diff --git a/.gitignore b/.gitignore
index 74eacbfdda55345c05d3f29b0c55db02b45babc6..fa9ab1fcec34d2887fd38c4119bc31bfd3f714dc 100755
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@ WR/
 *~
 temp_png.png
 Rplots.pdf
+stress/*
diff --git a/README.md b/README.md
index cfb0bb098f004ff6bb307f4e6d24454ab489540e..2120f0f82670840d8938e51d3ff8c445331e2f6a 100755
--- a/README.md
+++ b/README.md
@@ -11,8 +11,10 @@ Determining cellular heterogeneity in the human prostate with single-cell RNA se
         * <a href="https://orcid.org/0000-0002-0746-927X" target="orcid.widget" rel="noopener noreferrer" style="vertical-align:top;"><img src="https://orcid.org/sites/default/files/images/orcid_16x16.png" style="width:1em;margin-right:.5em;" alt="ORCID iD icon">orcid.org/0000-0002-0746-927X</a>
         * PI Email: [douglas.strand@utsouthwestern.edu](mailto:douglas.strand@utsouthwestern.edu)
 * **ANALYZED DATA FOR QUERYING AT: [StrandLab.net](http://strandlab.net/analysis.php)**
-* **Raw data at: [GEO (scRNA-Seq)](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117403) & [GEO (popRNA-Seq)](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117271) & [GenitoUrinary Development Molecular Anatomy Project (GUDMAP)]("https://doi.org/10.25548/W-R8CM")**
-* **Publication at: PENDING**
+* **Raw data at: [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120716) & [GenitoUrinary Development Molecular Anatomy Project (GUDMAP)]("https://doi.org/10.25548/W-R8CM")**
+* **Publication at:**
+    * Cell Reports: PENDING
+    * [BioRxiv](https://www.biorxiv.org/content/early/2018/10/15/439935)
 
 Data Analysis
 -------------
@@ -50,6 +52,10 @@ Data Analysis
         * run bash script [sc\_TissueMapper\-D17\_FACS.sh](https://git.biohpc.swmed.edu/StrandLab/sc-TissueMapper_Pr/blob/master/bash.scripts/sc_TissueMapper-D17_FACS.sh)
     * 3 Run on 2nd patient FACS samples
         * run bash script [sc\_TissueMapper\-D27\_FACS.sh](https://git.biohpc.swmed.edu/StrandLab/sc-TissueMapper_Pr/blob/master/bash.scripts/sc_TissueMapper-D27_FACS.sh)
+    * 4 Run on several downsamples from 1 sample from 1st patient
+        * run bash script [sc\_TissueMapper\-DS\_D17.sh](https://git.biohpc.swmed.edu/StrandLab/sc-TissueMapper_Pr/blob/master/bash.scripts/sc_TissueMapper-DS_D17.sh)
+    * 5 Aggregate and compare several downsamples from # 4
+        * run r script [sc\_TissueMapper\_RUN.DS\_D17.aggr.R](https://git.biohpc.swmed.edu/StrandLab/sc-TissueMapper_Pr/blob/master/r.scripts/sc_TissueMapper_RUN.DS_D17.aggr.R)
 * **Pipeline:**
     * Link cellranger count/aggr output to analysis
     * Create demultiplex file to add custom sample groups
@@ -84,8 +90,6 @@ Data Analysis
     * Lineage:
         * "DEG\_Epi_5FC.txt" DWS generated DEGs of epithelia from FACS population (bulk) RNA-sequencing
         * "DEG\_FMSt_5FC.txt" DWS generated DEGs of fibromuscular stroma from FACS population (bulk) RNA-sequencing
-        * "genes.deg.Epi.csv" DWS generated DEGs of epithelial cells from scRNA-Seq of 3 patient aggregate
-        * "genes.deg.St.csv" DWS generated DEGs of stromal cells from scRNA-Seq of 3 patient aggregate
     * Epithelia:
         * "DEG\_BE_5FC.txt" DWS generated DEGs of basal epithelia from FACS population (bulk) RNA-sequencing
         * "DEG\_LE_5FC.txt" DWS generated DEGs of luminal epithelia from FACS population (bulk) RNA-sequencing
@@ -118,4 +122,4 @@ Data Analysis
         * "c2.all.v6.1.symbols.gmt" MSigDB C2 Curated Gene Sets [**MSigDB C2**](http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=C2)
         * "c2.cp.kegg.v6.1.symbols" MSigDB C2 KEGG Gene Subsets [**KEGG**](http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=CP:KEGG)
         * "c5.all.v6.1.symbols.gmt" MSigDB C5 Gene Ontology Gene Sets [**MSigDB C5**](http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=C5)
-        * "c5.bp.v6.1.symbols.gmt" MSigDB C5 Gene Ontology Biological Processes Gene Subsets [**GO BP**](http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=BP)
\ No newline at end of file
+        * "c5.bp.v6.1.symbols.gmt" MSigDB C5 Gene Ontology Biological Processes Gene Subsets [**GO BP**](http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=BP)
diff --git a/bash.scripts/sc_TissueMapper-D17_FACS.sh b/bash.scripts/sc_TissueMapper-D17_FACS.sh
index a1b9e80f4b3ce0e426aa055443adfa3bf91f95cd..3bfd7e87712b157627bcf22d4c34a30fab7bea4c 100644
--- a/bash.scripts/sc_TissueMapper-D17_FACS.sh
+++ b/bash.scripts/sc_TissueMapper-D17_FACS.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name R_FullAnalysis
+#SBATCH --job-name R_FullAnalysis.D17FACS
 #SBATCH -p 256GB,256GBv1,384GB
 #SBATCH -N 1
 #SBATCH -t 7-0:0:0
@@ -10,4 +10,6 @@
 
 module load R/3.4.1-gccmkl
 
+sh ./sc_LinkData.sh D17_FACS
+
 Rscript ../r.scripts/sc-TissueMapper_RUN.D17_FACS.R
diff --git a/bash.scripts/sc_TissueMapper-D27_FACS.sh b/bash.scripts/sc_TissueMapper-D27_FACS.sh
index 492d870914d32d72cbfe82f9a201fee8df210ae4..8c2fa2d2cfb15019e5200a18adf4bbed0ccbd519 100644
--- a/bash.scripts/sc_TissueMapper-D27_FACS.sh
+++ b/bash.scripts/sc_TissueMapper-D27_FACS.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name R_FullAnalysis
+#SBATCH --job-name R_FullAnalysis.D27FACS
 #SBATCH -p 256GB,256GBv1,384GB
 #SBATCH -N 1
 #SBATCH -t 7-0:0:0
@@ -10,4 +10,6 @@
 
 module load R/3.4.1-gccmkl
 
+sh ./sc_LinkData.sh D27_FACS
+
 Rscript ../r.scripts/sc-TissueMapper_RUN.D27_FACS.R
diff --git a/bash.scripts/sc_TissueMapper-DS_D17.sh b/bash.scripts/sc_TissueMapper-DS_D17.sh
index 198685b1bb589757f0f23c097dea4d6f9d7f4a8b..34e565256585ad6cfaafce1ce3964d17700d2378 100644
--- a/bash.scripts/sc_TissueMapper-DS_D17.sh
+++ b/bash.scripts/sc_TissueMapper-DS_D17.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
-#SBATCH --job-name R_FullAnalysis
-#SBATCH -p 256GB,256GBv1
+#SBATCH --job-name R_FullAnalysis.DS
+#SBATCH -p 256GB,256GBv1,384GB
 #SBATCH -N 1
 #SBATCH -t 7-0:0:0
 #SBATCH -o job_%j.out
diff --git a/bash.scripts/sc_TissueMapper-Pd.sh b/bash.scripts/sc_TissueMapper-Pd.sh
index c75360c8ed4efff44f2eb37a5bee3eb9f152e5b5..82aec66333499c727c9fd1db5b097a9892a29aa0 100644
--- a/bash.scripts/sc_TissueMapper-Pd.sh
+++ b/bash.scripts/sc_TissueMapper-Pd.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name R_FullAnalysis
+#SBATCH --job-name R_FullAnalysis.Pd
 #SBATCH -p 256GB,256GBv1,384GB
 #SBATCH -N 1
 #SBATCH -t 7-0:0:0
@@ -10,4 +10,7 @@
 
