | 0 | 1 |
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| JARID2 | A | 1512.240 | 0 | 0 | 1 |
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| EMC3 | A | 8397.337 | 0 | 0 | 1 |
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| DDX41 | A | 3486.998 | 0 | 0 | 1 |
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### GLMM mode
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size.factor.offset = cell_offset,
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n.potential.basis.fns = 3,
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is.glmm = TRUE,
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glmm.adaptive = TRUE,
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id.vec = sim_data$subject,
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n.cores = 4,
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n.cores = 4L,
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verbose = FALSE)
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#> scLANE testing completed for 100 genes across 1 lineage in 2.968 mins
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#> scLANE testing completed for 100 genes across 1 lineage in 3.133 mins
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```
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**Note:** The GLMM mode is still under development, as we are working on
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col.names = c("Gene", "Lineage", "LRT stat.", "P-value", "Adj. p-value", "Predicted dynamic status"))
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```
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| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
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|:--------|:--------|----------:|--------:|-------------:|-------------------------:|
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| DDX1 | A | 132.422 | 0.000 | 0 | 1 |
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| GGNBP2 | A | 73.683 | 0.000 | 0 | 1 |
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| WDSUB1 | A | NA | NA | NA | 0 |
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| FAM135B | A | NA | NA | NA | 0 |
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| DAB1 | A | 9.217 | 0.684 | 1 | 0 |
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| Gene | Lineage | LRT stat. | P-value | Adj. p-value | Predicted dynamic status |
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|:-------|:--------|----------:|--------:|-------------:|-------------------------:|
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| VDAC1 | A | 205.644 | 0.000 | 0 | 1 |
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| CKAP4 | A | 139.246 | 0.000 | 0 | 1 |
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| MRTO4 | A | 5.048 | 0.998 | 1 | 0 |
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| FOXD3 | A | 2.586 | 1.000 | 1 | 0 |
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| UBE2Q1 | A | 94.077 | 0.000 | 0 | 1 |
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## Downstream analysis & visualization
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‎man/figures/README-plot-knot-dist-1.png

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‎man/figures/README-plot-models-glm-1.png

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‎man/figures/README-plot-models-glmm-1.png

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‎man/geneProgramSignificance.Rd

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