NOTE - before you begin, make sure your WORKING DIRECTORY is set to the location of the .vcf file being used.
All of this material will appear on the exam. Take notes on the workflow, functions, and concepts.
getwd() and
list.files(pattern = ...)extract.gt() command doesna.omit() on SNPsWe need just one package for this lesson, vcfR.
library(vcfR)## Warning: package 'vcfR' was built under R version 4.2.2##
## ***** *** vcfR *** *****
## This is vcfR 1.13.0
## browseVignettes('vcfR') # Documentation
## citation('vcfR') # Citation
## ***** ***** ***** *****In this lesson you will be using a .vcf file called
“all_loci.vcf”.
You must make sure that R has its working directory set to where this file is located.
The easiest way to do this is
Check the location of your working directory with
getwd()
setwd("C:/Users/mikaw/OneDrive/Pitt/Computational Biology/my_snps")
getwd()## [1] "C:/Users/mikaw/OneDrive/Pitt/Computational Biology/my_snps" # todoCheck for the presence of the “all_loci.vcf” file in the working
directory with list.files()
list.files()## [1] "(Studies in Feminist Philosophy) Elizabeth Brake - Minimizing Marriage_ Marriage, Morality, and the Law-Oxford University Press (2012).pdf"
## [2] "0110 F20 Recitation Worksheet 2 Key Updated to Post September 10.pdf"
## [3] "0110 F20 Recitation Worksheet 2 Send (1).pdf"
## [4] "0110 F20 Recitation Worksheet 2 Send.pdf"
## [5] "0110 F20 420 PM Syllabus Updated with Canvas Compliance (1).pdf"
## [6] "0110 F20 420 PM Syllabus Updated with Canvas Compliance.pdf"
## [7] "0110 F20 Lab 8 Thermochemistry I Updated for Openstax (1).pdf"
## [8] "0110 F20 Lab 8 Thermochemistry I Updated for Openstax (2).pdf"
## [9] "0110 F20 Lab 8 Thermochemistry I Updated for Openstax.pdf"
## [10] "0110 F20 Outline of Possible Final Exam Material.pdf"
## [11] "0110 F20 Recitation Skills Inventory for Students Send (1).pdf"
## [12] "0110 F20 Recitation Skills Inventory for Students Send.pdf"
## [13] "0110 F20 Recitation Skills Inventory Worksheet Key Send (1).pdf"
## [14] "0110 F20 Recitation Skills Inventory Worksheet Key Send.pdf"
## [15] "0110 F20 Recitation Worksheet 1 For Students (1).pdf"
## [16] "0110 F20 Recitation Worksheet 1 For Students.pdf"
## [17] "0110 F20 Recitation Worksheet 2 Key Send (1).pdf"
## [18] "0110 F20 Recitation Worksheet 2 Key Send.pdf"
## [19] "0110 F20 Schedule Post.pdf"
## [20] "0110 F20 Schedule Updated Post (1).pdf"
## [21] "0110 F20 Schedule Updated Post (2).pdf"
## [22] "0110 F20 Schedule Updated Post (3).pdf"
## [23] "0110 F20 Schedule Updated Post (4).pdf"
## [24] "0110 F20 Schedule Updated Post.pdf"
## [25] "0110 F20 Worksheet for Recitation Week beginning November 12.pdf"
## [26] "0110 FP20 OpenStax Chapter 4 Notes for Students to Use.pdf"
## [27] "020_W12D1_TWA Voc List UNIT 3.2 Relationships (Cohort).docx.rtf"
## [28] "020_W12D2_TWA UNIT UNIT 3.4 (Modal Verbs & Classifiers).pptx.pdf"
## [29] "1.10D-618F (1).ab1"
## [30] "1.10D-618F.ab1"
## [31] "1.11B-618F (1).ab1"
## [32] "1.11B-618F.ab1"
## [33] "1.11F-618F (1).ab1"
## [34] "1.11F-618F.ab1"
## [35] "1.11G-618F.seq"
## [36] "1.12G-618F.ab1"
## [37] "1.12G-618F.seq"
## [38] "1.12G-619R (1).ab1"
## [39] "1.12G-619R.ab1"
## [40] "1.12H-618F.seq"
## [41] "1.12H-618F_R (1).ab1"
## [42] "1.12H-618F_R.ab1"
## [43] "1.12H-618F_R.seq"
## [44] "1.12H-619R (1).ab1"
## [45] "1.12H-619R (2).ab1"
## [46] "1.12H-619R.ab1"
## [47] "1.3 worksheet.pdf"
## [48] "1_brkS1_3RenRDNRS.ab1"
## [49] "10_11 Class Notes.docx"
## [50] "102622 lg.pdf"
## [51] "102822 lg.pdf"
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## [60] "1530 Syllabus 2021-22.docx"