 module load R/3.4.1-gccmkl
 
-Rscript ../r.scripts/sc-TissueMapper_RUN.Pd.R
\ No newline at end of file
+sh ./sc_LinkData.sh Pd
+
+Rscript ../r.scripts/sc-TissueMapper_RUN.Pd.R
+Rscript ../r.scripts/sc-TissueMapper_RUN.Pd.StressCompare.R
diff --git a/genesets/SupTab6_Krt13_Hillock.csv b/genesets/SupTab6_Krt13_Hillock.csv
index 1fa9fc8244896fd65a42b5a8ee323db9f1670e93..d4ac03268d1128908dd915a783e2500a7a655fc5 100644
--- a/genesets/SupTab6_Krt13_Hillock.csv
+++ b/genesets/SupTab6_Krt13_Hillock.csv
@@ -1,9 +1,3 @@
-Supplementary Table 6,,,,
-Related to Extended Data 4g:  Krt4+/Krt13+ hillock-associated genes from 3' droplet-based scRNA-seq,,,,
-Thresholds: all genes shown,,,,
-,,,,
-,,,,
-,,,,
 Gene,log2 fold-change (means),log2 fold-change (MAST),p,FDR
 Ltf,4.371203488,1.997240249,2.12E-49,2.80E-47
 Krt4,2.883475949,2.715058937,3.95E-132,6.28E-129
diff --git a/genesets/genes.deg.Epi.csv b/genesets/genes.deg.Epi.csv
index 56b5c01aa34c17f4dcb75276f990a88da8e96708..ae0719ad6274e4e53a6195b27171b6caf4170570 100644
--- a/genesets/genes.deg.Epi.csv
+++ b/genesets/genes.deg.Epi.csv
@@ -1,101 +1,829 @@
 "","p_val","avg_logFC","pct.1","pct.2","p_val_adj"
-"KRT17",0,2.64673644764602,0.91,0.38,0
-"TACSTD2",0,2.58231159715341,0.981,0.282,0
-"KRT19",0,2.45083620114524,0.872,0.185,0
-"CLDN4",0,2.33798655274461,0.83,0.229,0
-"S100A2",0,2.14736694057859,0.824,0.336,0
-"SFN",0,2.14188708633312,0.849,0.126,0
-"KRT15",0,2.13730992361203,0.705,0.122,0
-"KRT8",0,1.98122977079159,0.878,0.156,0
-"SLPI",0,1.97212133924044,0.609,0.126,0
-"KRT23",0,1.9475241025607,0.647,0.109,0
-"KRT7",0,1.85832624877284,0.762,0.135,0
-"WFDC2",0,1.82603184536208,0.706,0.142,0
-"AQP3",0,1.75707702047555,0.714,0.138,0
-"KLF5",0,1.72630607453798,0.808,0.128,0
-"KRT18",0,1.65532949882324,0.889,0.214,0
-"FXYD3",0,1.61927290958347,0.793,0.123,0
-"DST",0,1.61622963903937,0.608,0.203,0
-"KRT5",0,1.61473814532591,0.661,0.08,0
-"AZGP1",0,1.59595095398688,0.423,0.063,0
-"ERRFI1",0,1.5002166398309,0.671,0.207,0
-"PERP",0,1.43991512228113,0.913,0.305,0
-"ELF3",0,1.39422648108229,0.616,0.113,0
-"SPINT2",0,1.39239631839613,0.792,0.11,0
-"AREG",0,1.3786787337354,0.444,0.097,0
-"ACTG1",0,1.37293440601088,0.992,0.839,0
-"DEFB1",0,1.34503533365797,0.431,0.055,0
-"ANXA1",0,1.30604259801877,0.86,0.505,0
-"F3",0,1.30192501208712,0.549,0.139,0
-"S100A14",0,1.29343286078518,0.589,0.071,0
-"FHL2",0,1.26558560397518,0.705,0.222,0
-"CLDN7",0,1.23141144972301,0.523,0.056,0
-"CXCL17",0,1.21533791862415,0.375,0.051,0
-"PHLDA2",0,1.19530647532187,0.808,0.394,0
-"GADD45A",0,1.18964097065961,0.83,0.473,0
-"EZR",0,1.15296351442262,0.754,0.253,0
-"CLDN1",0,1.11410745865298,0.457,0.064,0
-"EDN1",0,1.10942157265544,0.394,0.057,0
-"EHF",0,1.08296266097339,0.531,0.042,0
-"SERPINB5",0,1.08108443063903,0.462,0.034,0
-"S100A16",0,1.0769615457348,0.858,0.416,0
-"LAMB3",0,1.07163577834265,0.532,0.055,0
-"HBEGF",0,1.06681378224294,0.522,0.201,0
-"LIMA1",0,1.0513260826661,0.647,0.19,0
-"ACTB",0,1.05079544839747,0.992,0.9,0
-"VAMP8",0,1.02255596752927,0.607,0.073,0
-"PLAU",0,1.01992439702576,0.555,0.196,0
-"TSTD1",0,1.01749694576366,0.602,0.078,0
-"S100A11",0,1.0047767484748,0.991,0.879,0
-"ANXA2",0,1.0000025381118,0.987,0.867,0
-"MPZL2",0,0.994372861783234,0.644,0.149,0
-"CEACAM1",0,0.98928633223492,0.501,0.086,0
-"ZG16B",0,0.979605923698475,0.429,0.036,0
-"CD9",0,0.939348912075797,0.955,0.613,0
-"STK17A",0,0.937434104556968,0.612,0.222,0
-"SLC14A1",0,0.921059272534142,0.443,0.052,0
-"HMGA1",0,0.882636420446482,0.838,0.494,0
-"ALDH1A3",0,0.876037021829772,0.475,0.13,0
-"EPCAM",0,0.870929113732984,0.478,0.045,0
-"GSTP1",0,0.869236837206919,0.935,0.801,0
-"IRF6",0,0.857811859218995,0.471,0.038,0
-"PRSS22",0,0.855291125992551,0.367,0.028,0
-"MAST4",0,0.847336115298592,0.678,0.235,0
-"TSPAN1",0,0.832731564261921,0.392,0.04,0
-"PRDX5",0,0.822551687733958,0.789,0.438,0
-"TNFRSF12A",0,0.808337370482687,0.892,0.483,0
-"PHLDA3",0,0.795531021308692,0.494,0.111,0
-"S100A6",0,0.784779708737913,0.981,0.977,0
-"JUP",0,0.784069000314477,0.488,0.074,0
-"IRF2BP2",0,0.769645482116694,0.557,0.19,0
-"ITGA2",0,0.76350554541392,0.595,0.199,0
-"TNFRSF21",0,0.762654414147515,0.432,0.067,0
-"LAMC2",0,0.762354398240465,0.432,0.097,0
-"ANXA11",0,0.756371185335363,0.605,0.21,0
-"SDC4",0,0.753410062764128,0.503,0.146,0
-"MAL2",0,0.75247074578316,0.355,0.044,0
-"HEBP2",0,0.743409923773958,0.673,0.332,0
-"SDC1",0,0.718054645280138,0.351,0.04,0
-"CD47",0,0.71273810568372,0.599,0.22,0
-"CXCL16",0,0.704014318224997,0.426,0.071,0
-"KLK11",0,0.702290293393313,0.352,0.037,0
-"PPDPF",0,0.70023197306758,0.851,0.544,0
-"FAM110C",4.68811670147243e-302,0.81952198692582,0.321,0.023,1.08998713309234e-297
-"LMO7",3.54562564288941e-287,0.776064729336601,0.34,0.045,8.24357961971787e-283
-"LCN2",1.29929397078246e-279,1.98321221152763,0.43,0.13,3.02085848206921e-275
-"GPX2",1.75911908897727e-269,0.849509542409469,0.325,0.043,4.08995188187215e-265
-"RAB11FIP1",3.69529627971261e-269,0.720818871919051,0.341,0.055,8.59156385033181e-265
-"HSPB1",7.16440246438237e-267,0.785961433480702,0.893,0.779,1.6657235729689e-262
-"HIST1H1C",1.61000939082964e-254,0.719287302119213,0.48,0.184,3.74327183367892e-250
-"AGR2",2.68285137014948e-231,1.0612067064208,0.295,0.044,6.23762943559755e-227
-"KRT14",1.00746316626082e-212,1.65359002160063,0.295,0.056,2.3423518615564e-208
-"MMP7",9.3259028722788e-212,1.30588565024517,0.278,0.042,2.16827241780482e-207
-"KRT13",2.33641834478255e-210,1.63851273006006,0.292,0.056,5.43217265161943e-206
-"KRT16",1.07784000202821e-200,1.19125071652985,0.258,0.034,2.50597800471559e-196
-"S100P",8.37182577735041e-198,1.57588848571193,0.35,0.117,1.94644949323397e-193
-"ZFP36L2",2.94915164836327e-188,0.715517638044633,0.652,0.404,6.85677758244461e-184
-"MTRNR2L1",4.92681915776347e-166,0.796880879549293,0.498,0.282,1.14548545418001e-161
-"CD24",5.22646762822138e-146,0.921382790745211,0.331,0.131,1.21515372356147e-141
-"DKK1",8.30819005061725e-110,0.756949927526965,0.291,0.115,1.93165418676851e-105
-"GDF15",5.94555679467146e-81,1.18807270039704,0.276,0.139,1.38234195476112e-76
-"MSMB",6.76126653486279e-16,2.27369721884818,0.289,0.234,1.5719944693556e-11
+"TACSTD2",0,2.57726810521126,0.981,0.283,0
+"KRT19",0,2.44562146964485,0.872,0.186,0
+"SFN",0,2.13021494592865,0.848,0.127,0
+"KRT8",0,1.9837405050329,0.878,0.157,0
+"KRT23",0,1.95445076616131,0.646,0.11,0
+"KRT7",0,1.86177468760064,0.762,0.136,0
+"KLF5",0,1.72260162192647,0.809,0.129,0
+"KRT18",0,1.6544614576196,0.89,0.215,0
+"ERRFI1",0,1.49533568339587,0.672,0.209,0
+"PERP",0,1.44080486636929,0.913,0.306,0
+"SPINT2",0,1.38942170425359,0.792,0.112,0
+"ACTG1",0,1.37557849121183,0.992,0.839,0
+"ANXA1",0,1.30498452127776,0.86,0.505,0
+"S100A14",0,1.28712178614601,0.59,0.072,0
+"PHLDA2",0,1.19620250051962,0.808,0.394,0
+"GADD45A",0,1.1861152086952,0.83,0.473,0
+"EZR",0,1.15418177850897,0.754,0.254,0
+"CLDN1",0,1.10334313888273,0.456,0.065,0
+"EHF",0,1.08194253599274,0.531,0.043,0
+"SERPINB5",0,1.07904301130031,0.461,0.035,0
+"S100A16",0,1.07684653478913,0.858,0.417,0
+"LAMB3",0,1.06782682958569,0.532,0.056,0
+"LIMA1",0,1.04952079132435,0.647,0.19,0
+"ACTB",0,1.0494752816782,0.992,0.901,0
+"VAMP8",0,1.01710573350913,0.607,0.075,0
+"PLAU",0,1.01552674096069,0.554,0.197,0
+"TSTD1",0,1.0148572359161,0.602,0.079,0
+"S100A11",0,1.00344925140879,0.991,0.879,0
+"ANXA2",0,1.00254254146233,0.987,0.867,0
+"MPZL2",0,0.992987408636894,0.644,0.15,0
+"CEACAM1",0,0.985666856487883,0.502,0.088,0
+"CD9",0,0.937054074376387,0.955,0.614,0
+"SLC14A1",0,0.923942509022319,0.443,0.052,0
+"HMGA1",0,0.87967774639976,0.837,0.496,0
+"EPCAM",0,0.871832676672806,0.479,0.046,0
+"GSTP1",0,0.867311315336782,0.935,0.801,0
+"IRF6",0,0.853500523687699,0.47,0.04,0
+"PRSS22",0,0.853268580398519,0.368,0.029,0
+"MAST4",0,0.844566497192092,0.679,0.236,0
+"TSPAN1",0,0.83070138740681,0.392,0.041,0
+"PRDX5",0,0.820128715701054,0.788,0.439,0
+"TNFRSF12A",0,0.809310940531004,0.892,0.484,0
+"PHLDA3",0,0.792509542897709,0.493,0.112,0
+"JUP",0,0.782177202932232,0.489,0.075,0
+"IRF2BP2",0,0.766578024043836,0.557,0.191,0
+"ITGA2",0,0.759258545939197,0.595,0.2,0
+"TNFRSF21",0,0.758548199528498,0.432,0.068,0
+"LAMC2",0,0.757675452654469,0.432,0.099,0
+"ANXA11",0,0.754104060669295,0.604,0.211,0
+"SDC4",0,0.749123333766016,0.503,0.147,0
+"CD47",0,0.711633499070774,0.599,0.221,0
+"CXCL16",0,0.702223168353051,0.426,0.072,0
+"CXADR",0,0.682647423492381,0.36,0.025,0
+"CDH1",0,0.670046795299146,0.377,0.039,0
+"TPM4",0,0.665286167181594,0.881,0.643,0
+"SH3YL1",0,0.650812954872004,0.425,0.068,0
+"DSP",0,0.648453714320144,0.411,0.077,0
+"CTSH",0,0.647592382451259,0.451,0.121,0
+"CHCHD10",0,0.646219329939855,0.548,0.204,0
+"BLVRB",0,0.644510207623783,0.571,0.212,0
+"GALNT3",0,0.643983905348075,0.362,0.039,0
+"RPL34",0,0.622230269679737,1,0.996,0
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+"MTRNR2L8",7.77389243163005e-124,0.305585033217038,0.842,0.706,1.80742999035399e-119
+"NABP1",4.65765081388007e-122,0.362419503139175,0.276,0.092,1.08290381422712e-117
+"CXCL8",1.67610870406359e-121,0.149246734645602,0.815,0.659,3.89695273694785e-117
+"TMEM123",1.10227178472538e-120,0.33572098458656,0.56,0.343,2.5627818994865e-116
+"CHMP2B",2.44948487874497e-120,0.338873224705538,0.539,0.318,5.69505234308205e-116
+"COX7C",9.9334302301262e-120,0.256653971686863,0.893,0.795,2.30952252850434e-115
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+"RNASET2",1.10908772816736e-114,0.29452557444868,0.26,0.086,2.57862896798911e-110
+"CSNK1A1",2.27561886040115e-114,0.356056937652695,0.73,0.56,5.29081385043267e-110
+"PMAIP1",4.81249001970807e-114,0.3716382333426,0.469,0.264,1.11890392958213e-109
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+"ATP5G1",9.29892327420357e-113,0.331641971586956,0.507,0.298,2.16199966125233e-108
+"APRT",3.90732407364075e-112,0.330982522866327,0.623,0.422,9.08452847121474e-108
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+"CSRP1",7.41570143576537e-109,0.250448473671537,0.448,0.235,1.72415058381545e-104
+"PABPC1",7.6937525540134e-109,0.260828641722664,0.879,0.782,1.78879746880812e-104
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+"COX6B1",2.46576923242244e-105,0.28946962692598,0.718,0.549,5.73291346538218e-101
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+"FBXO32",9.81621354205022e-102,0.438611437197471,0.288,0.12,2.28226964852668e-97
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+"CHMP2A",6.29554844226939e-100,0.283220413230164,0.57,0.368,1.46371501282763e-95
+"RPS13",6.59877584774612e-99,0.140870854520895,0.998,0.993,1.53421538460097e-94
+"ATPIF1",1.43198888857907e-96,0.289445209317411,0.419,0.229,3.32937416594634e-92
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+"MANBAL",9.29899309020212e-78,0.234688223142285,0.27,0.123,2.16201589347199e-73
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+"MTRNR2L12",3.00761795595235e-74,0.22788591057026,0.62,0.438,6.99271174758921e-70
