Single-Cell Transcriptome Analysis of Pluripotent Stem Cells
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An overview of the computational pipeline used at the CReM
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Single-Cell Transcriptome Analysis of Pluripotent Stem Cells
- 1. Single-Cell Transcriptome Analysis of Pluripotent Stem Cells Nacho Caballero Center for Regenerative Medicine Boston University Jun 12, 2017 From raw data to insights
- 3. Raw data AT CG Initial QC Analysis pipeline
- 4. Raw data AT CG Alignment and Quantification Initial QC Analysis pipeline
- 5. Raw data AT CG Alignment and Quantification Outlier analysis Initial QC Analysis pipeline
- 6. Raw data AT CG Alignment and Quantification Outlier analysis Gene selection and clustering Initial QC Analysis pipeline
- 7. Raw data AT CG Alignment and Quantification Outlier analysis Gene selection and clustering Initial QC Insights Analysis pipeline
- 8. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG
- 10. Demultiplex One pair of sequencing files per cell Barcoded sequencing files AT CG
- 11. Demultiplex One pair of sequencing files per cell @NB500996:64:HNM72BGX2:3:12510:12240:9366 2:N:0:T CTACTGTCTAGAGCTTGTCTCAATGGATCTAGAACTTCATCGCCCTCTG + AAAAAEEEE<E/EEEEEEEEE6EE/6AEEE//E/EEE/AEA/EAEEEE< … Millions of reads Barcoded sequencing files AT CG
- 12. Demultiplex One pair of sequencing files per cell @NB500996:64:HNM72BGX2:3:12510:12240:9366 2:N:0:T CTACTGTCTAGAGCTTGTCTCAATGGATCTAGAACTTCATCGCCCTCTG + AAAAAEEEE<E/EEEEEEEEE6EE/6AEEE//E/EEE/AEA/EAEEEE< … Millions of reads Metadata file Cell_id Condition1 Condition2 Cell_01 BU3 red Cell_02 BU3 green Cell_03 C17 red Cell_04 C17 green Cell_05 BU3 red Cell_06 BU3 green … Barcoded sequencing files AT CG
- 13. Demultiplex One pair of sequencing files per cell @NB500996:64:HNM72BGX2:3:12510:12240:9366 2:N:0:T CTACTGTCTAGAGCTTGTCTCAATGGATCTAGAACTTCATCGCCCTCTG + AAAAAEEEE<E/EEEEEEEEE6EE/6AEEE//E/EEE/AEA/EAEEEE< … Millions of reads Metadata file Cell_id Condition1 Condition2 Cell_01 BU3 red Cell_02 BU3 green Cell_03 C17 red Cell_04 C17 green Cell_05 BU3 red Cell_06 BU3 green … Barcoded sequencing files AT CG Short simple names
- 14. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Analysis pipeline
- 16. Good cDNA quality Position in Read AvgSequenceQuality
- 17. Good cDNA quality Read length is often inversely correlated with base-pair sequencing quality Position in Read AvgSequenceQuality
- 18. Good cDNA quality Average quality Read length is often inversely correlated with base-pair sequencing quality Position in Read AvgSequenceQuality
- 19. Good cDNA quality Average quality Bad quality Read length is often inversely correlated with base-pair sequencing quality Position in Read AvgSequenceQuality
- 21. More reads is generally better than longer reads (safe target: 200K reads, 150-bp long) Numberofreadspercell 1M 10K 1K 0 400 Cells
- 22. The Fluidigm protocol makes it extremely easy to lose entire rows or columns Rows Columns
- 23. The Fluidigm protocol makes it extremely easy to lose entire rows or columns Rows Columns
- 24. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Analysis pipeline
- 25. We quantify the gene expression in a cell by counting how many reads align to each gene
- 26. SFTPC gene We quantify the gene expression in a cell by counting how many reads align to each gene
- 27. AGGCAGAGGGGCGAGATGCA… SFTPC gene We quantify the gene expression in a cell by counting how many reads align to each gene
- 28. AGGCAGAGGGGCGAGATGCA… 1358 reads aligned to the SFTPC gene in this cell SFTPC gene We quantify the gene expression in a cell by counting how many reads align to each gene
- 29. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 30. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 31. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 32. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 33. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 34. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241
- 35. Read type Number of reads per cell Raw 333,229 Unaligned 81,673 Aligned, but non-uniquely 28,813 Aligned uniquely, but not to a gene 32,774 Aligned uniquely, but span multiple genes 20,838 Aligned uniquely to a single gene 167,241 40-60% of the raw reads cannot be used to quantify gene expression
- 36. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Analysis pipeline
- 37. Filter out cells with fewer than 5K aligned reads Numberofalignedreads 1M 10K 1K 0 120 Cells
- 38. Filter out cells with a high percentage of mitochondrial gene counts (indicative of a broken cell membrane) %ofMitochondrialgenecounts 100% 75% 50% 0 48 Cells 25%
- 39. Filter out cells with less than 2K expressed genes Numberofexpressedgenes 6K 4K 0 30 Cells 2K
- 40. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Analysis pipeline
- 41. Raw count data Normalized expression data
- 42. Raw count data Assume that most genes are not differentially expressed Normalized expression data
- 43. Raw count data Assume that most genes are not differentially expressed Calculate scaling factors for each cell Normalized expression data
- 44. Raw count data Assume that most genes are not differentially expressed Calculate scaling factors for each cell Normalized expression data Apply the scaling factors and log
- 45. Raw count data Normalization corrects for differences in capture efficiency, sequencing depth and other technical bias Assume that most genes are not differentially expressed Calculate scaling factors for each cell Normalized expression data Apply the scaling factors and log
- 50. Averageexpression Expression Variance high expression low variance cell Expression low expression low variance
- 51. Averageexpression Expression Variance high expression low variance cell Expression low expression low variance high expression high variance high expression high variance
- 52. Typical questions What are the expression differences between my experimental groups?
