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comparison R_functions/pca_helpers.R @ 0:64e75e21466e draft default tip

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author pmac
date Wed, 01 Jun 2016 03:38:39 -0400
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-1:000000000000 0:64e75e21466e
1 library(flashpcaR)
2
3 # read in data from either a numeric ped file or an rds object
4 # output a numeric ped file, with the rownames set to the ids of the samples
5 get_source_data = function(data_source, data_type) {
6 data_type = tolower(data_type)
7 if (data_type == "numeric_ped") {
8 # read in ped file
9 ped_data = read.table(data_source, sep="\t", row.names=1)
10 } else if (data_type == "rds") {
11 hapmap3_object = readRDS(data_source)
12 ped_data = hapmap3_object$bed
13 } else if (data_type == "rdata") {
14 hapmap3_object = load_obj(data_source)
15 ped_data = hapmap3_object$bed
16 } else {
17 print("Unrecognised data type, returning NULL")
18 ped_data = NULL
19 }
20 return(ped_data)
21 }
22
23 # A function that will read in and return a single object from an RData file
24 # This is a workaround so the program can run without needing to know name of the object;
25 # however the assumption is that the RData file contains only ONE object (the one we want)
26 load_obj = function(filename) {
27 # create new environment
28 env = new.env()
29 # load the rdata file into the new environment, and get the NAME
30 # of the first object
31 object_name = load(filename, env)[1]
32 # return the object
33 return(env[[object_name]])
34 }
35
36 # remove unwanted rows or columns (samples and snps, respectively) from
37 # the ped data
38 filter_ped_data = function(ped_data, xsamples, xsnps) {
39 # rows to remove
40 rr = which(rownames(ped_data) %in% xsamples)
41 # remove rejected samples
42 if (length(rr) != 0) {
43 fpd1 = ped_data[-rr, , drop=FALSE]
44 } else {
45 fpd1 = ped_data
46 }
47 # remove all zero and rejected snp columns
48 snps = which(colnames(ped_data) %in% xsnps)
49 zeros = which(colSums(abs(fpd1)) == 0)
50 cr = union(snps, zeros)
51 if (length(cr) != 0) {
52 fpd2 = fpd1[, -cr, drop=FALSE]
53 } else {
54 fpd2 = fpd1
55 }
56 # remove monomorphic snps
57 snp_sds = apply(fpd2, 2, sd)
58 clean_ped = fpd2[, snp_sds >= 0.01, drop=FALSE]
59 return(clean_ped)
60 }
61
62 # Ethnicity file requirements:
63 # - tab delimited
64 # - Must have at least two columns
65 # - First column has sample ID's
66 # - Second column has ethnicities
67 # - First row must be a header
68 parse_ethnicity_file = function(eth_filename) {
69 if (file.exists(eth_filename) == FALSE) {
70 print(paste0("Warning: Ethnicity file: ", eth_filename, " not found"))
71 return(NULL)
72 }
73 if (file.info(eth_filename)$size == 0) {
74 print(paste0("Warning: Ethnicity file: '", eth_filename, "' is empty"))
75 return(NULL)
76 }
77 eth_data = read.table(eth_filename, header=TRUE, sep="\t")
78 rownames(eth_data) = eth_data[, 1]
79 colnames(eth_data)[1] = "IID"
80 colnames(eth_data)[2] = "population"
81 return(eth_data)
82 }
83
84 # Read in a file and return the first column as a
85 # character vector
86 get_first_column = function(fname) {
87 rv = c()
88 if (file.exists(fname) == FALSE) {
89 print(paste0("Warning: File: '", fname, "' not found"))
90 return(rv)
91 }
92 if (file.info(fname)$size == 0) {
93 print(paste0("Warning: File: '", fname, "' is empty"))
94 return(rv)
95 } else {
96 rv = as.character(read.table(fname)[, 1])
97 return(rv)
98 }
99 }
100
101 # Do pca using flashpcar. Returns a 2 element list
102 # values - contains the loadings of the pcs
103 # Will be an n x m matrix, where
104 # - n = Number of samples
105 # - m = number of pcs
106 # ids - Character array of ids, same length as number of rows in values
107 do_pca = function(ped_data) {
108 pca_data = list()
109 pm = data.matrix(ped_data)
110 values = flashpca(pm, ndim=6)$vectors
111 pca_data$values = values
112 pca_data$ids = as.character(rownames(ped_data))
113 return(pca_data)
114 }