+"HSBP1",4.55733659913653e-74,0.234420035730864,0.571,0.389,1.05958075929924e-69
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+"ACTR3",2.44901012246364e-72,0.280751202797236,0.59,0.431,5.69394853472797e-68
+"ACADVL",1.01802124031464e-71,0.250182607838765,0.619,0.457,2.36689938373154e-67
+"GLIPR1",1.13509928537266e-71,0.30531241017709,0.27,0.13,2.63910583849142e-67
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+"CCND1",3.92913578972544e-71,0.0775346611411778,0.383,0.219,9.13524071111164e-67
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+"DAAM1",1.33480553686993e-70,0.329526924480458,0.297,0.155,3.10342287322258e-66
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+"SRPK1",6.66401219245582e-69,0.242309899910213,0.319,0.173,1.54938283474598e-64
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+"ERGIC3",3.5917003209759e-68,0.230662240685471,0.469,0.306,8.35070324626896e-64
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+"HSP90AA1",8.6499850949843e-68,0.439046713482282,0.966,0.943,2.01112153458385e-63
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+"DRAP1",1.49785155599781e-65,0.337248183755489,0.497,0.354,3.4825048676949e-61
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+"DDT",1.09928624564124e-64,0.239041648575937,0.502,0.338,2.55584052111587e-60
+"NHP2",3.34636174580012e-64,0.24654901343226,0.544,0.379,7.78029105898528e-60
+"HMGN3",6.42084426702298e-64,0.209734092730123,0.363,0.21,1.49284629208284e-59
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diff --git a/genesets/genes.deg.LE.csv b/genesets/genes.deg.LE.csv
index da2020b38bba0b4d14c255766b80f6f9d9dc3dc0..a5c286f623e5c2418f62c4b7aac8d99673924d73 100644
--- a/genesets/genes.deg.LE.csv
+++ b/genesets/genes.deg.LE.csv
@@ -1,80 +1,72 @@
 "","p_val","avg_logFC","pct.1","pct.2","p_val_adj"
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-"PLA2G2A",0,2.81892272901388,0.444,0.02,0
-"KLK2",0,2.49353927858673,0.594,0.025,0
-"TFF1",0,2.15334059243419,0.329,0.041,0
-"AZGP1",0,2.0049490413152,0.897,0.345,0
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-"TSPAN8",0,1.82868329126073,0.741,0.077,0
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-"NEAT1",0,1.66342555320245,0.992,0.932,0
-"SEC11C",0,1.50139213870143,0.616,0.159,0
-"RDH11",0,1.43938695807039,0.54,0.1,0
-"CPE",0,1.42726881338742,0.609,0.195,0
-"AGR2",0,1.38739010258592,0.569,0.185,0
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-"TMPRSS2",0,1.17657374095478,0.598,0.1,0
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-"SMIM14",0,0.990064601095013,0.664,0.201,0
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-"MT2A",4.48785503840556e-60,1.21424445779221,0.712,0.623,1.04342629642929e-55
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+"CLDN3",0,1.52902244520835,0.678,0.129,0
+"NEAT1",0,1.51900228702682,0.992,0.94,0
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+"ZG16B",0,1.1068824310419,0.705,0.394,0
+"NPDC1",0,1.10185293373806,0.691,0.205,0
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+"FDPS",9.5545509588763e-102,0.772645903487662,0.412,0.241,2.22143309793874e-97
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+"GADD45B",1.11647864383533e-59,0.960832459862578,0.512,0.387,2.59581284691715e-55
diff --git a/genesets/genes.deg.St.csv b/genesets/genes.deg.St.csv
index 015986317ed1c70b827af9f5132112cc0b36f0b9..490ada983ba328c86cc9dd7d15e1ecf57080e8f5 100644
--- a/genesets/genes.deg.St.csv
+++ b/genesets/genes.deg.St.csv
@@ -1,159 +1,464 @@
 "","p_val","avg_logFC","pct.1","pct.2","p_val_adj"
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+"C5orf24",1.3884587022702e-189,0.418247821254093,0.267,0.104,3.22816648277821e-185
+"UBC",7.16397313935364e-189,0.613929016295546,0.954,0.933,1.66562375489972e-184
+"SNHG7",1.12084455710174e-185,0.573167869181974,0.484,0.297,2.60596359526154e-181
+"ETS1",2.79036356402128e-185,0.424541110210437,0.27,0.107,6.48759528634948e-181
+"ATP13A3",2.49810454762386e-183,0.485712243084163,0.367,0.184,5.80809307322548e-179
+"SNX3",8.94455579608059e-170,0.457943618152566,0.702,0.57,2.07960922258874e-165
+"MALAT1",1.72435381745405e-169,0.232290305506145,1,0.999,4.00912262558067e-165
+"LGMN",7.12162834798954e-165,0.380070940009376,0.275,0.119,1.65577859090757e-160
+"PTPN1",1.03201512112984e-164,0.565641436877844,0.308,0.147,2.39943515662688e-160
+"MAP1LC3B",1.33695433569871e-162,0.476701864312365,0.622,0.469,3.10841883049951e-158
+"DDX3Y",9.89009891124664e-162,0.446735551730621,0.408,0.23,2.29944799686484e-157
+"RHOA",1.72811532321374e-161,0.431835360092166,0.703,0.578,4.01786812647194e-157
+"C6orf48",8.38193112778112e-161,0.481195067412786,0.485,0.307,1.94879898720911e-156
+"FOSL2",2.81249941107179e-160,0.486207473882082,0.407,0.232,6.53906113074192e-156
+"PNPLA8",2.63573316492019e-158,0.496649846703486,0.467,0.292,6.12807960843943e-154
+"DEGS1",1.30809140633768e-156,0.372429681730519,0.267,0.114,3.04131251973511e-152
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+"FTH1",5.18676907380206e-156,0.625814155803589,0.999,1,1.20592380965898e-151
+"TNPO1",1.18654110823983e-155,0.435626872965411,0.305,0.147,2.7587080766576e-151
+"CHD1",4.30975887910181e-152,0.529026467530444,0.411,0.242,1.00201893939117e-147
+"SOX4",8.30739144163552e-151,0.559549900433759,0.394,0.218,1.93146851018026e-146
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+"NAP1L1",2.03035118225823e-149,0.391530632020795,0.831,0.778,4.72056649875038e-145
+"SPTBN1",2.06626381876957e-145,0.408676499222323,0.338,0.176,4.80406337863925e-141
+"TMEM70",2.99354434095741e-145,0.520532579636224,0.261,0.118,6.95999059272597e-141
+"GOLT1B",3.61462964261699e-145,0.367182362522714,0.255,0.112,8.40401391908451e-141
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+"PSMA7",7.59855005239599e-133,0.352385187254693,0.771,0.68,1.76666288718207e-128
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+"CLEC2B",2.29065734070759e-124,0.568705997474566,0.296,0.154,5.32577831714514e-120
+"SERPINH1",3.58670167932476e-120,0.249750487074965,0.443,0.256,8.33908140443006e-116
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+"ATF4",9.29423343825725e-118,0.372919258654619,0.696,0.593,2.16090927439481e-113
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+"TCEB1",5.98563509545964e-101,0.4435893125498,0.619,0.521,1.39166015969437e-96
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+"HNRNPA2B1",2.39247248450422e-97,0.292886291563817,0.86,0.825,5.56249852647231e-93
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+"ROCK1",6.3646664749093e-72,0.29982940951283,0.297,0.187,1.47978495541641e-67
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+"ARGLU1",1.24931599147722e-49,0.257417345095213,0.606,0.551,2.90465968018454e-45
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+"RSF1",3.3639206909261e-10,0.133662262286845,0.254,0.222,7.82111560640319e-06
+"BUD31",4.30142052260295e-10,0.138412701842224,0.391,0.368,1.00008027150519e-05
+"GGNBP2",4.30538547421204e-10,0.149905314563402,0.282,0.251,1.0010021227543e-05
+"HNRNPA1",4.5592679122725e-10,0.0827921667136893,0.853,0.857,1.06002978960336e-05
+"RAD21",6.42605127719827e-10,0.12010405151664,0.27,0.236,1.4940569219486e-05
+"RBPJ",7.64004227546234e-10,0.124125913658508,0.283,0.249,1.77630982904499e-05
+"NFKB1",9.91236750819679e-10,0.150458657894496,0.261,0.229,2.30462544565575e-05
+"ATRX",1.00930597389625e-09,0.135910265130966,0.324,0.296,2.34663638930879e-05
+"POLR2H",1.07730055306908e-09,0.120742334176016,0.286,0.252,2.50472378588561e-05
+"EIF4H",1.29497938155059e-09,0.156537673705705,0.394,0.374,3.01082706210513e-05
+"TFG",1.35938246981866e-09,0.147324231641414,0.336,0.311,3.16056424232839e-05
+"MESDC1",1.58225517849557e-09,0.186499980288537,0.278,0.251,3.67874329000221e-05
+"CELF1",2.39646827098635e-09,0.132406770691684,0.275,0.245,5.57178873004327e-05
+"ARL2",4.44095076552743e-09,0.152786399861516,0.268,0.242,0.000103252105298513
+"ERH",4.60173606247665e-09,0.125116770077499,0.518,0.508,0.000106990363452582
+"EGR1",4.65089302536169e-09,0.103060831193645,0.261,0.323,0.000108133262839659
+"GADD45GIP1",5.66309408870719e-09,0.116802096263936,0.474,0.457,0.000131666937562442
+"DHX36",6.02706446907949e-09,0.137203073953996,0.373,0.349,0.000140129248906098
+"RBMX",1.03576444284063e-08,0.130650578758283,0.418,0.399,0.000240815232960447
+"BRD2",1.39140610779406e-08,0.150434350854822,0.441,0.433,0.00032350192006212
+"ANP32B",2.50206031680006e-08,0.135382628676275,0.477,0.473,0.000581729023656013
+"HNRNPDL",2.8992940878236e-08,0.087443236851924,0.759,0.782,0.000674085875418987
+"LAMP2",3.12241833418149e-08,0.145236694688935,0.277,0.251,0.000725962262697197
+"TNFRSF1A",4.20970852152336e-08,0.143020297939488,0.356,0.334,0.000978757231254181
+"CCT3",5.94833588354247e-08,0.106423189520583,0.472,0.461,0.00138298809292362
+"UBE2A",6.08593579596259e-08,0.118284573149095,0.299,0.273,0.0014149800725613
+"CBX3",6.11828762087969e-08,0.126039783215699,0.312,0.285,0.00142250187185453
+"BCAP29",6.15882485606728e-08,0.0870423153787729,0.275,0.242,0.00143192677903564
+"PSMB1",6.51560916442557e-08,0.0918262490912156,0.632,0.637,0.00151487913072895
+"MED28",6.70430497608672e-08,0.123896485642612,0.331,0.309,0.00155875090694016
+"RBM39",7.55799993600076e-08,0.0873804082849208,0.727,0.746,0.00175723498512018
+"JUND",8.43581063835415e-08,0.128791941495486,0.509,0.499,0.00196132597341734
+"KIF5B",1.28419235000308e-07,0.125954332927409,0.467,0.466,0.00298574721375715
+"GPBP1",1.35959063046354e-07,0.119739113379385,0.526,0.528,0.00316104821582774
+"KLF4",1.43228710180173e-07,0.129761973992998,0.342,0.421,0.00333006751168903
+"OSTC",1.50054042305361e-07,0.134093925793119,0.364,0.346,0.00348875648359964
+"PPP4R2",2.44331761657338e-07,0.12446384511502,0.323,0.303,0.0056807134585331
+"NDUFS5",2.78024766864049e-07,0.110443892274219,0.672,0.691,0.00646407582958914
+"BRD4",3.58804361843802e-07,0.113895976181653,0.343,0.322,0.00834220141286839
+"AK2",4.92045169971043e-07,0.144321155883358,0.303,0.286,0.0114400502018267
+"GNG12",5.9323697207061e-07,0.116876775337362,0.286,0.263,0.0137927596006417
+"PAK2",8.47193044645296e-07,0.0990788036450452,0.323,0.297,0.0196972382880031
+"ITGB1",1.2463315769801e-06,0.0639960729255851,0.669,0.671,0.0289772091647874
+"COX17",1.38398723677478e-06,0.148811300252279,0.397,0.388,0.0321777032550137
+"IPO7",1.68638441356589e-06,0.117913385818765,0.331,0.312,0.039208437615407
diff --git a/r.scripts/sc-TissueMapper.R b/r.scripts/sc-TissueMapper.R
index 196bb868929a39fa62e848ef433c1fd63dcb4cd5..71d98579bb582df4326eda8683d27572aa1f90ac 100644
--- a/r.scripts/sc-TissueMapper.R
+++ b/r.scripts/sc-TissueMapper.R
@@ -103,17 +103,14 @@ scCellCycle <- function(sc10x,sub=FALSE){
   sc10x <- ScaleData(object=sc10x,display.progress=FALSE,do.par=TRUE,num.cores=45)
   sc10x <- CellCycleScoring(object=sc10x,s.genes=genes.s,g2m.genes=genes.g2m,set.ident=TRUE)
   