## [61] "1540_in_class_exercise_random_numrbers.pdf"
## [62] "1D-616F.ab1"
## [63] "2.2 workbook 11-2016.pdf"
## [64] "2.2E-618F.seq"
## [65] "2.2E-618F_R (1).ab1"
## [66] "2.2E-618F_R.ab1"
## [67] "2.2E-618F_R.seq"
## [68] "2.2E-619R (1).ab1"
## [69] "2.2E-619R.ab1"
## [70] "2015KEY (1).pdf"
## [71] "2015KEY.pdf"
## [72] "2019_12_04 Hainer Lab meeting.pptx"
## [73] "2020 Cailin Jordan Horowitz Fellowship Final Report.docx"
## [74] "2020 Horowitz family letter.docx"
## [75] "2020 Horowitz Fellowship Research Statement (1).docx"
## [76] "2020 Horowitz Fellowship Research Statement.docx"
## [77] "2021Movie#1.docx (1).pdf"
## [78] "2021Movie#1.docx.pdf"
## [79] "2022 Mika Wesley Horowitz Research Report.docx"
## [80] "2022 Summer Fellowship application .docx"
## [81] "2022 Summer fellowship instructions (1).docx"
## [82] "2022 Summer fellowship instructions (2).docx"
## [83] "2022 Summer fellowship instructions (3).docx"
## [84] "2022 Summer fellowship instructions.docx"
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## [86] "210702 big Lab meeting projects12.pptx"
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## [88] "220316 D1R PCR screen A7-12 B7-12.TIF"
## [89] "220316 D1R PCR screen A7-12 B7-12.tiff"
## [90] "220317 D1R digest screen A7-12 B7-12(1).tiff"
## [91] "220317 D1R digest screen A7-12 B7-12.TIF"
## [92] "220324 PCR test BB RS gDNA temp(1).tiff"
## [93] "220325 another test MW plate diluted and sarahs plate(1).tiff"
## [94] "220328 braulio test MW primers w MWgDNA SHgDNA A1 SH primers w MW gDNA SHgDNA A1(1).tiff"
## [95] "220331 D1R candidates A1 A2 B3 A4(1).tiff"
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## [97] "220407 D1R digest A1 A2 B3 A4.tiff"
## [98] "220414 D1R PCR screen A1_A2_B3_A5_B2_B5_B6_A5.tiff"
## [99] "220418 D1R row 5 and 5BC RD_ PCR A1_A2_B3_A4_B2_B5_B6_A5 2.tiff"
## [100] "220418 D1R row 5 and 5BC RD_ PCR A1_A2_B3_A4_B2_B5_B6_A5.tiff"
## [101] "220421 RA3 (conc 20_80_200) and B3 (conc 20_80_200).tiff"
## [102] "220422 PCR new primers row 2_ A1_ B3_ A4.tiff"
## [103] "220426 PCR test B3(133 at 60 and 65_ 616 at 60 and 65) other DNA(113 at 60 and 65_ 616 at 60 and 65).tiff"
## [104] "220428 PCR row2 (4 and 8ul).tiff"
## [105] "220428 PCR test 60_ 65_ 70 B3_ A1 different primers.tiff"
## [106] "220429 PCR row3 (4 and 8ul) (1).tiff"
## [107] "220429 PCR row3 (4 and 8ul).tiff"
## [108] "220429 PCR test 60_ 65_ 70 B3_ A4 different primers.tiff"
## [109] "220509 PCR row 4 (primers 113_ 109_ 115).tiff"
## [110] "220510 PCR 60 degrees A4 primers 113_ 109_ 115.tiff"
## [111] "220510 PCR 65 degrees A4 primers 113_ 109_ 115.tiff"
## [112] "220511 PCR A4 primer 113 (57_ 60_ 63)_ A4 primer 109 (60).tiff"
## [113] "220511 PCR A4 primers 113_114.tiff"
## [114] "220512 PCR wt gDNA primers 113_114.tiff"
## [115] "220513 PCR primers 113.114"
## [116] "220516 PCR 616_114 and 109_110 row 10 wt (1).tiff"
## [117] "220516 PCR 616_114 and 109_110 row 10 wt.tiff"
## [118] "220517 PCR row 7 wt 616_114 and 109_110 6 ul.tiff"
## [119] "220517 PCR targetted BC plate row 10 616_114.tiff"
## [120] "220517 PCR wt row 7 616_114 and 109_110 8 ul.tiff"
## [121] "220518 PCR wt gDNA primers 618_619 and 109_110.tiff"
## [122] "220520 PCR wt row 8 primers 618_619.tiff"
## [123] "221013 D3L, D2L, PxL.SQV"
## [124] "221018 D2L, PxL, and D3R row 1 RD.TIF"
## [125] "221110 D3R Rd rows 7 to 12 (pxL positive control at end).TIF"
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## [137] "4sU Labelling and Labelled RNA Isolation (1).docx"
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## [149] "A_Companion_to_Contemporary_French_Cinema_----_(2_“Do_We_Have_the_Right_to_Exist_”_French_Cinema_Culture_and_World_Tra...).pdf"
## [150] "A3-111F.ab1"
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## [152] "Abortion Resources.pdf"
