Matt:LabNotes/2015-4-14
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Normalize DARTFISH Counts with in vitro cDNA Capture[edit]
Starting Files[edit]
- V4 and V7 in vitro capture reads mapped to reference probelist
- CountReadsPer_Gene_Probe.pl
- CA12kNov2014_V4cDNA_R1_H1H2_sorted_filtered_Genecounts.txt
- CA12kNov2014_V4cDNA_R1_H1H2_sorted_filtered_Probecounts.txt
- CA12kNov2014_V7cDNA_R1_H1H2_sorted_filtered_Genecounts.txt
- CA12kNov2014_V7cDNA_R1_H1H2_sorted_filtered_Probecounts.txt
- Get FPKM from UHRR RNA-Seq: genome-miner2:/media/LTS_33T/RL_LTS33T/20140510_20Samples_UHRR_RefRNA_2014603Expt143HiSeqRapid/1-UHRR-10pg-rep1_S1_mapped/genes.fpkm_tracking
Normalize DARTFISH Counts[edit]
- Convert genes.fpkm_tracking Ensembl Gene IDs to HGNC Symbols
- Normalize in vitro cDNA counts by dividing each gene by RNA-Seq FPKM
- Normalize DARTFISH counts by normalized in vitro cDNA counts
- Correlate with RNA-Seq, BeadArray, and FISSEQ
Get FPKM for V4 and V7 genes[edit]
- Files in "Dropbox\GradZhangLab\CA12k_Nov2014\V4_CaptureAnalysis"
- Converted ENSG genes from genes.fpkm_tracking to HGNC with Biomart
- UHRRgenes.txt <- list of ENSG names
- Used excel to match FPKM values to 242 genes in V4 and V7 probe sets
- V4V7genes_UHRRfpkm.txt <- HGNC names and FPKM values from UHRR RNA-Seq
- PTPRK and LAMA2 were not in UHRR RNA-Seq data
Divide in vitro cDNA Counts by FPKM[edit]
- Workspace: Dropbox\GradZhangLab\DecodeData\ReferenceData\.RData
setwd("C:/Users/Matt/Dropbox/GradZhangLab/CA12k_Nov2014/V4_CaptureAnalysis") invitro_cV4_GeneCounts <- read.table("CA12kNov2014_V4cDNA_R1_H1H2_sorted_filtered_Genecounts.txt", header = FALSE, sep = "", row.names = 1) colnames(invitro_cV4_GeneCounts) <- c("invitro_cV4") setwd("C:/Users/Matt/Dropbox/GradZhangLab/CA12k_Nov2014/V7_CaptureAnalysis") invitro_cV7_GeneCounts <- read.table("CA12kNov2014_V7cDNA_R1_H1H2_sorted_filtered_Genecounts.txt", header = FALSE, sep = "", row.names = 1) colnames(invitro_cV7_GeneCounts) <- c("invitro_cV7") setwd("C:/Users/Matt/Dropbox/GradZhangLab/CA12k_Nov2014/V4_CaptureAnalysis") UHRR_FPKM <- read.table("V4V7genes_UHRRfpkm.txt", header = FALSE, sep = "", row.names = 1) colnames(UHRR_FPKM) <- c("UHRR_FPKM") setwd("C:/Users/Matt/Dropbox/GradZhangLab/DecodeData/ReferenceData") V4V7_GeneCounts <- transform(merge(V4V7_GeneCounts,invitro_cV4_GeneCounts,by=0,all.x=TRUE,all.y=FALSE),row.names=Row.names,Row.names=NULL) V4V7_GeneCounts <- transform(merge(V4V7_GeneCounts,invitro_cV7_GeneCounts,by=0,all.x=TRUE,all.y=FALSE),row.names=Row.names,Row.names=NULL) V4V7_GeneCounts <- transform(merge(V4V7_GeneCounts,UHRR_FPKM,by=0,all.x=TRUE,all.y=FALSE),row.names=Row.names,Row.names=NULL) V4V7_GeneCounts$invitro_cV4_Norm <- V4V7_GeneCounts$invitro_cV4 / V4V7_GeneCounts$UHRR_FPKM V4V7_GeneCounts$invitro_cV7_Norm <- V4V7_GeneCounts$invitro_cV7 / V4V7_GeneCounts$UHRR_FPKM V4V7_GeneCounts$loginvitro_cV4_Norm <- log(V4V7_GeneCounts$invitro_cV4_Norm) V4V7_GeneCounts$loginvitro_cV7_Norm <- log(V4V7_GeneCounts$invitro_cV7_Norm) V4V7_GeneCounts$loginvitro_cV4_Norm[which(!is.finite(V4V7_GeneCounts$loginvitro_cV4_Norm))] <- NA V4V7_GeneCounts$loginvitro_cV7_Norm[which(!is.finite(V4V7_GeneCounts$loginvitro_cV7_Norm))] <- NA
