From f6a9f5aa93889c2b95547fd7317c0bdafa5de628 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Thu, 31 Oct 2024 13:26:52 -0400 Subject: [PATCH] Fix naming, and better example Rmd --- inst/templates/base/reports/example.Rmd | 33 +++++++++++++++++++ inst/templates/chipseq/readme.md | 2 +- inst/templates/rnaseq/README.md | 2 +- ...tion_template.rmd => norm_integration.rmd} | 0 inst/templates/singlecell/README.md | 8 +++-- 5 files changed, 40 insertions(+), 5 deletions(-) rename inst/templates/singlecell/Integration/{scRNA_normalization_template.rmd => norm_integration.rmd} (100%) diff --git a/inst/templates/base/reports/example.Rmd b/inst/templates/base/reports/example.Rmd index e771265..772e8dd 100644 --- a/inst/templates/base/reports/example.Rmd +++ b/inst/templates/base/reports/example.Rmd @@ -37,6 +37,39 @@ This code is in this ![](https://img.shields.io/badge/status-stable-green) revis source(params$project_file) ``` +```{r} +knitr::opts_chunk$set(echo = TRUE) +# Load libraries +library(knitr) +library(rmarkdown) +library(DT) +library(ggprism) +library(grafify) + +ggplot2::theme_set(theme_prism(base_size = 12)) +# https://grafify-vignettes.netlify.app/colour_palettes.html +# NOTE change colors here if you wish +scale_colour_discrete <- function(...) + scale_colour_manual(..., values = as.vector(grafify:::graf_palettes[["kelly"]])) + +#options(ggplot2.discrete.colour= ) + +# Set seed for reproducibility +set.seed(1454944673L) +opts_chunk[["set"]]( + audodep = TRUE, + cache = FALSE, + cache.lazy = FALSE, + error = TRUE, + echo = TRUE, + fig.height = 5L, + fig.retina = 2L, + fig.width = 9.6, + message = FALSE, + tidy = TRUE, + warning = TRUE) +``` + # Overview - Project: `r project` diff --git a/inst/templates/chipseq/readme.md b/inst/templates/chipseq/readme.md index c1a53d1..645b56a 100755 --- a/inst/templates/chipseq/readme.md +++ b/inst/templates/chipseq/readme.md @@ -1,4 +1,4 @@ -# Guidelines for analysis +# Guidelines for ChIPSeq analysis Make sure there is a valid project name, and modify `information.R` with the right information for your project. You can use this file with any other Rmd to include the project/analysis information. diff --git a/inst/templates/rnaseq/README.md b/inst/templates/rnaseq/README.md index 114d2bf..9714054 100644 --- a/inst/templates/rnaseq/README.md +++ b/inst/templates/rnaseq/README.md @@ -1,6 +1,6 @@ # Guideline for RNAseq downstream analysis -Make sure there is a project name for this. +Make sure there is a valid project name, and modify `information.R` with the right information for your project. You can use this file with any other Rmd to include the project/analysis information. ## Run data with nf-core rnaseq diff --git a/inst/templates/singlecell/Integration/scRNA_normalization_template.rmd b/inst/templates/singlecell/Integration/norm_integration.rmd similarity index 100% rename from inst/templates/singlecell/Integration/scRNA_normalization_template.rmd rename to inst/templates/singlecell/Integration/norm_integration.rmd diff --git a/inst/templates/singlecell/README.md b/inst/templates/singlecell/README.md index 5c7dc35..c950a4e 100644 --- a/inst/templates/singlecell/README.md +++ b/inst/templates/singlecell/README.md @@ -1,6 +1,8 @@ -# Project name +# Guideline for scRNAseq analysis -# Start with cell-ranger +Make sure there is a valid project name, and modify `information.R` with the right information for your project. You can use this file with any other Rmd to include the project/analysis information. + +# cell-ranger `pre-process-w-cellranger.md` contains step by step guidelines on how to run cellranger and load data into R. This `scripts/seurat_init.R` script contains all the pieces to go from cellranger output to Seurat obj. It is assuming a mouse genome. @@ -10,5 +12,5 @@ Currently we are working on deploying a shiny app to inspect the single cell obj # Integration -Currently we are working on guidelines and templates for this step. There is some draft under *Integration** folder. +`Integration/norm_integration.rmd` is a template with guidelines on how to work with multiple samples. It compares log2norm vs SCT, work with SCT by samples to remove batch biases better, provide options for integration between CCA and Harmony. As last step, it contains cell type clustering and visualization to help decide the best parameters.