mikelove

> x <- data.frame(seqnames="chr1", start=c(1,101), width=100, foo=c("bar","boo")) |> as_granges()
> x
GRanges object with 2 ranges and 1 metadata column:
      seqnames    ranges strand |         foo
         <Rle> <IRanges>  <Rle> | <character>
  [1]     chr1     1-100      * |         bar
  [2]     chr1   101-200      * |         boo
  -------
  seqinfo: 1 sequence from an unspecified genome; no seqlengths
> x |> tile_ranges(25)

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# Created by chat GPT

# Load necessary libraries
library(splines)
library(GPfit)

# Set seed for reproducibility
set.seed(123)

# Generate non-uniform data

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load_all("../biocmask")

suppressPackageStartupMessages(library(airway))
data(airway)
rowData(airway)$group <- sort(rep(1:10, length=nrow(airway)))

library(tibble)
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(purrr))
airway |>

mikelove

library(jsonlite)
library(readr)
dat <- read_csv("IGVF MPRA FG designed sequence metadata - Sheet1.csv")
dat$constraints <- as.list(dat$constraints)
dat$constraints[[1]] <- list(required=TRUE)
dat$constraints[[2]] <- list(required=TRUE)
dat$constraints[[3]] <- list(
  required=TRUE,
  enum=strsplit(sub(".*enum: \\[(.*)\\]","\\1",dat$constraints[[3]]),", ")[[1]]
)

mikelove

---
title: "Differential chromatin accessibility and gene expression"
format: html
---

# Differential expression from RNA-seq

```{r}
#| eval: FALSE
dir <- system.file("extdata", package="macrophage")

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mikelove

library(plyranges)
library(nullranges)

x <- data.frame(
  seqnames=rep(c("1","2","3"), each=10),
  start=rep(c(0:4, 10:14) * 1000 + 1, times=3),
  width=100) |>
  as_granges()

seqlengths(x) <- c("1"=20123, "2"=20123, "3"=20123)

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library(SummarizedExperiment)
library(plyranges)

# example data
m <- matrix(rnorm(600), nrow=100)
r1 <- data.frame(seqnames=1, start=1:50 * 100 + 2501,
                 width=5, id1=paste0("u",formatC(1:50,width=3,flag="0"))) |>
  as_granges()
r2 <- data.frame(seqnames=1, start=1:100 * 100 + 1,
                 width=5, id2=paste0("v",formatC(1:100,width=3,flag="0"))) |>

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library(shiny)
library(UpSetR)
library(dplyr)
library(tidyr)
library(readr)
library(ggplot2)
library(pheatmap)
dat <- read_delim("ancestry_dataframe.tsv")

ui <- fluidPage(

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mat <- matrix(rnbinom(2e5, mu=100, size=1/.01), ncol=100)

library(DESeq2)

d <- DESeqDataSetFromMatrix(mat, DataFrame(x=rep(1,100)), ~1)
# library size correction, centered log ratio to reference sample
d <- estimateSizeFactors(d)
# variance
d <- estimateDispersionsGeneEst(d)
# trend