Divide DARTFISH Counts by Normalized in vitro cDNA Counts[edit]
- DARTFISH and data from other methods same as from: Matt:LabNotes/2015-4-8
V4V7_GeneCounts$FibV4_Normv2 <- V4V7_GeneCounts$FibV4_Sum / V4V7_GeneCounts$invitro_cV4_Norm V4V7_GeneCounts$BA8V4_Normv2 <- V4V7_GeneCounts$BA8V4_Sum / V4V7_GeneCounts$invitro_cV4_Norm V4V7_GeneCounts$FibV7_Normv2 <- V4V7_GeneCounts$FibV7_Sum / V4V7_GeneCounts$invitro_cV7_Norm V4V7_GeneCounts$logFibV4_Normv2 <- log(V4V7_GeneCounts$FibV4_Normv2) V4V7_GeneCounts$logBA8V4_Normv2 <- log(V4V7_GeneCounts$BA8V4_Normv2) V4V7_GeneCounts$logFibV7_Normv2 <- log(V4V7_GeneCounts$FibV7_Normv2) V4V7_GeneCounts$logFibV4_Normv2[which(!is.finite(V4V7_GeneCounts$logFibV4_Normv2))] <- NA V4V7_GeneCounts$logBA8V4_Normv2[which(!is.finite(V4V7_GeneCounts$logBA8V4_Normv2))] <- NA V4V7_GeneCounts$logFibV7_Normv2[which(!is.finite(V4V7_GeneCounts$logFibV7_Normv2))] <- NA
Linear Regressions[edit]
- Compare to regressions with gDNA in vitro normalized DARTFISH counts: Matt:LabNotes/2015-4-8
Gene Counts: V4 vs V7 in vitro Capture[edit]
- Previously (left/top fig.) very different gene count distributions between V4 and V7 caused the correlation between V4 and V7 DARTFISH gene counts in PGP1f to have no correlation
- Now (right/bottom fig.) gene count between V4 and V7 in vitro capture is much better, but maybe only because both were divided by same FPKM values
lm(loginvitro_cV7_Norm~loginvitro_cV4_Norm, data = V4V7_GeneCounts, na.action=na.omit)
File:Regression invitrocounts V4 vs V7.jpeg File:Regression NormalizedcDNA invitrocounts V4 vs V7.png
PGP1f: V4 vs V7 (cDNA invitro normalized)[edit]
File:PGP1f log cDNAnorm V4 vs V7.png
- Much better correlation than when DARTFISH counts normalized by gDNA in vitro counts
File:PGP1f log norm V4 vs V7.jpeg
PGP1f vs BA8 (V4, cDNA invitro normalized)[edit]
File:V4 log cDNAnorm PGP1f vs BA8.png
- Such high correlation between different samples is not a positive result...
PGP1f: DARTFISH (V4, cDNA invitro normalized) vs RNA-Seq[edit]
File:PGP1fV4 logDARTFISH cDNAnorm vs logRNASeq regression.png
PGP1f: DARTFISH (V7, cDNA invitro normalized) vs RNA-Seq[edit]
File:PGP1fV7 logDARTFISH cDNAnorm vs logRNASeq regression.png
- V7 is improved over gDNA invitro normalization (used to be negatively correlated) but still worse than V4
PGP1f: DARTFISH (V4, cDNA invitro normalized) vs RNA-Seqv2[edit]
File:PGP1fV4 logDARTFISH cDNAnorm vs logRNASeqv2 regression.png
PGP1f: DARTFISH (V4, cDNA invitro normalized) vs BeadArray[edit]
- Using: FPKM for gene = max FPKM of gene's isoforms
File:PGP1fV4 logDARTFISH cDNAnorm vs logBeadArrayMax regression.png
PGP1f: DARTFISH (V4, cDNA invitro normalized) vs FISSEQ[edit]
File:PGP1fV4 logDARTFISH cDNAnorm vs logFISSEQv1 regression.png
PGP1f: DARTFISH (V4, cDNA invitro normalized) vs FISSEQv2[edit]
File:PGP1fV4 logDARTFISH cDNAnorm vs logFISSEQv2 regression.png
BA8: DARTFISH vs RNA-Seq[edit]
File:BA8 logDARTFISH cDNAnorm vs logRNASeqBulkT regression.png
- Got worse from gDNA in vitro normalization: R^2 = 0.2