## [153] "About you (1).docx"
## [154] "About you.docx"
## [155] "ABR Module 1 PowerPoint.pdf"
## [156] "Abstract (1) (1).docx"
## [157] "Abstract (1).docx"
## [158] "Abstract.docx"
## [159] "Academic Transcript.pdf"
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## [162] "Activity 9_ Electrophilic Aromatic Substitution Reactions_ Bromination of Aniline and N-Acetylaniline (1).pdf"
## [163] "Activity 9_ Electrophilic Aromatic Substitution Reactions_ Bromination of Aniline and N-Acetylaniline.pdf"
## [164] "actual expression clone.ape"
## [165] "Additional Questions.docx"
## [166] "Additional Questions_SH (1).docx"
## [167] "Additional Questions_SH.docx"
## [168] "Adjectives (1).docx"
## [169] "Adjectives.docx"
## [170] "Adli_2018_CRISPRreview.pdf"
## [171] "aligned to SB.png"
## [172] "aligned to WT.png"
## [173] "allomtery_3_scatterplot3d (1).Rmd"
## [174] "An_effect_of_DNA_sequence_on_nucleosome_occupancy_ (1).pdf"
## [175] "An_effect_of_DNA_sequence_on_nucleosome_occupancy_.pdf"
## [176] "Analysis of classmates data interpretation.docx"
## [177] "Anne Fausto-Sterling, “Dueling Dualisms” .pdf"
## [178] "annotated-Journal%202.docx.pdf"
## [179] "annotated-Reflection%20Paper%201.docx.pdf"
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## [181] "annotated-Sequence%20Analysis%202.docx.pdf"
## [182] "Annotated Bibliography.docx"
## [183] "Antigone FR0221.pptx"
## [184] "Ape_program.docx"
## [185] "ApE_win_current.zip"
## [186] "APP1210_New_Application_From_Ericka_9.24.20 (1).pdf"
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## [189] "ar_biochem89_213.ris"
## [190] "ASL Stories (1).docx"
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## [202] "bell hooks - All about Love_ New Visions-William Morrow Paperbacks (2001).pdf"
## [203] "Bey-Rozet_FR16_History_of_French_Cinema_Syllabus (1).docx"
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## [212] "Bey-Rozet_FR16_History_of_French_Cinema_Syllabus.docx"
## [213] "Binder3-was s21 doc cam intro imf.pdf"
## [214] "BIo-Rad_EZ_Imager.docx"
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## [216] "BIOSC 0370 Syllabus, 2214.pdf"
## [217] "Book Report (1).docx"
## [218] "Book Report.docx"
## [219] "BookLot.17.2102.1pawk (1).exe"
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## [224] "Bottom Flowers and Placement.jpeg"
## [225] "brk_coding_region (1).ape"
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## [227] "BS1520-Lecture 1.pptx"
## [228] "Buchsbaum - Do We Have a Right to Exist.pdf"
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## [330] "Chapter 1 Structure and Bonding in Organic Molecules answer key.docx"
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## [1194] "Topic 7 PPT - Population distributions (student).pptx"
## [1195] "Transformation_of_E.coli_updated_2-21-22.docx"
## [1196] "transpose_VCF_data.Rmd"
## [1197] "Troubleshooting and Progress July11.pptx"
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## [1226] "Vocab List.docx"
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## [1229] "Wed Nov 04 2020 6_06_13 PM (1).webm"
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## [1231] "Wed Nov 04 2020 6_06_13 PM.webm"
## [1232] "Week 11 Annotation presentation and intro to covid variants full slides.pptx"
## [1233] "Week 13 AA substitution protein analysis full slides.pptx"
## [1234] "Week 2 Breakout session slides.pptx"
## [1235] "Week 3 Cover and HW Pdf.pdf"
## [1236] "Week 4 BLAST breakout slides Wed.pptx"
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## [1238] "Week 7 Function breakout slides.pptx"
