How can I get normalized counts with ImpulseDE2?
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@037ddc51
Last seen 7 months ago
Japan

Hi, I am trying RNA-seq analysis with ImpluseDE2. As a next step, I want to make a plot of normalized counts for each genes I am interested in, or make do co-expression analysis with metabolome data. How can I get the normalized count data for each gene? And, I don't understand how to use "Timecatg" in dfannotation. How should I set this parameter?

Any advice you could give would be much appreciated. I apologize if my English is difficult to understand.

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I also have similar issue. I think the best way to do this is use plotHeatmap function or rather its truncated version. I paste the code below:

objectImpulseDE2 <- objectImpulseDE2 # object, which is an output from runImpulseDE2 function
strCondition  <- "case"
boolIdentifyTransients <- TRUE # TRUE or FALSE, dependent on your analysis, it should work in both cases
scaQThres <- 0.05 # adjusted pvalue threshold for your analysis 

#for some unknown reasons I have to define evalImpulse_comp function, which should be already defined

evalImpulse_comp <- function(vecImpulseParam, vecTimepoints) 
{
   vecImpulseValue <- sapply(vecTimepoints, function(t) {
      (1/vecImpulseParam[3]) * (vecImpulseParam[2] + (vecImpulseParam[3] - 
        vecImpulseParam[2]) * (1/(1 + exp(-vecImpulseParam[1] * 
        (t - vecImpulseParam[5]))))) * (vecImpulseParam[4] + 
        (vecImpulseParam[3] - vecImpulseParam[4]) * (1/(1 + 
            exp(vecImpulseParam[1] * (t - vecImpulseParam[6])))))
   })
   vecImpulseValue[vecImpulseValue < 10^(-10)] <- 10^(-10)
   return(vecImpulseValue)
}

