Blue:RNA-Seq Analyses:C1 hNuc 1000 Dataset: Difference between revisions
Jump to navigation
Jump to search
>B1lake |
>B1lake |
||
Line 13: | Line 13: | ||
*Circled experiments represent the same batch of nuclei. There is little effect of freezing the nuclei in 10%DMSO or running the nuclei on a small chip rather than the medium chip. Global levels of cDNA were reduced with adding a nuclei stain/wash step to the loading protocol using the DNA Seq Cell Wash buffer. Also, addition of PolydIdC did globally increase cDNA levels even for the 0_Nuc conditions. | *Circled experiments represent the same batch of nuclei. There is little effect of freezing the nuclei in 10%DMSO or running the nuclei on a small chip rather than the medium chip. Global levels of cDNA were reduced with adding a nuclei stain/wash step to the loading protocol using the DNA Seq Cell Wash buffer. Also, addition of PolydIdC did globally increase cDNA levels even for the 0_Nuc conditions. | ||
*Examination of [http://genome-tech.ucsd.edu/LabNotes/index.php/Rui:LabNotes/SingleCell/2014-7-3#Basic_mapping_statistics basic mapping stats] shows that the small chip run 19 was highly variable (likely due to capture of small low RNA containing nuclei), while addition of PolydIdC seemed to stabilize mapping rates. Also, even though the cDNA yields were very high for the 0_Nuc with PolydIdC, most of the reads still mapped to ERCC. | *Examination of [http://genome-tech.ucsd.edu/LabNotes/index.php/Rui:LabNotes/SingleCell/2014-7-3#Basic_mapping_statistics basic mapping stats] shows that the small chip run 19 was highly variable (likely due to capture of small low RNA containing nuclei), while addition of PolydIdC seemed to stabilize mapping rates. Also, even though the cDNA yields were very high for the 0_Nuc with PolydIdC, most of the reads still mapped to ERCC. | ||
==Comparison of C1 Run Variations on the Same Nuclei Batch== | |||
*Using Rui's mapped TPM data, performed analysis using Fluidigmn's Singular software: | |||
**Identified outliers based on log2 mapping rates and trimmed genes with TPM less than 2 (LOD1) | |||
**Performed pairwise comparison of each run: | |||
[[File:0227_Nuc_Pairwise_1.png | 500px]] | |||
**Performed PCA analysis on each run: | |||
[[File:0227_Nuc_PCA_1.png |500px]] | |||
*Can see that none of the conditions affected the gene expression profiles of the captured nuclei and all nuclei retained similar PCA distributions across conditions | |||
== next== |
Revision as of 18:52, 11 July 2014
Overview
- C1 Run Summary:
Average cDNA Yields Across Runs
File:20 C1 Runs cDNA Yields 2.png
- Circled experiments represent the same batch of nuclei. There is little effect of freezing the nuclei in 10%DMSO or running the nuclei on a small chip rather than the medium chip. Global levels of cDNA were reduced with adding a nuclei stain/wash step to the loading protocol using the DNA Seq Cell Wash buffer. Also, addition of PolydIdC did globally increase cDNA levels even for the 0_Nuc conditions.
- Examination of basic mapping stats shows that the small chip run 19 was highly variable (likely due to capture of small low RNA containing nuclei), while addition of PolydIdC seemed to stabilize mapping rates. Also, even though the cDNA yields were very high for the 0_Nuc with PolydIdC, most of the reads still mapped to ERCC.
Comparison of C1 Run Variations on the Same Nuclei Batch
- Using Rui's mapped TPM data, performed analysis using Fluidigmn's Singular software:
- Identified outliers based on log2 mapping rates and trimmed genes with TPM less than 2 (LOD1)
- Performed pairwise comparison of each run:
- Performed PCA analysis on each run:
- Can see that none of the conditions affected the gene expression profiles of the captured nuclei and all nuclei retained similar PCA distributions across conditions