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## [1240] "Week 9 Guiding Principles Floral slides (1).pptx"
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## [1253] "Wesley_Summer 2022 Fellowship Letter (1).pdf"
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## [1255] "Who are the gilets jaunes and what do they want_ _ France _ The Guardian.pdf"
## [1256] "Will Conroy's Notes Chapter V.pdf"
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## [1271] "Zoom_cm_fiisiZ9vvrZo4_mX9ECbv1ZEkEMZF+UUOkrDGyeiZp6W5prBWMS@EaezZkzDPa+yaQS2_kfdcf9dc3b617a3fb_.exe" # todoIf you have lots of files in the working directory, you can search for the file specifically with list.files(pattern = “vcf”)
list.files(pattern = "vcf") ## [1] "code_checkpoint_vcfR.html" "code_checkpoint_vcfR.Rmd"
## [3] "vcfR_test.vcf" "vcfR_test.vcf.gz" # todoSNP data is often stored in Variant Call Format (VCF) files that are organized differently than normal R data. When SNP data is analyzed in R, such as by doing PCA or cluster analysis, the SNPs are usually the features (variables) and each row is a sample (such as a person, or other organism). VCF files, however, are organized with SNPs in rows and samples in columns.
Changing how data is arranged is called reshaping. Reshaping can take different forms. In this case we need to flip the orientation of the data so that the rows, SNPS, become the columns.
In R we can flip the orientation of matrix or dataframe using
t() operation, which stands for transpose.
The transpose of a dataset takes each row and makes it into a column.
For vcf data, this means that the SNPs in rows can be made into
columns.
The example below will illustrate the general principle of transposition of data. First, let’s make two vectors of data in vcf-like format, with genotypes expressed as “A|A”.
# Add the c() function to make
## vectors containing SNP genotypes
SNP01 <- c("A|A","A|A","A|A","A|A") # TODO
SNP02 <- c("G|G","G|G","G|G","G|G")Now let’s make a matrix with rbind()
# add the rbind() function
## to combine the vectors into a dataframe
my_matrix <- rbind(SNP01, SNP02) # TODOSince this is representing a vcf file, the rows will be SNPS and the
columns will be samples. I’ll name the columns with
colnames().
# add the colnames() function around
## the my_matrix object to
## rename it
colnames(my_matrix) <- c("sample1", # TODO
"sample2",
"sample3",
"sample4")We now have a matrix in the general form of a vcf file
# add the my_matrix object to the chunk so
## its contents are shown
my_matrix## sample1 sample2 sample3 sample4
## SNP01 "A|A" "A|A" "A|A" "A|A"
## SNP02 "G|G" "G|G" "G|G" "G|G" # TODOThe t() function will re-orient our matrix for us so
that SNPs are in columns and samples are in rows.
# add the t() function to transpose
## the matrix
t(my_matrix) # TODO## SNP01 SNP02
## sample1 "A|A" "G|G"
## sample2 "A|A" "G|G"
## sample3 "A|A" "G|G"
## sample4 "A|A" "G|G"We can save the transposed output to a new matrix.
# add the t() function and assign
## the output to an object called
## my_matrix_t
my_matrix_t <- t(my_matrix) # TODOWe can do this in R with dataframes also:
# add the data.frame() function to
## turn my_matrix into a dataframe
## and assign it to an object called my_df
my_df <- data.frame(my_matrix) # TODOAgain, t() flips the dataframe for us:
# add the t() function to show the flipped
## dataframe
t(my_df) # TODO## SNP01 SNP02
## sample1 "A|A" "G|G"
## sample2 "A|A" "G|G"
## sample3 "A|A" "G|G"
## sample4 "A|A" "G|G"A typical VCF file can be found in the file
all_loci.vcf. (Note that this file is NOT compressed and so
has an extension of .vcf, not .vcf.gz). Load
the data into an object called bird_snps using the function
vcfR::read.vcfR().