-  tryCatch({
-    postscript(paste0(folder,"Ridge_cc.Raw.eps"))
-    plot <- RidgePlot(object=sc10x,features.plot=c("PCNA","TOP2A","MCM6","MKI67"),y.log=TRUE,nCol=2,do.return=TRUE)
-    plot(plot)
-    dev.off()
-    postscript(paste0(folder,"Violin_cc.Raw.eps"))
-    plot <- VlnPlot(object=sc10x,features.plot=c("PCNA","TOP2A","MCM6","MKI67"),nCol=2,point.size.use=1,size.title.use=20,x.lab.rot=TRUE)
-    plot(plot)
-    dev.off()
-  },error=function(e){cat("ERROR : ",conditionMessage(e),"/\n")})
-
+  postscript(paste0(folder,"Ridge_cc.Raw.eps"))
+  plot <- RidgePlot(object=sc10x,features.plot=c("PCNA","TOP2A","MCM6","MKI67"),y.log=TRUE,nCol=2,do.return=TRUE)
+  plot(plot)
+  dev.off()
+  postscript(paste0(folder,"Violin_cc.Raw.eps"))
+  plot <- VlnPlot(object=sc10x,features.plot=c("PCNA","TOP2A","MCM6","MKI67"),nCol=2,point.size.use=1,size.title.use=20,x.lab.rot=TRUE)
+  plot(plot)
+  dev.off()
   sc10x <- RunPCA(object=sc10x,pc.genes=c(genes.s,genes.g2m),do.print=FALSE,pcs.store=2)
   postscript(paste0(folder,"PCA_cc.Raw.eps"))
   plot <- PCAPlot(object=sc10x,do.return=TRUE)
@@ -349,6 +346,8 @@ scStress <- function(sc10x,stg="go",res.use=1,pc.use=10,cut=0.95){
   sc10x.Stress <- t(sc10x.Stress)
   sc10x.Stress <- sc10x.Stress[,apply(sc10x.Stress,2,var)!=0]
   sc10x.Stress.pca <- prcomp(sc10x.Stress,center=TRUE,scale.=TRUE)
+  sc10x.Stress.pca.pc1var <- round((sc10x.Stress.pca$sdev[1]^2)/sum(sc10x.Stress.pca$sdev^2)*100,0)
+  sc10x.Stress.pca.pc2var <- round((sc10x.Stress.pca$sdev[2]^2)/sum(sc10x.Stress.pca$sdev^2)*100,0)
   sc10x.Stress.pca <- sc10x.Stress.pca$x[,1:2]
   colnames(x=sc10x.Stress.pca) <- paste0("Stress",1:2)
   if (skewness(sc10x.Stress.pca[,1])<0){
@@ -366,6 +365,8 @@ scStress <- function(sc10x,stg="go",res.use=1,pc.use=10,cut=0.95){
   plot <- plot+geom_density2d(color="black",bins=25,alpha=0.5)
   plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
   plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot <- plot+xlab(paste0("Stress PC1 (",sc10x.Stress.pca.pc1var,"%)"))
+  plot <- plot+ylab(paste0("Stress PC2 (",sc10x.Stress.pca.pc2var,"%)"))
   plot(plot)
   dev.off()
   
