Tackling big questions with gene expression data

My data science job talk! I gave the full version of this talk at one company, and a 15-minute version at another.

Transcript

1. Alyssa Frazee Oct 23, 2014 Tackling big questions with gene expression data
2. What makes humans diverse?
3. How does a healthy cell turn into a cancer cell?
4. How do our bodies deal with injuries? What gives a cell its identity? How do we grow and develop?
5. In a cell:
6. Stories in DNA data Novembre et al 2008, PMID 18758442
7. What is gene expression?
8. AUCAGUCGAUCACCGAU What is gene expression? transcription DNA RNA translation protein ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
9. AUCAGUCGAUCACCGAU transcription DNA RNA gene’s “expression level” = amount of RNA in cell that was transcribed from that gene What is gene expression? ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
10. How do we measure gene expression? RNA-seq reads (50-100 bp long) Genome RNA transcripts
11. What does gene expression data look like? @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:3:177:-56:294:S/2 GCGTGAGCCACAGGGCCCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCT + @=ABBBBIIIIIIIIHHGGGGIIDBDIIIIIIGIIIIHIIIIHFDD@BBDBGGFIDEE8DCC/29>BGFCGHHHGF @22:16362385-16362561W:ENST00000440999:4:177:137:254:S/1 TCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCAAGGCCTGAACTACCTGCaGTGGGGAGCACCTCAGGGTTT + DDGBBCGGGIGGGBDDDHIIGGDGD77=BDIIIIIIIIFHHHHIIIHEFFHGGDD8A>DEGHHIFDDHH8@BEDDI @22:16362385-16362561W:ENST00000440999:5:177:68:251:S/2 AGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCCTGGAGCGAGTTGTGGATGGCAAAAAGACNCGCC + HIGHIHFHEGE4111:.;8@?@HDIIIIIIIEGGIHHHIIGA?=:FIIIDD8.02506A8=AC############# @22:16362385-16362561W:ENST00000440999:6:177:348:453:S/1 AAGGCCTGAACTACCTGCGGTGGGGAGCACCTCAGGGTTTGCCCAGGCAACCAGCCAGCCCTGGTCCAAGGCATCC + B9?@8=42:E@GDEDIIIIIGGHIIIFBEEAGIIDIIDHHGGHIIEGEIIIIIHIHFHFFEEFGGGGGB88>:DGH @22:51205934-51222090C:ENST00000464740:132:612:223:359:S/2 GGAAGTATGATGCTGATGACAACGTGAAGATCATCTGCCTGGGAGACAGCGCAGTGGGCAAATCCAAACTCATGGA + IIEHHHHHIIIIIIIHGGDGHHEDDG8=;?==19;<<>D@@GGGIIHIIHGGDDHGBA=ABEG@@DFCCAA<:=>8 @22:51205934-51222090C:ENST00000464740:125:612:-1:185:S/1 TGGAGTGCGCTGCGGCGCGAGCTGGGCCGGCGGGCGTGGTTCGAGAGCGCGCAGAGTCCAGACTGGCGGCAGGGCC + HHIIIHIDGG@;=@GIIIIIDDGBBBEDB@8>5554,/':9B@@C?==@1:2@?=GG=;<HHHHGIIHHEC-;;3?
12. How do we measure gene expression? RNA-seq reads (50-100 bp long) Genome RNA transcripts (**unknown**)
13. Data processing (1) put reads back where they came from
14. Data processing (1) put reads back where they came from (2) decide how to measure expression. One strategy: count number of reads originating from each gene expression = 24
15. Data processing (1) put reads back where they came from (2) decide how to measure expression. One strategy: count number of reads originating from each gene expression = 24 (lots of complexity here!)
16. CGAUCACCGAU AUCAGUCGAUCACCGAU AUCAGUCGAUC Example of a complication: DNA overlapping transcripts from same gene ACTGACCTAGATCAGTCGATCGATCGTATACGATTACAAAATCATCGGCAT
17. Data processing (1) put reads back where they came from (2) decide how to measure expression. One strategy: count number of reads originating from each gene expression = 24 (lots of complexity here!)
19. Is population structure also present in gene expression data? Makes sense that populations share DNA sequences, but do the genes themselves behave differently in different populations?
20. GEUVADIS gene expression data Genetic EUropean VAriation in health and DISease Raw gene expression data is publicly available for hundreds of individuals, so of course we couldn’t resist analyzing it ourselves. Two papers were published along with data release: one on scientific/biology findings (Lappalainen et al 2013, PMID 24037378) and one on technical patterns in gene expression data (‘t Hoen et al 2013, PMID 24037425)
21. turning “big” data into small data @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD @22:16362385-16362561W:ENST00000440999:2:177:-40:244:S/2 CCAGCCCACCTGAGGCTTCTTTTTCCTTCCCAAGCCACATCACCATCCTGGTGGAACTCTCCTGTGAGGACAGCCA + GGFF<BB=>GBGIIIIIIIIIIIIIIEGEHGHHIIIIIIIIHFHBB2/:=??EGGGEGFHHIHHEDBD?@@DDHHD raw data (~3 Tb) 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 aligned reads (~1.5 Tb) chr1 Cufflinks exon 14765 16672 . + . gene_id "XLOC_000001"; transcript_id "TCONS_00000001"; exon_number "1"; oId "CUFF.9.1"; tss_id "TSS1"; chr1 Cufflinks exon 566984 569564 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number "1"; oId "CUFF.14.1"; tss_id "TSS2"; chr1 Cufflinks exon 569902 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number "2"; oId "CUFF.14.1"; tss_id "TSS2"; chr1 Cufflinks exon 567008 568410 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number "1"; oId "CUFF.14.2"; tss_id "TSS2"; chr1 Cufflinks exon 569017 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number "2"; oId "CUFF.14.2"; tss_id "TSS2"; chr1 Cufflinks exon 567066 567843 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number "1"; oId "CUFF.14.3"; tss_id "TSS2"; chr1 Cufflinks exon 568627 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number all transcripts in data set (~150 Mb)