NOTE - before you begin, make sure your WORKING DIRECTORY is set to the location of the .vcf file being used.
setwd("C:/Users/mikaw/OneDrive/Pitt/Computational Biology/my_snps")
bird_snps <- vcfR::read.vcfR("all_loci.vcf",
convertNA = T) #TODO## Scanning file to determine attributes.
## File attributes:
## meta lines: 8
## header_line: 9
## variant count: 1929
## column count: 81
##
Meta line 8 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 1929
## Character matrix gt cols: 81
## skip: 0
## nrows: 1929
## row_num: 0
##
Processed variant 1000
Processed variant: 1929
## All variants processedMake sure that at the bottom of the output in the console it says “Processed variant: 1929”.
Call head on the bird_snps object. You should be able to identify the metadata (at the top) and be able to see how samples are organized in rows.
# run head() on bird_snps
head(bird_snps) # TODO## [1] "***** Object of class 'vcfR' *****"
## [1] "***** Meta section *****"
## [1] "##fileformat=VCFv4.0"
## [1] "##fileDate=20160506"
## [1] "##source=\"Stacks v1.20\""
## [1] "##INFO=<ID=NS,Number=1,Type=Integer,Description=\"Number of Samples With Data\">"
## [1] "##INFO=<ID=AF,Number=.,Type=Float,Description=\"Allele Frequency\">"
## [1] "##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">"
## [1] "First 6 rows."
## [1]
## [1] "***** Fixed section *****"
## CHROM POS ID REF ALT QUAL FILTER
## [1,] "un" "1478" "17" "C" "A" NA "PASS"
## [2,] "un" "1479" "17" "A" "G" NA "PASS"
## [3,] "un" "1723" "20" "C" "T" NA "PASS"
## [4,] "un" "1948" "22" "C" "A" NA "PASS"
## [5,] "un" "1972" "22" "C" "T" NA "PASS"
## [6,] "un" "2299" "26" "C" "T" NA "PASS"
## [1]
## [1] "***** Genotype section *****"
## FORMAT sample_ACAAA_Nel3 sample_ACAGTG_Nel5
## [1,] "GT:DP:GL" "1/0:344:.,-359.6,." "1/0:265:.,-273.8,."
## [2,] "GT:DP:GL" "0/1:344:.,-359.6,." "0/1:265:.,-273.8,."
## [3,] "GT:DP:GL" "0/0:254:.,352.119,." "0/0:267:.,370.141,."
## [4,] "GT:DP:GL" "0/0:332:.,460.25,." "0/0:339:.,469.954,."
## [5,] "GT:DP:GL" "0/0:332:.,431.419,." "0/0:339:.,454.108,."
## [6,] "GT:DP:GL" "0/0:421:.,553.847,." "0/0:412:.,540.081,."
## sample_AGCAT_Nel8 sample_ATGAAAC_Nel10 sample_ATGAAAC_Nel15
## [1,] "0/0:252:.,349.346,." "0/0:253:.,350.732,." "0/0:260:.,360.437,."
## [2,] "0/0:252:.,349.346,." "0/0:253:.,350.732,." "0/0:260:.,360.437,."
## [3,] "0/0:373:.,517.088,." "0/0:222:.,307.757,." "0/0:298:.,413.116,."
## [4,] "0/0:288:.,399.253,." "0/0:232:.,321.62,." "0/0:342:.,474.113,."
## [5,] "0/0:288:.,399.253,." "0/0:232:.,321.62,." "0/0:342:.,474.113,."
## [6,] "0/0:444:.,615.515,." "0/0:377:.,522.633,." "0/0:379:.,525.406,."
## [1] "First 6 columns only."
## [1]
## [1] "Unique GT formats:"
## [1] "GT:DP:GL"
## [1]We can view just the meta data like this:
bird_snps@meta## [1] "##fileformat=VCFv4.0"
## [2] "##fileDate=20160506"
## [3] "##source=\"Stacks v1.20\""
## [4] "##INFO=<ID=NS,Number=1,Type=Integer,Description=\"Number of Samples With Data\">"
## [5] "##INFO=<ID=AF,Number=.,Type=Float,Description=\"Allele Frequency\">"
## [6] "##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">"
## [7] "##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read Depth\">"
## [8] "##FORMAT=<ID=GL,Number=.,Type=Float,Description=\"Genotype Likelihood\">"(Don’t worry about what the “at” symbol is using - this is a somewhat less common R syntax).