@@ -400,11 +401,13 @@ scStress <- function(sc10x,stg="go",res.use=1,pc.use=10,cut=0.95){
   plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
   plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
   plot <- plot+geom_vline(xintercept=cut.x,color="red",lwd=2.5)
+  plot <- plot+xlab(paste0("Stress PC1 (",sc10x.Stress.pca.pc1var,"%)"))
+  plot <- plot+ylab(paste0("Stress PC2 (",sc10x.Stress.pca.pc2var,"%)"))
   plot(plot)
   dev.off()
   
   #Subsample all cells (+Stress) to better visualize their clustering
-  if (ncol(sc10x@data)>2500){
+  if (ncol(sc10x@data)<2500){
     rnd <- sample(1:ncol(sc10x@data),2500)
   } else {
     rnd <- 1:ncol(sc10x@data)
@@ -848,6 +851,8 @@ scNE <- function(sc10x,neg="EurUro",cut=0.95){
   sc10x.NE <- t(sc10x.NE)
   sc10x.NE <- sc10x.NE[,apply(sc10x.NE,2,var)!=0]
   sc10x.NE.pca <- prcomp(sc10x.NE,center=TRUE,scale.=TRUE)
+  sc10x.NE.pca.pc1var <- round((sc10x.NE.pca$sdev[1]^2)/sum(sc10x.NE.pca$sdev^2)*100,0)
+  sc10x.NE.pca.pc2var <- round((sc10x.NE.pca$sdev[2]^2)/sum(sc10x.NE.pca$sdev^2)*100,0)
   sc10x.NE.pca <- sc10x.NE.pca$x[,1:2]
   colnames(x=sc10x.NE.pca) <- paste0("NE",1:2)
   if (skewness(sc10x.NE.pca[,1])<0){
@@ -864,6 +869,8 @@ scNE <- function(sc10x,neg="EurUro",cut=0.95){
   plot <- DimPlot(object=sc10x,reduction.use="NE",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE)
   plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
   plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot <- plot+xlab(paste0("NE PC1 (",sc10x.NE.pca.pc1var,"%)"))
+  plot <- plot+ylab(paste0("NE PC2 (",sc10x.NE.pca.pc2var,"%)"))
   plot(plot)
   dev.off()
   
@@ -897,11 +904,13 @@ scNE <- function(sc10x,neg="EurUro",cut=0.95){
   plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
   plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
   plot <- plot+geom_vline(xintercept=cut.x,color="red",lwd=2.5)
+  plot <- plot+xlab(paste0("NE PC1 (",sc10x.NE.pca.pc1var,"%)"))
+  plot <- plot+ylab(paste0("NE PC2 (",sc10x.NE.pca.pc2var,"%)"))
   plot(plot)
   dev.off()
   
   #Subsample all cells (+NE) to better visualize their clustering
-  if (ncol(sc10x@data)>2500){
+  if (ncol(sc10x@data)<2500){
     rnd <- sample(1:ncol(sc10x@data),2500)
   } else {
     rnd <- 1:ncol(sc10x@data)
diff --git a/r.scripts/sc-TissueMapper_RUN.D17_FACS.R b/r.scripts/sc-TissueMapper_RUN.D17_FACS.R
index 7812cd1180f5bd6ac6604f8cd4d15fd8bdae8992..fb8f5a8865a16879e52db962097f7055d68099f4 100644
--- a/r.scripts/sc-TissueMapper_RUN.D17_FACS.R
+++ b/r.scripts/sc-TissueMapper_RUN.D17_FACS.R
@@ -167,13 +167,12 @@ if (opt$st==TRUE){
   sc10x <- scCluster(sc10x,pc.use=pc.use.poststress,res.use=0.5,folder="post.stress",red="pca")
 }
 
-gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+
+gene.set1 <- read_delim("./genesets/DEG_Epi_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "Epi"
 gene.set <- c(gene.set1)
-gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+gene.set1 <- read_delim("./genesets/DEG_FMSt_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "St"
 gene.set <- c(gene.set,gene.set1)
diff --git a/r.scripts/sc-TissueMapper_RUN.D27_FACS.R b/r.scripts/sc-TissueMapper_RUN.D27_FACS.R
index 61cadc9c0da54b927d49f81fb35f9f76986d82e8..0337bd6bbd42914eed17258f52e65991e66273e0 100644
--- a/r.scripts/sc-TissueMapper_RUN.D27_FACS.R
+++ b/r.scripts/sc-TissueMapper_RUN.D27_FACS.R
@@ -167,13 +167,12 @@ if (opt$st==TRUE){
   sc10x <- scCluster(sc10x,pc.use=pc.use.poststress,res.use=0.5,folder="post.stress",red="pca")
 }
 
-gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+
+gene.set1 <- read_delim("./genesets/DEG_Epi_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "Epi"
 gene.set <- c(gene.set1)
-gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+gene.set1 <- read_delim("./genesets/DEG_FMSt_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "St"
 gene.set <- c(gene.set,gene.set1)
diff --git a/r.scripts/sc-TissueMapper_RUN.DS_D17.R b/r.scripts/sc-TissueMapper_RUN.DS_D17.R
index b1766dc054b9bec797537e88a4d194604f7aa1d7..efec340dbda666de55b016f037b22f4ee3e04c78 100644
--- a/r.scripts/sc-TissueMapper_RUN.DS_D17.R
+++ b/r.scripts/sc-TissueMapper_RUN.DS_D17.R
@@ -13,6 +13,8 @@ library(viridis)
 library(reshape2)
 library(NMI)
 
+options(bitmapType="cairo")
+
 source("../r.scripts/sc-TissueMapper.R")
 
 #Create folder structure
@@ -101,7 +103,7 @@ option_list=list(
   make_option("--lx",action="store",default=0.2,type='numeric',help="x low threshold for hvg selection"),
   make_option("--hx",action="store",default=5,type='numeric',help="x high threshold for hvg selection"),
   make_option("--ly",action="store",default=1,type='numeric',help="y low threshold for hvg selection"),
-  make_option("--cc",action="store",default=TRUE,type='logical',help="Scale cell cycle?"),
+  make_option("--cc",action="store",default=FALSE,type='logical',help="Scale cell cycle?"),
   make_option("--cca",action="store",default=50,type='integer',help="Number of CCAs to cacluate"),
   make_option("--acca",action="store",default=30,type='integer',help="Number of CCAs to align"),
   make_option("--pc",action="store",default=50,type='integer',help="Number of PCs to cacluate"),
@@ -178,13 +180,11 @@ if (opt$st==TRUE){
   sc10x <- scCluster(sc10x,pc.use=pc.use.poststress,res.use=opt$res.poststress,folder="post.stress",red="pca")
 }
 
-gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+gene.set1 <- read_delim("./genesets/DEG_Epi_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "Epi"
 gene.set <- c(gene.set1)
-gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-gene.set1 <- gene.set1[1]
+gene.set1 <- read_delim("./genesets/DEG_FMSt_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
 gene.set1 <- as.list(gene.set1)
 names(gene.set1) <- "St"
 gene.set <- c(gene.set,gene.set1)
@@ -305,6 +305,11 @@ rm(gene.set)
 sc10x.Epi.NE <- scNE(sc10x.Epi,neg="dws",cut=opt$cut.ne)
 
 sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Epi.dws.sc",i.2="St.dws.sc",nm="Merge_Epi.dws.sc_St.dws.sc")
+cells.ne <- names(sc10x.Epi.NE@ident[sc10x.Epi.NE@ident=="NE"])
+sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc")
+sc10x <- SetIdent(object=sc10x,cells.use=cells.ne,ident.use="NE")
+sc10x <- StashIdent(object=sc10x,save.name="Merge_Epi.dws.sc_St.dws.sc_NE")
+rm(cells.ne)
 
 sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc")
 sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc")
@@ -316,7 +321,7 @@ plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
 plot(plot)
 dev.off()
 
-scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc")
+scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc_NE")
 
 sctSNECustCol(sc10x,i="Lin",bl="Epi",rd="St",file="D17")
 sctSNECustCol(sc10x,i="Merge_Epi.dws.sc_St.dws.sc",bl=c("BE","LE","OE_SCGB","OE_KRT13"),rd=c("Fib","SM","Endo","Leu"),file="D17")
@@ -344,6 +349,41 @@ for (g in c("Fib","SM","Endo","Leu")){
 rm(i)
 rm(g)
 
+try(genes.deg.Stress <- scDEG(sc10x.Stress,i="Stress",g.1="Stress",g.2="ALL",pct=0.5,t=5))
+
+try(genes.deg.Epi <- scDEG(sc10x,i="Lin",g.1="Epi",g.2="St",t=2))
+try(genes.deg.St <- scDEG(sc10x,i="Lin",g.1="St",g.2="Epi",t=2))
+
+try(genes.deg.BE <- scDEG(sc10x,i="Merge_Epi.dws.sc_St.dws.sc_NE",g.1="BE",g.2=c("LE","OE_SCGB","OE_KRT13"),pct=0.25,t=2))
+try(genes.deg.LE <- scDEG(sc10x,i="Merge_Epi.dws.sc_St.dws.sc_NE",g.1="LE",g.2=c("BE","OE_SCGB","OE_KRT13"),pct=0.25,t=2))
+try(genes.deg.OE1 <- scDEG(sc10x,i="Merge_Epi.dws.sc_St.dws.sc_NE",g.1="OE_SCGB",g.2=c("BE","LE","OE_KRT13"),pct=0.25,t=2))
+try(genes.deg.OE2 <- scDEG(sc10x,i="Merge_Epi.dws.sc_St.dws.sc_NE",g.1="OE_KRT13",g.2=c("BE","LE","OE_SCGB"),pct=0.25,t=2))
+
+try(genes.deg.NE <- scDEG(sc10x,i="Merge_Epi.dws.sc_St.dws.sc_NE",g.1="NE",g.2=c("BE","LE","OE_SCGB","OE_KRT13"),pct=0.01,t=1))
+
+try(genes.deg.Fib <- scDEG(sc10x.St,i="Merge_Epi.dws.sc_St.dws.sc",g.1="Fib",g.2=c("SM","Endo","Leu"),pct=0.25,t=2))
+try(genes.deg.SM <- scDEG(sc10x.St,i="Merge_Epi.dws.sc_St.dws.sc",g.1="SM",g.2=c("Fib","Endo","Leu"),pct=0.25,t=2))
+try(genes.deg.Endo <- scDEG(sc10x.St,i="Merge_Epi.dws.sc_St.dws.sc",g.1="Endo",g.2=c("Fib","SM","Leu"),pct=0.25,t=2))
+try(genes.deg.Leu <- scDEG(sc10x.St,i="St.dws.sc",g.1="Leu",g.2=c("Fib","SM","Endo"),pct=0.25,t=2))
+
+try(genes.deg.BE.unique <- setdiff(rownames(genes.deg.BE),Reduce(union,list(rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.LE.unique <- setdiff(rownames(genes.deg.LE),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.OE1.unique <- setdiff(rownames(genes.deg.OE1),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.OE2.unique <- setdiff(rownames(genes.deg.OE2),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.NE.unique <- setdiff(rownames(genes.deg.NE),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.Fib.unique <- setdiff(rownames(genes.deg.Fib),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.SM.unique <- setdiff(rownames(genes.deg.SM),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.Endo),rownames(genes.deg.Leu)))))
+try(genes.deg.Endo.unique <- setdiff(rownames(genes.deg.Endo),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Leu)))))
+try(genes.deg.Leu.unique <- setdiff(rownames(genes.deg.Leu),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo)))))
+
+try(genes.deg.5 <- c(genes.deg.BE.unique[1:5],genes.deg.LE.unique[1:5],genes.deg.OE1.unique[1:5],genes.deg.OE2.unique[1:5],genes.deg.NE.unique[1:5],genes.deg.Fib.unique[1:5],genes.deg.SM.unique[1:5],genes.deg.Endo.unique[1:5],genes.deg.Leu.unique[1:5]))
+try(genes.deg.5 <- rev(genes.deg.5))
+try(genes.deg.10 <- c(genes.deg.BE.unique[1:10],genes.deg.LE.unique[1:10],genes.deg.OE1.unique[1:10],genes.deg.OE2.unique[1:10],genes.deg.NE.unique[1:10],genes.deg.Fib.unique[1:10],genes.deg.SM.unique[1:10],genes.deg.Endo.unique[1:10],genes.deg.Leu.unique[1:10]))
+try(genes.deg.10 <- rev(genes.deg.10))
+
+for (i in ls(pattern="^genes.deg")){
+  try(write.table(get(i),file=paste0("./analysis/deg/",i,".csv"),row.names=TRUE,col.names=NA,append=FALSE,sep=","))
+}
 
 save(list=ls(pattern="sc10x.Stress"),file="./analysis/sc10x.Stress.Rda")
 rm(list=ls(pattern="sc10x.Stress"))
diff --git a/r.scripts/sc-TissueMapper_RUN.DS_D17.aggr.R b/r.scripts/sc-TissueMapper_RUN.DS_D17.aggr.R
index cac02a21cb42514e98900e8a21a90ffc2f57dadb..91ae977c98f84b5dfcb9943b4293cccbb35f5625 100755
--- a/r.scripts/sc-TissueMapper_RUN.DS_D17.aggr.R
+++ b/r.scripts/sc-TissueMapper_RUN.DS_D17.aggr.R
@@ -13,6 +13,8 @@ library(viridis)
 library(reshape2)
 library(NMI)
 
+options(bitmapType="cairo")
+
 source("../r.scripts/sc-TissueMapper.R")
 
 setwd("../")
diff --git a/r.scripts/sc-TissueMapper_RUN.Pd.R b/r.scripts/sc-TissueMapper_RUN.Pd.R
index 7772609db250c04cfb7c6511a9d7f3b344563a11..396cf44665e71c349c817667729f5b8b72afacea 100644
--- a/r.scripts/sc-TissueMapper_RUN.Pd.R
+++ b/r.scripts/sc-TissueMapper_RUN.Pd.R
@@ -11,6 +11,8 @@ library(monocle)
 library(dplyr)
 library(viridis)
 
+options(bitmapType="cairo")
+
 source("../r.scripts/sc-TissueMapper.R")
 
 #Create folder structure
@@ -337,7 +339,7 @@ sc10x <- SetIdent(object=sc10x,cells.use=names(sc10x@ident[sc10x@ident %in% c("S
 sc10x <- StashIdent(object=sc10x,save.name="mLin")
 
 gene.orthog <- read.delim("./genesets/Ensemble.mus-hum.txt")
-gene.set1 <- read_csv("./genesets/SupTab3_Consensus_Sigs.csv",skip=6)
+gene.set1 <- read_csv("./genesets/SupTab3_Consensus_Sigs.csv",skip=5)
 gene.set2 <- as.data.frame(gene.set1$Basal[!is.na(gene.set1$Basal)])
 colnames(gene.set2) <- "genes"
 gene.set2 <- as.data.frame(merge(gene.set2,gene.orthog,by.x="genes",by.y="Gene.name")[,4])
@@ -375,7 +377,7 @@ gene.set2 <- as.list(gene.set2)
 names(gene.set2) <- "Ionocyte"
 gene.set <- c(gene.set,gene.set2)
 rm(gene.set2)
-gene.set1 <- read_csv("./genesets/SupTab6_Krt13_Hillock.csv",skip=6)
+gene.set1 <- read_csv("./genesets/SupTab6_Krt13_Hillock.csv")
 gene.set1 <- gene.set1[gene.set1$FDR<=0.05 & gene.set1$'log2 fold-change (MAST)'>=1.5,1]
 colnames(gene.set1) <- "genes"
 gene.set1 <- as.data.frame(merge(gene.set1,gene.orthog,by.x="genes",by.y="Gene.name")[,4])
@@ -385,7 +387,7 @@ gene.set <- c(gene.set,gene.set1)
 rm(gene.set1)
 rm(gene.orthog)
 gene.set <- lapply(gene.set,droplevels)
-results.cor.Epi.MusLungHierarchy <- scQuSAGEsm(sc10x.Epi,gs=gene.set,ds=min.epi,nm="Epi.dws.sub_NE",folder="MusLungHierarchy")
+results.cor.Epi.MusLungHierarchy <- scQuSAGEsm(sc10x.Epi,gs=gene.set,ds=0,nm="Epi.dws.sub_NE",folder="MusLungHierarchy")
 rm(gene.set)
 
 gene.set.c2.all <- read.gmt("./genesets/c2.all.v6.1.symbols.gmt")
@@ -407,14 +409,16 @@ scTables(sc10x,i.1="Merge_Epi.dws_St.go_NE",i.2="Merge_Epi.dws_St.go")
 
 genes.deg.Stress <- scDEG(sc10x.Stress,i="Stress",g.1="Stress",g.2="ALL",pct=0.5,t=5)
 