- 53. Typical questions What are the expression differences between my experimental groups? What are the subpopulations in my data?
- 54. Typical questions What are the expression differences between my experimental groups? What are the subpopulations in my data? What are the gene expression patterns in each subpopulation?
- 57. ASSIGN CELLS TO GROUPS SELECT GENES NO A difference between the populations (signal) should appear among the most variable genes Averageexpression Variance TREAT CONDITIONS AS GROUPS?
- 58. ASSIGN CELLS TO GROUPS SELECT GENES NO A difference between the populations (signal) should appear among the most variable genes Averageexpression Variance TREAT CONDITIONS AS GROUPS?
- 59. ASSIGN CELLS TO GROUPS SELECT GENES NO Variance is a necessary but insufficient indicator of population differences Averageexpression Variance TREAT CONDITIONS AS GROUPS?
- 60. ASSIGN CELLS TO GROUPS SELECT GENES NO Averageexpression Variance Unique populations consistently over or under-express a set of genes TREAT CONDITIONS AS GROUPS?
- 63. ASSIGN CELLS TO GROUPS SELECT GENES NO TREAT CONDITIONS AS GROUPS? The silhouette coefficient is a useful metric to determine the optimal number of groups
- 64. ASSIGN CELLS TO GROUPS SELECT GENES NO k = 2 Silhouette coefficient: 0.48 TREAT CONDITIONS AS GROUPS? The silhouette coefficient is a useful metric to determine the optimal number of groups
- 65. ASSIGN CELLS TO GROUPS SELECT GENES NO k = 3 Silhouette coefficient: 0.56 TREAT CONDITIONS AS GROUPS? The silhouette coefficient is a useful metric to determine the optimal number of groups
- 66. ASSIGN CELLS TO GROUPS SELECT GENES NO k = 4 Silhouette coefficient: 0.47 TREAT CONDITIONS AS GROUPS? The silhouette coefficient is a useful metric to determine the optimal number of groups
- 67. ASSIGN CELLS TO GROUPS TEST GENES FOR DIFFERENTIAL EXPRESSION YES SELECT GENES NO TREAT CONDITIONS AS GROUPS?
- 68. ASSIGN CELLS TO GROUPS TEST GENES FOR DIFFERENTIAL EXPRESSION YES SELECT GENES NO TREAT CONDITIONS AS GROUPS? Variance Average expression Differentially expressed genes
- 69. ASSIGN CELLS TO GROUPS TEST GENES FOR DIFFERENTIAL EXPRESSION YES SELECT GENES NO TREAT CONDITIONS AS GROUPS? Variance Average expression Differentially expressed genes
- 70. ASSIGN CELLS TO GROUPS TEST GENES FOR DIFFERENTIAL EXPRESSION YES SELECT GENES NO TREAT CONDITIONS AS GROUPS? Variance Average expression Differentially expressed genes Variance Average expression Highly variable genes
- 71. ASSIGN CELLS TO GROUPS TEST GENES FOR DIFFERENTIAL EXPRESSION YES SELECT GENES NO TREAT CONDITIONS AS GROUPS? Variance Average expression Differentially expressed genes Variance Average expression Highly variable genes
- 72. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Analysis pipeline
- 74. Real heatmaps are a rough-draft visualization
- 75. NKX2-1 CD47 Real heatmaps are a rough-draft visualization
- 76. NKX2-1 CD47 NKX2-1 CD47 Real heatmaps are a rough-draft visualization
- 77. NKX2-1 CD47 NKX2-1 CD47 ROW-SCALING GLOBAL SCALING Real heatmaps are a rough-draft visualization
- 78. Expression patterns are better conveyed by showing individual genes
- 79. Expression patterns are better conveyed by showing individual genes
- 80. CLUSTERED Expression patterns are better conveyed by showing individual genes
- 81. CLUSTEREDRANDOM Expression patterns are better conveyed by showing individual genes
- 82. Geneset enrichment analysis depends on the quality of the geneset
- 83. Geneset enrichment analysis depends on the quality of the geneset MsigDB hallmark genesets only contain 4000 genes
- 84. Geneset enrichment analysis depends on the quality of the geneset MsigDB hallmark genesets only contain 4000 genes MAKE YOUR OWN GENESETS FROM THE LITERATURE
- 94. Remember to provide a metadata file Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways
- 95. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways More reads is usually better than longer reads
- 96. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways You will only be able to align 50% of your reads
- 97. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways Assume that 50% of your cells could fail
- 98. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways High variance doesn’t imply subpopulations
- 99. Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways Make your own gene lists!
- 100. Slides available at: bit.ly/crem_bioinformatics Raw data Initial QC Alignment and Quantification Outlier analysis Gene selection and clustering Insights AT CG Takeaways