22. turning “big” data into small data 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 300811_fcB_:1:2207:14419:123617:0:1 256 1 19282 0 101M * 0 0 GCGAGCCTGTGTGGTGCGCAGGGATGAGAAG GCAGAGGCGCGACTGGGGTTCATGAGGAAGGGCAGGAGGAGGGTGTGGGATGGTGGAGGGGTTTGAGAAG [_J\cccegggegh^efghiiihg`fhfhiiifihiiiiiifgecccccZaccdccccccccccaccccc^aacQQ O[_cacca]]_[`^acT]^abcacc AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:101 YT:Z:UU NH:i:5 CC:Z:16 CP:i:68971 HI:i:0 aligned reads (~1.5 Tb) chr1 Cufflinks exon 14765 16672 . + . gene_id "XLOC_000001"; transcript_id "TCONS_00000001"; exon_number "1"; oId "CUFF.9.1"; tss_id "TSS1"; chr1 Cufflinks exon 566984 569564 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number "1"; oId "CUFF.14.1"; tss_id "TSS2"; chr1 Cufflinks exon 569902 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000002"; exon_number "2"; oId "CUFF.14.1"; tss_id "TSS2"; chr1 Cufflinks exon 567008 568410 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number "1"; oId "CUFF.14.2"; tss_id "TSS2"; chr1 Cufflinks exon 569017 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000003"; exon_number "2"; oId "CUFF.14.2"; tss_id "TSS2"; chr1 Cufflinks exon 567066 567843 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number "1"; oId "CUFF.14.3"; tss_id "TSS2"; chr1 Cufflinks exon 568627 570307 . + . gene_id "XLOC_000002"; transcript_id "TCONS_00000004"; exon_number all transcripts in data set (~150 Mb) combine to get expression matrix
23. GEUVADIS gene expression data 464 people 16,808 transcripts matrix entries: normalized expression measurement for person i, transcript j My software: https://github.com/alyssafrazee/ballgown & code: https://github. com/alyssafrazee/ballgown_code/tree/master/GEUVADIS_preprocessing for processing this data
24. Is population structure also present in gene expression data? populations??
25. Is population structure also present in gene expression data? No :(
26. Is population structure also present in gene expression data?
27. Lab effects labs 2, 3, 5 labs 1, 4, 6, 7
28. Data quality effects
29. Maybe some population structure
30. Maybe some population structure
31. Percent variance explained 16.7% variance explained by PC1 (lab!)
32. Rich Data: more to explore • genotype information • some individual-level characteristics (e.g. sex) • lots of technical information • several individuals sequenced in replicate • microRNA data • raw & processed data available!
33. Which SNPs affect expression levels of other genes? expression level for transcript 5000 nucleotides away from Chr1, location 64,122,505 copies of non-canonical nucleotide at Chr1, location 64,122,505
35. cancer data challenges
36. (1) put reads back where they came from (2) decide how to measure expression. One strategy: count number of reads originating from each gene expression = 24 cancer data challenges ALL BETS ARE OFF
37. a new strategy Frazee et al 2014, PMID 24398039
38. Thank you! Collaborators: Jeff Leek (advisor), Sarven Sabunciyan, Ben Langmead, Andrew Jaffe, Steven Salzberg, Rafa Irizarry, Kasper Hansen, Geo Pertea, Leonardo Collado Torres Code/data: This analysis: https://gist.github. com/alyssafrazee/5eb6b13f282a030c471f Processed GEUVADIS data: http://files.figshare.com/1625419/fpkm. rda. Software for analyzing transcript assemblies in R: https://github. com/alyssafrazee/ballgown
39. Image Credits • human diversity: Simon Abrams, CC BY-SA 2.0 [link] • tumor cells: cnicholsonpath (via Flickr), CC BY- SA 2.0 [link] • awesome cast: Jennifer Carole, CC BY-NA 2.0 [link] • transcription: National Library of Medicine, public domain [link] • microarrays: Caricato da Schutz, CC BY 2.5 [link] • cell differentiation: Rasback (via Wikipedia), CC BY-SA 2.5 [link] (I cropped it) • fly life cycle: Image Editor (via Flickr), CC BY 2.0 [link]
40. Metzker 2010, PMID 19997069
41. Metzker 2010, PMID 19997069
42. samples indexed by i locations indexed by l j confounders indexed by k expression confounders covariate of interest [extra] finding per-nt signal
43. Population structure: DNA data 1387 people 197,146 DNA locations where nucleotide varies matrix entries: how many copies of a “non- canonical” nucleotide at position j does person i have? (0, 1, or 2)