mikelove

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

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set.seed(5)

n <- 1000
reps <- 10
rna <- matrix(
  rnbinom(n * reps, mu = 10, size = 100),
  ncol=reps
)
dna <- matrix(
  rnbinom(n * reps, mu = 10, size = 100),

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# dataframes vs lm S3 vs Bioc S4
# Michael Love
# Nov 1 2023

dat <- data.frame(genotype=c("wt","wt","mut","mut"),
                  count=c(10,20,30,40),
                  score=c(-1.2,0,3.4,-5),
                  gene=c("Abc","Abc","Xyz","Xyz"))
library(tibble)
dat |> as_tibble()

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---
title: "Toy tree example for collapsing"
author: "Michael Love"
---
  
Example data with 20 inferential replicates, here we just have 1
sample per condition and we calculate the LFC at each level of the
tree.

From the below simulation setup (see first chunk), the true DE signal

mikelove

library(plyranges)
library(readr)

bindata <- read_tsv("bindata.40000.hg19.tsv.gz")

fire <- read_bed("fire-adult-hg19.txt")

# not necessary, but nice to have
si <- Seqinfo(genome="hg19")
si <- keepStandardChromosomes(si)

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# minimap2 map reads:
minimap2 -t {threads} -a -x map-ont -p 1.0 -N 100 {input.index} {input.fastq} | samtools view -Sb > {output.bam}
# salmon Quant:
salmon quant --ont --noLengthCorrection -p 8 -t {input.transcriptome} -l U -a {input.bam} -o {output}

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library(palmerpenguins) # penguins!
library(ggplot2) # "grammar of graphics" plots
suppressPackageStartupMessages(
  library(dplyr) # data pliers
)

penguins |> 
  slice(1:3) |>
  select(species, island)

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Index CHR,Index SNP,BP Left,BP Right,# Conditional independent,Index BP,Index P value,Merged Indexes,2nd SNP,2nd BP,2nd P value,3rd SNP,3rd BP,3rd P value,4th SNP,4th BP,4th P value,5th SNP,5th BP,5th P value,,,,,,,,
6,rs13195636,24939493,30559493,4,27509493,3.33E-40,rs13195636,rs9461856,33395199,4.13E-14,rs8192589,32187637,2.27E-10,rs9368789,34001678,2.03E-07,rs10946808,26233387,1.11E-06,,,,,,,,
18,rs9636107,52666708,53680258,4,53200117,1.92E-13,"rs12969453,rs74914300,rs72934602,rs9636107",rs12969453,52751708,3.93E-11,rs78322266,53063676,6.89E-08,rs72934602,53568458,6.79E-07,rs9952704,53447690,2.60E-04,,,,,,,,
3,rs2710323,48132866,53603354,3,52815905,1.02E-21,"rs11715134,rs11917680,rs2710323,rs13080668",rs2236989,50505395,1.81E-10,rs7633840,48719638,4.41E-07,rs4687724,53408177,1.10E-04,,,,,,,,,,,
18,rs72980087,77324421,77981194,2,77632194,4.80E-16,"rs56040937,rs7238071,rs72980087,rs72970145",rs11664298,77578986,5.24E-16,rs12455965,77688830,0.0001109,,,,,,,,,,,,,,
3,rs13090130,161092491,161969035,2,161777035,

mikelove

library(plyranges)
x <- data.frame(seqnames=1,
                start=sample(1000,10,FALSE),
                width=1, id=1:10) %>%
  as_granges()
y <- data.frame(seqnames=1,
                start=sample(1000,10,FALSE),
                width=1, id=letters[1:10]) %>%
  as_granges()

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library(plyranges)
library(microbenchmark)
library(dplyr)
library(tibble)

make_rand_gr <- function(N, grps) {
  data.frame(seqnames = sample(c("seq1", "seq2", "seq3"), N, replace = TRUE), 
    strand = sample(c("+", "-", "*"), N, replace = TRUE), start = rpois(N, 
      N), width = rpois(N, N), grps = sample(grps, N, replace = TRUE), 
    score = runif(N)) %>% as_granges()