-genes.deg.Epi <- scDEG(sc10x,i="Lin",g.1="Epi",g.2="St",t=2)
-genes.deg.St <- scDEG(sc10x,i="Lin",g.1="St",g.2="Epi",t=2)
+genes.deg.Epi <- scDEG(sc10x,i="Lin",g.1="Epi",g.2="St",t=0)
+genes.deg.St <- scDEG(sc10x,i="Lin",g.1="St",g.2="Epi",t=0)
 
 genes.deg.BE <- scDEG(sc10x.Epi.NE,i="Epi.dws.sub",g.1="BE",g.2=c("LE","OE1","OE2"),pct=0.25,t=2)
-genes.deg.LE <- scDEG(sc10x.Epi.NE,i="Epi.dws.sub",g.1="LE",g.2=c("BE","LE","OE1"),pct=0.25,t=2)
+genes.deg.LE <- scDEG(sc10x.Epi.NE,i="Epi.dws.sub",g.1="LE",g.2=c("BE","OE1","OE2"),pct=0.25,t=2)
 genes.deg.OE1 <- scDEG(sc10x.Epi.NE,i="Epi.dws.sub",g.1="OE1",g.2=c("BE","LE","OE2"),pct=0.25,t=2)
 genes.deg.OE2 <- scDEG(sc10x.Epi.NE,i="Epi.dws.sub",g.1="OE2",g.2=c("BE","LE","OE1"),pct=0.25,t=2)
 
+genes.deg.Epi <- genes.deg.Epi[rownames(genes.deg.Epi) %in% setdiff(rownames(genes.deg.Epi),union(union(union(rownames(genes.deg.BE),rownames(genes.deg.LE)),rownames(genes.deg.OE1)),rownames(genes.deg.OE2))),]
+
 genes.deg.NE <- scDEG(sc10x.Epi.NE,i="NE",g.1="NE",g.2="ALL",pct=0.01,t=1)
 
 genes.deg.Fib <- scDEG(sc10x.St,i="Merge_Epi.dws_St.go_NE",g.1="Fib",g.2=c("SM","Endo","Leu"),pct=0.25,t=2)
@@ -422,6 +426,8 @@ genes.deg.SM <- scDEG(sc10x.St,i="Merge_Epi.dws_St.go_NE",g.1="SM",g.2=c("Fib","
 genes.deg.Endo <- scDEG(sc10x.St,i="Merge_Epi.dws_St.go_NE",g.1="Endo",g.2=c("Fib","SM","Leu"),pct=0.25,t=2)
 genes.deg.Leu <- scDEG(sc10x.St,i="St.go",g.1="Leu",g.2=c("Fib","SM","Endo"),pct=0.25,t=2)
 