#now its time to define our function of interest, which is a simple truncation of plotHeatmap
get_norm_count_table <- function(objectImpulseDE2, strCondition, boolIdentifyTransients, scaQThres)
{
dfAnnot <- get_dfAnnotationProc(obj = objectImpulseDE2)
scaNGenes <- dim(get_matCountDataProc(obj = objectImpulseDE2))[1]
vecSignificantIDs <- rownames(objectImpulseDE2$dfImpulseDE2Results[!is.na(objectImpulseDE2$dfImpulseDE2Results$padj) & 
    objectImpulseDE2$dfImpulseDE2Results$padj < scaQThres, 
    ])
vecTimePointsToEval <- sort(unique(dfAnnot$Time), decreasing = FALSE)
scaNTPtoEvaluate <- length(vecTimePointsToEval)
matImpulseValue <- do.call(rbind, lapply(vecSignificantIDs, 
    function(x) {
        evalImpulse_comp(vecImpulseParam = get_lsModelFits(obj = objectImpulseDE2)[[strCondition]][[x]]$lsImpulseFit$vecImpulseParam, 
            vecTimepoints = vecTimePointsToEval)
    }))
rownames(matImpulseValue) <- vecSignificantIDs
matidxMaxTimeSort <- t(apply(matImpulseValue, 1, function(genevalues) {
    sort(genevalues, decreasing = TRUE, index.return = TRUE)$ix
}))
vecMaxTime <- vecTimePointsToEval[matidxMaxTimeSort[, 1]]
matidxMinTimeSort <- t(apply(matImpulseValue, 1, function(genevalues) {
    sort(genevalues, decreasing = FALSE, index.return = TRUE)$ix
}))
if (boolIdentifyTransients) {
    vecidxTransient <- which(objectImpulseDE2$dfImpulseDE2Results[vecSignificantIDs, 
        ]$isTransient)
    vecidxMonotonous <- which(objectImpulseDE2$dfImpulseDE2Results[vecSignificantIDs, 
        ]$isMonotonous)
    if (length(vecidxMonotonous) == 1) {
        matImpulseMonot <- t(matImpulseValue[vecidxMonotonous, 
            ])
    }
    else {
        matImpulseMonot <- matImpulseValue[vecidxMonotonous, 
            ]
    }
    vecboolMonotonousUp <- apply(matImpulseMonot, 1, function(gene) {
        gene[1] < gene[scaNTPtoEvaluate]
    })
    vecboolMonotonousDown <- !vecboolMonotonousUp
    vecidxMonotonousUp <- vecidxMonotonous[vecboolMonotonousUp]
    vecidxMonotonousUpSort <- vecidxMonotonousUp[do.call(order, 
        as.data.frame(matidxMinTimeSort[vecidxMonotonousUp, 
            1]))]
    vecidxMonotonousDown <- vecidxMonotonous[vecboolMonotonousDown]
    vecidxMonotonousDownSort <- vecidxMonotonousDown[do.call(order, 
        as.data.frame(matidxMinTimeSort[vecidxMonotonousDown, 
            1]))]
    if (length(vecidxTransient) == 1) {
        matImpulseTransient <- t(matImpulseValue[vecidxTransient, 
            ])
    }
    else {
        matImpulseTransient <- matImpulseValue[vecidxTransient, 
            ]
    }
    vecboolTransientValley <- apply(matImpulseTransient, 
        1, function(genevalues) {
            boolValley <- any(genevalues[2:(scaNTPtoEvaluate - 
              1)] < genevalues[1] & genevalues[2:(scaNTPtoEvaluate - 
              1)] < genevalues[scaNTPtoEvaluate])
            return(boolValley)
        })
    vecboolTransientPeak <- !vecboolTransientValley
    vecidxTransientPeak <- vecidxTransient[vecboolTransientPeak]
    vecidxTransientPeakSort <- vecidxTransientPeak[do.call(order, 
        as.data.frame(matidxMaxTimeSort[vecidxTransientPeak, 
            1]))]
    vecidxTransientValley <- vecidxTransient[vecboolTransientValley]
    vecidxTransientValleySort <- vecidxTransientValley[do.call(order, 
        as.data.frame(matidxMinTimeSort[vecidxTransientValley, 
            1]))]
    vecidxAllSort <- c(vecidxMonotonousUpSort, vecidxMonotonousDownSort, 
        vecidxTransientPeakSort, vecidxTransientValleySort)
    vecTrajectoryType <- c(rep("up", length(vecidxMonotonousUpSort)), 
        rep("down", length(vecidxMonotonousDownSort)), rep("*up", 
            length(vecidxTransientPeakSort)), rep("*down", 
            length(vecidxTransientValleySort)))
    lsvecGeneGroups <- list(transition_up = vecSignificantIDs[vecidxMonotonousUpSort], 
        transition_down = vecSignificantIDs[vecidxMonotonousDownSort], 
        transient_up = vecSignificantIDs[vecidxTransientPeakSort], 
        transient_down = vecSignificantIDs[vecidxTransientValleySort])
}
else {
    vecidxAllSort <- sort(vecMaxTime, decreasing = FALSE, 
        index.return = TRUE)$ix
    vecTrajectoryType <- rep(" ", length(vecidxAllSort))
    lsvecGeneGroups <- list(all = vecSignificantIDs[vecidxAllSort])
}
vecUniqueTP <- unique(dfAnnot$Time)
vecSizeFactors <- computeNormConst(matCountDataProc = get_matCountDataProc(obj = objectImpulseDE2), 
    vecSizeFactorsExternal = get_vecSizeFactors(obj = objectImpulseDE2))
matSizefactors <- matrix(vecSizeFactors, nrow = length(vecSignificantIDs), 
    ncol = dim(get_matCountDataProc(obj = objectImpulseDE2))[2], 
    byrow = TRUE)
matDataNorm <- get_matCountDataProc(obj = objectImpulseDE2)[vecSignificantIDs, 
    ]/matSizefactors
matDataHeat <- do.call(cbind, lapply(vecUniqueTP, function(tp) {
    vecidxCols <- which(dfAnnot$Time %in% tp)
    if (length(vecidxCols) > 1) {
        return(rowMeans(matDataNorm[, vecidxCols], na.rm = TRUE))
    }
    else {
        return(matDataNorm[, vecidxCols])
    }
}))


}

#now you can run your analysis
get_norm_count_table(objectImpulseDE2, strCondition, boolIdentifyTransients, scaQThres)
#your results are in matDataNorm variable
my_norm_counts <-  matDataNorm

You can improve my solution in many ways (e.g. by adding 'return matDataNorm' at the end of function), but I see that you post the issue ~half year ago, so I put here first working solution. Hopefuly it will help.

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