We can get a snapshot of the samples like this:
bird_snps@gt[1:10, 1:3]## FORMAT sample_ACAAA_Nel3 sample_ACAGTG_Nel5
## [1,] "GT:DP:GL" "1/0:344:.,-359.6,." "1/0:265:.,-273.8,."
## [2,] "GT:DP:GL" "0/1:344:.,-359.6,." "0/1:265:.,-273.8,."
## [3,] "GT:DP:GL" "0/0:254:.,352.119,." "0/0:267:.,370.141,."
## [4,] "GT:DP:GL" "0/0:332:.,460.25,." "0/0:339:.,469.954,."
## [5,] "GT:DP:GL" "0/0:332:.,431.419,." "0/0:339:.,454.108,."
## [6,] "GT:DP:GL" "0/0:421:.,553.847,." "0/0:412:.,540.081,."
## [7,] "GT:DP:GL" "0/0:421:.,583.63,." "0/0:412:.,571.153,."
## [8,] "GT:DP:GL" "0/0:421:.,583.63,." "0/0:412:.,571.153,."
## [9,] "GT:DP:GL" "0/0:528:.,731.963,." "0/0:301:.,417.275,."
## [10,] "GT:DP:GL" "0/0:253:.,350.732,." "0/0:248:.,343.801,."Each row is a SNP and each column is a sample (a bird in this case.)
We extract genotype scores using vcfR::extract.gt(). You
need to be familiar with this command, but I’ll give you the code to do
this on an exam.
# Add vcfR::extract.gt() to extract the
## numeric scores
bird_snps_num <- vcfR::extract.gt(bird_snps, # TODO
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T)We now have just the numeric data that would go into an analysis such as PCA.
The sample names are REALLY long so its hard to display in a compact
way. The code below will make this a a little easier to see using the
regular expression gsub().
colnames(bird_snps_num) <- gsub("sample_", "",
colnames(bird_snps_num))
colnames(bird_snps_num) <- gsub("_", "",
colnames(bird_snps_num))We can see that the matrix just contains numeric genotype scores of 0, 1 or 2.
Here’s a small view of data:
bird_snps_num[1:10, 1:4]## ACAAANel3 ACAGTGNel5 AGCATNel8 ATGAAACNel10
## [1,] 1 1 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 0 0 0 0
## [5,] 0 0 0 0
## [6,] 0 0 0 0
## [7,] 0 0 0 0
## [8,] 0 0 0 0
## [9,] 0 0 0 0
## [10,] 0 0 0 0We can call summary of a bit of the data like this:
summary(bird_snps_num[, 1:5])## ACAAANel3 ACAGTGNel5 AGCATNel8 ATGAAACNel10
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.08574 Mean :0.07962 Mean :0.08259 Mean :0.07192
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## NA's :28 NA's :20 NA's :28 NA's :24
## ATGAAACNel15
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.0766
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :23The data are formatted as genotype scores but SNPs are in columns and
samples in row. We can reformat them using the transpose function
t().
# run t() on bird_snps_num and
## save the output to bird_snps_num_t
bird_snps_num_t <- t(bird_snps_num) # TODOWe can look at the output like this:
bird_snps_num_t[1:10, 1:4]## [,1] [,2] [,3] [,4]
## ACAAANel3 1 0 0 0
## ACAGTGNel5 1 0 0 0
## AGCATNel8 0 0 0 0
## ATGAAACNel10 0 0 0 0
## ATGAAACNel15 0 0 0 0
## CGATGTNel4 0 0 0 0
## CTAGCNel7 0 0 0 0
## CTGTANel12 0 0 0 0
## CTGTANel9 0 0 0 0
## CTTGANel2 0 0 0 0dim(bird_snps_num_t)## [1] 72 1929These data have a lot of NAs. If we just call na.omit() on them, what
happens? Call na.omit() on the bird_snps_num_t
object, then check the dimensions.
# Call na.omit() on bird_snps_num_t
## and assign the output to no_NAs
no_NAs <- na.omit(bird_snps_num_t) # TODO
# what is the remaining size of the data?
# why
dim(no_NAs) # TODO## [1] 0 1929