some updates and fixes

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sicegar
 Type: Package
 Title: Analysis of Single-Cell Viral Growth Curves
-Version: 0.1
+Version: 0.1.0.0000
 Date: 2015-12-17
 Authors@R: c( person("M. Umut", "Caglar", role = c("aut", "cre"), email =
     "[email protected]"), person("Claus O.", "Wilke", role = c("aut"), email =

R/categorize.R

@@ -186,7 +186,7 @@ categorize<-
            parameterVectorDoubleSigmoidal,
            threshold_line_slope_parameter=0.01,
            threshold_intensity_interval=0.1,
-           threshold_minimum_for_intensity_maximum=0.4,
+           threshold_minimum_for_intensity_maximum=0.3,
            threshold_difference_AIC=0,
            threshold_lysis_finalAsymptoteIntensity=0.75,
            threshold_AIC=-10)
@@ -376,7 +376,7 @@ categorize_nosignal<-
   function(parameterVectorLinear,
            threshold_line_slope_parameter=0.01,
            threshold_intensity_interval=0.1,
-           threshold_minimum_for_intensity_maximum=0.4)
+           threshold_minimum_for_intensity_maximum=0.3)
   {
     #************************************************
     # First Part Define NA

R/doublesigmoidalFitFunctions.R

@@ -475,7 +475,7 @@ f_mid2_doublesigmoidal <- function(parameterDf){
             B2=parameterDf$slope2_Estimate ,
             L=parameterDf$midPointDistance_Estimate);
 
-  mid2x <- stats::uniroot(f0mid, interval=c(argumentt,max_x*(3)),
+  mid2x <- stats::uniroot(f0mid, interval=c(argumentt,max_x*(2)),
                           tol=0.0001,
                           B1=parameterDf$slope1_Estimate,
                           M1=parameterDf$midPoint1_Estimate,

Readme.md

@@ -2,4 +2,4 @@
 
 Package for single-cell virology.
 
-We need some more description here, small change v2.
+the package aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal and double sigmoidal curves on to time vs intensity data. The all the fits together used to make decision between sigmoidal, double sigmoidal, no signal or ambiguous. No signal means the intensity do not reach to a high enough point or do not change at all. Sigmoidal means intensity start from a small number than climb to a maximum. Double sigmoidal means intensity start from a small number, climb to a maximum then starts to decay. After the decision between those four options, algorithm gives sigmoidal (or double sigmoidal) associated parameter values that quantifies the time intensity curve.

inst/doc/Introduction.html

@@ -12,7 +12,7 @@
 
 <meta name="author" content="Vignette Author" />
 
-<meta name="date" content="2017-03-10" />
+<meta name="date" content="2017-03-15" />
 
 <title>Vignette Title</title>
 
@@ -70,7 +70,7 @@
 
 <h1 class="title toc-ignore">Vignette Title</h1>
 <h4 class="author"><em>Vignette Author</em></h4>
-<h4 class="date"><em>2017-03-10</em></h4>
+<h4 class="date"><em>2017-03-15</em></h4>
 
 
 

inst/doc/double_sigmoidal_vignette.R

@@ -0,0 +1,98 @@
+## ----setup, include=FALSE------------------------------------------------
+knitr::opts_chunk$set(echo = TRUE)
+
+## ----install packages, echo=FALSE, warning=FALSE, results='hide',message=FALSE----
+
+###*****************************
+# INITIAL COMMANDS TO RESET THE SYSTEM
+rm(list = ls())
+if (is.integer(dev.list())){dev.off()}
+cat("\014")
+seedNo=14159
+set.seed(seedNo)
+###*****************************
+
+###*****************************
+require("sicegar")
+require("dplyr")
+require("ggplot2")
+###*****************************
+
+## ----generate data-------------------------------------------------------
+time=seq(3,24,0.5)
+
+#simulate intensity data and add noise
+noise_parameter=0.1
+intensity_noise=stats::runif(n = length(time),min = 0,max = 1)*noise_parameter
+intensity=doublesigmoidalFitFormula(time,
+                                    finalAsymptoteIntensity=.3,
+                                    maximum=4,
+                                    slope1=1,
+                                    midPoint1=7,
+                                    slope2=1,
+                                    midPointDistance=8)
+intensity=intensity+intensity_noise
+
+dataInput=data.frame(intensity=intensity,time=time)
+
+## ----time normalization, eval=FALSE--------------------------------------
+#  timeRatio=max(timeData); timeData=timeData/timeRatio
+
+## ----intensity normalization, eval=FALSE---------------------------------
+#  intensityMin = min(dataInput$intensity)
+#  intensityMax = max(dataInput$intensity)
+#  intensityRatio = intensityMax - intensityMin
+#  
+#  intensityData=dataInput$intensity-intensityMin
+#  intensityData=intensityData/intensityRatio
+
+## ----normalize_data------------------------------------------------------
+normalizedInput = sicegar::normalizeData(dataInput = dataInput, 
+                                         dataInputName = "Sample001")
+
+## ----normalized_data_output----------------------------------------------
+head(normalizedInput$timeIntensityData) # the normalized time and intensity data
+print(normalizedInput$dataScalingParameters) # the normalization parameters that is needed to go back to original scale
+print(normalizedInput$dataInputName) # a useful feature to track the sample in all the process
+
+## ----plot raw and normal data, echo=FALSE, fig.height=4, fig.width=8-----
+dataInput %>% dplyr::mutate(process="raw")->dataInput2
+normalizedInput$timeIntensityData %>%
+  dplyr::mutate(process="normalized")->timeIntensityData2
+dplyr::bind_rows(dataInput2,timeIntensityData2) -> combined
+combined$process <- factor(combined$process, levels = c("raw","normalized"))
+
+ggplot2::ggplot(combined,aes(x=time, y=intensity))+
+  ggplot2::facet_wrap(~process, scales = "free")+
+  ggplot2::geom_point()
+
+## ----doublesigmoidalfit_data---------------------------------------------
+parameterVector<-sicegar::doublesigmoidalFitFunction(normalizedInput,tryCounter=2)
+
+# Where tryCounter is a tool usually provided by sicegar::fitFunction when the sicegar::sigmoidalFitFunction is called from sicegar::fitFunction. 
+
+# If tryCounter==1 it took the  start position given by sicegar::fitFunction
+# If tryCounter!=1 it generates a random start position from given interval
+
+## ----parameter vector----------------------------------------------------
+print(t(parameterVector))
+
+## ----plot raw data and fit, fig.height=4, fig.width=8--------------------
+intensityTheoretical=
+  sicegar::doublesigmoidalFitFormula(
+    time,
+    finalAsymptoteIntensity=parameterVector$finalAsymptoteIntensity_Estimate,
+    maximum=parameterVector$maximum_Estimate,
+    slope1=parameterVector$slope1_Estimate,
+    midPoint1=parameterVector$midPoint1_Estimate,
+    slope2=parameterVector$slope2_Estimate,
+    midPointDistance=parameterVector$midPointDistance_Estimate)
+
+comparisonData=cbind(dataInput,intensityTheoretical)
+
+require(ggplot2)
+ggplot2::ggplot(comparisonData)+
+  ggplot2::geom_point(aes(x=time, y=intensity))+
+  ggplot2::geom_line(aes(x=time,y=intensityTheoretical))+
+  ggplot2::expand_limits(x = 0, y = 0)
+