+genes.deg.St <- genes.deg.St[rownames(genes.deg.St) %in% setdiff(rownames(genes.deg.St),union(union(union(rownames(genes.deg.Fib),rownames(genes.deg.SM)),rownames(genes.deg.Endo)),rownames(genes.deg.Leu))),]
+
 genes.deg.BE.unique <- setdiff(rownames(genes.deg.BE),Reduce(union,list(rownames(genes.deg.LE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu))))
 genes.deg.LE.unique <- setdiff(rownames(genes.deg.LE),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.OE1),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu))))
 genes.deg.OE1.unique <- setdiff(rownames(genes.deg.OE1),Reduce(union,list(rownames(genes.deg.BE),rownames(genes.deg.LE),rownames(genes.deg.OE2),rownames(genes.deg.NE),rownames(genes.deg.Fib),rownames(genes.deg.SM),rownames(genes.deg.Endo),rownames(genes.deg.Leu))))
diff --git a/r.scripts/sc-TissueMapper_RUN.Pd.StressCompare.R b/r.scripts/sc-TissueMapper_RUN.Pd.StressCompare.R
new file mode 100755
index 0000000000000000000000000000000000000000..fc23ed8ce0e316175feeb4840d48c6b99a85b61e
--- /dev/null
+++ b/r.scripts/sc-TissueMapper_RUN.Pd.StressCompare.R
@@ -0,0 +1,135 @@
+library(methods)
+library(optparse)
+library(Seurat)
+library(readr)
+library(fBasics)
+library(pastecs)
+library(qusage)
+library(RColorBrewer)
+library(VennDiagram)
+library(Cairo)
+
+options(bitmapType="cairo")
+
+if (!dir.exists("../analysis/stress")){
+  dir.create("../analysis/stress")
+}
+
+load("../analysis/sc10x.Stress.Rda")
+sc10x.Stress <- SetAllIdent(object=sc10x.Stress,id="ALL")
+gene.exp <- as.data.frame(as.matrix(sc10x.Stress@raw.data[,colnames(sc10x.Stress@data)]))
+
+genes.stress.go <- read_csv("../genesets/DEG_C2.CGP.M10970.txt")
+genes.stress.go <- genes.stress.go[2:nrow(genes.stress.go),]
+genes.stress.go <- as.list(genes.stress.go)
+
+genes.stress.dws <- read_delim("../genesets/genes.deg.Stress.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+genes.stress.dws <- genes.stress.dws[1]
+colnames(genes.stress.dws) <- "scDWS.Stress"
+genes.stress.dws <- as.list(genes.stress.dws)
+
+#genes.stress.vdb <- read_delim("./vanderBrink.Stress.mus.tsv","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
+#colnames(genes.stress.vdb) <- "vdBrink.Stress"
+
+genes.stress.vdb <- c("Actg1__chr11","Ankrd1__chr19","Arid5a__chr1","Atf3__chr1","Atf4__chr15","Bag3__chr7","Bhlhe40__chr6","Brd2__chr17","Btg1__chr10","Btg2__chr1","Ccnl1__chr3","Ccrn4l__chr3","Cebpb__chr2","Cebpd__chr16","Cebpg__chr7","Csrnp1__chr9","Cxcl1__chr5","Cyr61__chr3","Dcn__chr10","Ddx3x__chrX","Ddx5__chr11","Des__chr1","Dnaja1__chr4","Dnajb1__chr8","Dnajb4__chr3","Dusp1__chr17","Dusp8__chr7","Egr1__chr18","Egr2__chr10","Eif1__chr11","Eif5__chr12","Erf__chr7","Errfi1__chr4","Fam132b__chr1","Fos__chr12","Fosb__chr7","Fosl2__chr5","Gadd45a__chr6","Gcc1__chr6","Gem__chr4","H3f3b__chr11","Hipk3__chr2","Hsp90aa1__chr12","Hsp90ab1__chr17","Hspa1a__chr17","Hspa1b__chr17","Hspa5__chr2","Hspa8__chr9","Hspb1__chr5","Hsph1__chr5","Id3__chr4","Idi1__chr13","Ier2__chr8","Ier3__chr17","Ifrd1__chr12","Il6__chr5","Irf1__chr11","Irf8__chr8","Itpkc__chr7","Jun__chr4","Junb__chr8","Jund__chr8","Klf2__chr8","Klf4__chr4","Klf6__chr13","Klf9__chr19","Litaf__chr16","Lmna__chr3","Maff__chr15","Mafk__chr5","Mcl1__chr3","Midn__chr10","Mir22hg__chr11","Mt1__chr8","Mt2__chr8","Myadm__chr7","Myc__chr15","Myd88__chr9","Nckap5l__chr15","Ncoa7__chr10","Nfkbia__chr12","Nfkbiz__chr16","Nop58__chr1","Nppc__chr1","Nr4a1__chr15","Odc1__chr12","Osgin1__chr8","Oxnad1__chr14","Pcf11__chr7","Pde4b__chr4","Per1__chr11","Phlda1__chr10","Pnp__chr14","Pnrc1__chr4","Ppp1cc__chr5","Ppp1r15a__chr7","Pxdc1__chr13","Rap1b__chr10","Rassf1__chr9","Rhob__chr12","Rhoh__chr5","Ripk1__chr13","Sat1__chrX","Sbno2__chr10","Sdc4__chr2","Serpine1__chr5","Skil__chr3","Slc10a6__chr5","Slc38a2__chr15","Slc41a1__chr1","Socs3__chr11","Sqstm1__chr11","Srf__chr17","Srsf5__chr12","Srsf7__chr17","Stat3__chr11","Tagln2__chr1","Tiparp__chr3","Tnfaip3__chr10","Tnfaip6__chr2","Tpm3__chr3","Tppp3__chr8","Tra2a__chr6","Tra2b__chr16","Trib1__chr15","Tubb4b__chr2","Tubb6__chr18","Ubc__chr5","Usp2__chr9","Wac__chr18","Zc3h12a__chr4","Zfand5__chr19","Zfp36__chr7","Zfp36l1__chr12","Zfp36l2__chr17","Zyx__chr6","Gadd45g__chr13","Hspe1__chr1","Ier5__chr1","Kcne4__chr1")
+genes.stress.vdb <- gsub("\\__.*","",genes.stress.vdb)
+genes.stress.vdb <- data.frame(vdBrink.Stress=genes.stress.vdb)
+genes.homolog <- read_delim("../genesets/Ensemble.mus-hum.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+genes.stress.vdb <- list("vdBrink.Stress"=merge(genes.stress.vdb,genes.homolog[,c(2,4)],by.x="vdBrink.Stress",by.y="Gene name")[,2])
+
+venn.diagram(list(go=unlist(genes.stress.go),dws=unlist(genes.stress.dws),vdb=unlist(genes.stress.vdb)),"../analysis/stress/Venn.tiff")
+
+
+for (i in c("go","dws","vdb")){
+  genes.stress=get(paste0("genes.stress.",i))
+  
+  gene.stress.pct <- apply(gene.exp,2,function(x) sum(x[rownames(gene.exp) %in% unlist(genes.stress)])/sum(x))
+
+  #histo <- hist(gene.stress.pct,breaks=100,prob=TRUE,plot=TRUE,main="Distribution of Stress Gene Expression in Transcriptome",xlab="% of transcriptome associated to stress genes")
+  #abline(v=.0575,col="red")
+
+  sc10x.Stress <- SetAllIdent(object=sc10x.Stress,id="ALL+Stress")
+  cell.index.stress <- names(sc10x.Stress@ident[sc10x.Stress@ident=="Stress"])
+  cell.index.stress <- which(colnames(gene.exp) %in% cell.index.stress)
+  cell.index.ntstress <- names(sc10x.Stress@ident[sc10x.Stress@ident=="ALL"])
+  cell.index.ntstress <- which(colnames(gene.exp) %in% cell.index.ntstress)
+
+  histo.stress <- hist(gene.stress.pct[cell.index.stress],breaks=seq(0,ceiling(max(gene.stress.pct)*100)/100,0.005),main="Distribution of Stress Gene Expression in Transcriptome",xlab="% of transcriptome associated to stress genes",plot=FALSE)
+  #abline(v=.0575,col="red")
+  histo.ntstress <- hist(gene.stress.pct[cell.index.ntstress],breaks=seq(0,ceiling(max(gene.stress.pct)*100)/100,0.005),main="Distribution of Stress Gene Expression in Transcriptome",xlab="% of transcriptome associated to stress genes",plot=FALSE)
+  #abline(v=.0575,col="red")
+  
+  Cairo(paste0("../analysis/stress/Hist.Stress.",i,".png"),width=1000,height=500,bg="white")
+  plot(histo.ntstress$mids,histo.ntstress$density,col=rgb(0,1,0,1/5),xlim=c(0,ceiling(max(gene.stress.pct)*100)/100),ylim=c(0,ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10),type="h",lend="square",lwd=10,xlab="Fraction of transcriptome associated to stress genes",ylab="Density")
+  par(new=TRUE)
+  plot(histo.stress$mids,histo.stress$density,col=rgb(1,0,0,1/5),xlim=c(0,ceiling(max(gene.stress.pct)*100)/100),ylim=c(0,ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10),type="h",lend="square",lwd=10,axes=FALSE,xlab="",ylab="")
+  abline(v=0.0575,col="red")
+  text(0.0575+0.01,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3+2.5,paste0(round(sum(histo.ntstress$counts[histo.ntstress$breaks>0.035][!is.na(histo.ntstress$counts[histo.ntstress$breaks>0.035])])/sum(histo.ntstress$counts[!is.na(histo.ntstress$counts)])*100),"%"),col="green")
+  text(0.0575-0.01,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3+2.5,paste0(round(sum(histo.ntstress$counts[histo.ntstress$breaks<=0.035][!is.na(histo.ntstress$counts[histo.ntstress$breaks<=0.035])])/sum(histo.ntstress$counts[!is.na(histo.ntstress$counts)])*100),"%"),col="green")
+  text(0.0575+0.01,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3-2.5,paste0(round(sum(histo.stress$counts[histo.stress$breaks>0.035][!is.na(histo.stress$counts[histo.stress$breaks>0.035])])/sum(histo.stress$counts[!is.na(histo.stress$counts)])*100),"%"),col="red")
+  text(0.0575-0.01,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3-2.5,paste0(round(sum(histo.stress$counts[histo.stress$breaks<=0.035][!is.na(histo.stress$counts[histo.stress$breaks<=0.035])])/sum(histo.stress$counts[!is.na(histo.stress$counts)])*100),"%"),col="red")
+  text(0.0575+0.05,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3+2.5,"Not Stressed",col="green")
+  text(0.0575+0.05,(ceiling(max(histo.stress$density,histo.ntstress$density)/10)*10)/4*3-2.5,"Stressed",col="red")
+  dev.off()
+}
+rm(i)
+rm(genes.stress)
+rm(gene.stress.pct)
+rm(cell.index.stress)
+rm(cell.index.ntstress)
+rm(histo.stress)
+rm(histo.ntstress)
+
+for (cut in c(0.9,0.95)){
+  for (i in c("go","dws","vdb")){
+    genes.stress=get(paste0("genes.stress.",i))
+    sc10x.Stress <- SetAllIdent(object=sc10x.Stress,id="ALL")
+    sc10x.Stress.dta <- as.data.frame(as.matrix(sc10x.Stress@data[rownames(sc10x.Stress@scale.data) %in% unlist(genes.stress),]))
+    
+    #Run PCA of subsetted stress genes
+    sc10x.Stress.dta <- t(sc10x.Stress.dta)
+    sc10x.Stress.dta <- sc10x.Stress.dta[,apply(sc10x.Stress.dta,2,var)!=0]
+    sc10x.Stress.dta.pca <- prcomp(sc10x.Stress.dta,center=TRUE,scale.=TRUE)
+    sc10x.Stress.dta.pca <- sc10x.Stress.dta.pca$x[,1:2]
+    colnames(x=sc10x.Stress.dta.pca) <- paste0("Stress",1:2)
+    if (skewness(sc10x.Stress.dta.pca[,1])<0){
+      sc10x.Stress.dta.pca[,1] <- (-sc10x.Stress.dta.pca[,1])
+    }
+    if (skewness(sc10x.Stress.dta.pca[,2])<0){
+      sc10x.Stress.dta.pca[,2] <- (-sc10x.Stress.dta.pca[,2])
+    }
+    sc10x.dta <- sc10x.Stress
+    sc10x.dta <- SetDimReduction(object=sc10x.dta,reduction.type="Stress",slot="cell.embeddings",new.data=sc10x.Stress.dta.pca)
+    sc10x.dta <- SetDimReduction(object=sc10x.dta,reduction.type="Stress",slot="key",new.data="Stress")
+    
+    #CDS
+    cdf <- ecdf(GetCellEmbeddings(object=sc10x.dta,reduction.type="Stress",dims.use=1))
+    cut.x <- quantile(cdf,probs=cut)
+    
+    assign(i,GetCellEmbeddings(object=sc10x.dta,reduction.type="Stress",dims.use=1))
+    assign(paste0("cut.",i,".",cut),cut.x)
+}}
+rm(cut)
+rm(i)
+rm(genes.stress)
+rm(sc10x.Stress.dta)
+rm(sc10x.Stress.dta.pca)
+rm(sc10x.dta)
+rm(cdf)
+rm(cut.x)  
+
+comp <- cbind(vdb,go[match(rownames(vdb),rownames(go))],dws[match(rownames(vdb),rownames(dws))])
+colnames(comp) <- c("vdb","go","dws")
+comp <- data.frame(comp)
+
+Cairo(paste0("../analysis/stress/Stress.vdb.vs.go.png"),width=1000,height=500,bg="white")
+ggplot(comp,aes(x=vdb,y=go))+geom_point(size=0.01)+geom_vline(xintercept=cut.vdb.0.95)+geom_hline(yintercept=cut.go.0.95)+annotate("text",x=max(comp$vdb)/4*3,y=max(comp$go)/4*3,col="red",size=10,fontface=2,label=paste0(round(nrow(comp[comp$vdb>cut.vdb.0.95,][comp[comp$vdb>cut.vdb.0.95,"go"]>cut.go.0.95,])/nrow(comp[comp$vdb>cut.vdb.0.95,])*100,1),"%"))+annotate("text",x=max(comp$vdb)/100,y=max(comp$go)/100,col="green",size=10,fontface=2,label=paste0(round(nrow(comp[comp$vdb<=cut.vdb.0.95,][comp[comp$vdb<=cut.vdb.0.95,"go"]<=cut.go.0.95,])/nrow(comp[comp$vdb<=cut.vdb.0.95,])*100,1),"%"))
+dev.off()
+Cairo(paste0("../analysis/stress/Stress.vdb.vs.dws.png"),width=1000,height=500,bg="white")
+ggplot(comp,aes(x=vdb,y=dws))+geom_point(size=0.01)+geom_vline(xintercept=cut.vdb.0.95)+geom_hline(yintercept=cut.dws.0.95)+annotate("text",x=max(comp$vdb)/4*3,y=max(comp$dws)/4*3,col="red",size=10,fontface=2,label=paste0(round(nrow(comp[comp$vdb>cut.vdb.0.95,][comp[comp$vdb>cut.vdb.0.95,"dws"]>cut.dws.0.95,])/nrow(comp[comp$vdb>cut.vdb.0.95,])*100,1),"%"))+annotate("text",x=max(comp$vdb)/100,y=max(comp$dws)/100,col="green",size=10,fontface=2,label=paste0(round(nrow(comp[comp$vdb<=cut.vdb.0.95,][comp[comp$vdb<=cut.vdb.0.95,"dws"]<=cut.dws.0.95,])/nrow(comp[comp$vdb<=cut.vdb.0.95,])*100,1),"%"))
+dev.off()
+Cairo(paste0("../analysis/stress/Stress.go.vs.dws.png"),width=1000,height=500,bg="white")
+ggplot(comp,aes(x=go,y=dws))+geom_point(size=0.01)+geom_vline(xintercept=cut.go.0.95)+geom_hline(yintercept=cut.dws.0.95)+annotate("text",x=max(comp$go)/4*3,y=max(comp$dws)/4*3,col="red",size=10,fontface=2,label=paste0(round(nrow(comp[comp$go>cut.go.0.95,][comp[comp$go>cut.go.0.95,"dws"]>cut.dws.0.95,])/nrow(comp[comp$go>cut.go.0.95,])*100,1),"%"))+annotate("text",x=max(comp$go)/100,y=max(comp$dws)/100,col="green",size=10,fontface=2,label=paste0(round(nrow(comp[comp$go<=cut.go.0.95,][comp[comp$go<=cut.go.0.95,"dws"]<=cut.dws.0.95,])/nrow(comp[comp$go<=cut.go.0.95,])*100,1),"%"))
+dev.off()
+
+rm(comp)