kbroman

# smooth genetic map by mixing it with a bit of constant recombination (separate rec rate for each chromosome)
# (the input gmap and pmap are lists of vectors of marker positions)
smooth_gmap <- 
    function(gmap, pmap, alpha=0.02)
    {
        stopifnot(is.list(gmap), is.list(pmap))
        stopifnot(length(gmap)==length(pmap), all(names(gmap) == names(pmap)))
        stopifnot(length(alpha)==1, alpha >= 0, alpha <= 1)
        for(chr in seq(along=gmap)) {
            g <- gmap[[chr]]

kbroman

import re
import requests
from requests_html import HTMLSession
import pandas as pd
import matplotlib.pyplot as plt

session = HTMLSession()

r = session.get('https://covidresponse.wisc.edu/dashboard/')

kbroman

# guess genotypes from allele dosages
#
# alleleprob = n_ind x n_alleles matrix of allele dosages (rows sum to 1)
# epsilon = tolerance value

guess_geno_from_alleleprob <-
    function(alleleprob, epsilon=0.01)
    {
        stopifnot(is.matrix(alleleprob)) # expect ind x allele matrix
        stopifnot(is.numeric(epsilon), length(epsilon)==1, epsilon >= 0, epsilon <= 1)

kbroman

# plot strain distribution patterns for SNPs
# 
# pos = vector of SNP positions
# sdp = vector of strain distribution patterns
# labels = names of the strains
#
# example usage:
# plot_sdp(runif(100, 0, 100), sample(0:255, 100, replace=TRUE))

plot_sdp <-

kbroman

---
title: "test_plotly"
author: "Karl"
date: "5/10/2021"
output: html_document
---

Compile this at command line with 

```

kbroman

# functions for expanding probs and kinship matrices
#   for individual-level analysis of RILs


# create mapping, individuals -> lines
#
# n = sample size for each line
n2mapping <-
    function(n)
{

kbroman

library(qtl)

# example data set; calculate pairwise rec fracs
data(hyper)
hyper <- est.rf(hyper)

# set up color scale
n_colors <- 256
zmax <- 12
lod <- seq(0, zmax, len=n_colors)

kbroman

# bootstrap of scan1()

scan1boot <-
    function(genoprobs, pheno, kinship=NULL, addcovar=NULL, Xcovar=NULL, intcovar=NULL,
             weights=NULL, n_boot=1, ...)
{
    # to contain the results
    result <- vector("list", n_boot)

    # drop individuals that are not in common across all data

kbroman

# find large intervals in a map
#
#
# input: map = list of chromosomes that are vectors of marker positions
#              [can also be a cross object, in which case pull.map() is used]
#        min_distance = minimum distance between markers to be flagged
#
# example use:
#   library(qtl)
#   data(hyper)

kbroman