be4311c07e14feb728abc6425ee606ffaa611a58
markd
  Fri Jan 22 06:46:58 2021 -0800
merge with master

diff --git src/utils/clusterMatrixToBarChartBed/clusterMatrixToBarChartBed.c src/utils/clusterMatrixToBarChartBed/clusterMatrixToBarChartBed.c
new file mode 100644
index 0000000..c82d45a
--- /dev/null
+++ src/utils/clusterMatrixToBarChartBed/clusterMatrixToBarChartBed.c
@@ -0,0 +1,325 @@
+/* clusterMatrixToBarChartBed - Compute a barchart bed file from  a gene matrix 
+ * and a gene bed file and a way to cluster samples. */
+
+#include "common.h"
+#include "linefile.h"
+#include "hash.h"
+#include "options.h"
+#include "localmem.h"
+#include "obscure.h"
+#include "sqlNum.h"
+
+boolean clSimple = FALSE;
+boolean clMedian = FALSE;
+char *clName2 = NULL;
+
+void usage()
+/* Explain usage and exit. */
+{
+errAbort(
+  "clusterMatrixToBarChartBed - Compute a barchart bed file from  a gene matrix\n"
+  "and a gene bed file and a way to cluster samples.\n"
+  "NOTE: consider using matrixClusterColumns and matrixToBarChartBed instead\n"
+  "usage:\n"
+  "   clusterMatrixToBarChartBed sampleClusters.tsv geneMatrix.tsv geneset.bed output.bed\n"
+  "where:\n"
+  "   sampleClusters.tsv is a two column tab separated file with sampleId and clusterId\n"
+  "   geneMatrix.tsv has a row for each gene. The first row uses the same sampleId as above\n"
+  "   geneset.bed has the maps the genes in the matrix (from it's first column) to the genome\n"
+  "        geneset.bed needs 6 standard bed fields.  Unless name2 is set it also needs a name2\n"
+  "        field as the last field\n"
+  "   output.bed is the resulting bar chart, with one column per cluster\n"
+  "options:\n"
+  "   -simple - don't store the position of gene in geneMatrix.tsv file in output\n"
+  "   -median - use median (instead of mean)\n"
+  "   -name2=twoColFile.tsv - get name2 from file where first col is same ase geneset.bed's name\n"
+  );
+}
+
+/* Command line validation table. */
+static struct optionSpec options[] = {
+   {"simple", OPTION_BOOLEAN},
+   {"median", OPTION_BOOLEAN},
+   {"name2", OPTION_STRING},
+   {NULL, 0},
+};
+
+void hashSamplesAndClusters(char *tsvFile, 
+    struct hash **retSampleHash, struct hash **retClusterHash)
+/* Read two column tsv file into a hash keyed by first column */
+{
+struct hash *sampleHash = hashNew(0);
+struct hash *clusterHash = hashNew(0);
+char *row[2];
+struct lineFile *lf = lineFileOpen(tsvFile, TRUE);
+while (lineFileNextRowTab(lf, row, ArraySize(row)) )
+    {
+    /* Find cluster in cluster hash, if it doesn't exist make it. */
+    char *clusterName = row[1];
+    struct hashEl *hel = hashLookup(clusterHash, clusterName);
+    if (hel == NULL)
+	hel = hashAddInt(clusterHash, clusterName, 1);
+    else
+	hel->val = ((char *)hel->val)+1;    // Increment hash pointer as per hashIncInt
+    char *clusterStableName = hel->name;	// This is allocated in clusterHash
+    hashAdd(sampleHash, row[0], clusterStableName);
+    }
+lineFileClose(&lf);
+*retSampleHash = sampleHash;
+*retClusterHash = clusterHash;
+}
+
+void clusterMatrixToBarChartBed(char *sampleClusters, char *matrixTsv, char *geneBed, char *output)
+/* clusterMatrixToBarChartBed - Compute a barchart bed file from  a gene matrix 
+ * and a gene bed file and a way to cluster samples. */
+{
+/* Figure out if we need to do medians etc */
+boolean doMedian = clMedian;
+
+/* Load up the gene set */
+verbose(2, "clusterMatrixToBarChartBed(%s,%s,%s,%s)\n", sampleClusters, matrixTsv, geneBed, output);
+int bedRowSize = 0;
+struct hash *geneHash = hashTsvBy(geneBed, 3, &bedRowSize);
+verbose(2, "%d columns about %d genes in %s\n", bedRowSize, geneHash->elCount, geneBed);
+
+/* Deal with external gene hash */
+struct hash *nameToName2 = NULL;
+if (clName2 != NULL)
+    {
+    int colCount = 0;
+    nameToName2 = hashTsvBy(clName2, 0, &colCount);
+    if (colCount != 2)
+        errAbort("Expecting %s to be a two column tab separated file", clName2);
+    }
+
+/* Keep track of how many fields gene bed has to have and locate name2 */
+int geneBedMinSize = 7;
+int name2Ix = bedRowSize - 1;	    // Last field if it is in bed
+if (clName2 != NULL)
+    geneBedMinSize -= 1;
+if (bedRowSize < geneBedMinSize)
+    {
+    if (clName2 == NULL)
+	errAbort("%s needs to have at least 6 standard BED fields and a name2 field\n", geneBed);
+    else
+	errAbort("%s needs to have at least 6 standard BED fields\n", geneBed);
+    }
+
+
+/* Load up the sample clustering */
+struct hash *sampleHash = NULL, *clusterHash = NULL;
+hashSamplesAndClusters(sampleClusters, &sampleHash, &clusterHash);
+int clusterCount = clusterHash->elCount;
+verbose(1, "%d samples and %d clusters in %s\n", sampleHash->elCount, clusterCount,
+    sampleClusters);
+if (clusterCount <= 1 || clusterCount >= 10000)
+    errAbort("%d is not a good number of clusters", clusterCount);
+double clusterTotal[clusterCount];
+int clusterElements[clusterCount];
+
+/* Alphabetize cluster names  */
+char *clusterNames[clusterCount];
+struct hashEl *hel;
+struct hashCookie cookie = hashFirst(clusterHash);
+int clusterIx = 0;
+while ((hel = hashNext(&cookie)) != NULL)
+    {
+    clusterNames[clusterIx] = hel->name;
+    ++clusterIx;
+    }
+sortStrings(clusterNames, clusterCount);
+verbose(2, "%s to %s\n", clusterNames[0], clusterNames[clusterCount-1]);
+
+/* Figure out size of each alphabetized cluster in terms of number of samples in cluster 
+ * if we are doing median */
+int clusterSize[clusterCount];
+double *clusterSamples[clusterCount];
+if (doMedian)
+    {
+    for (clusterIx = 0; clusterIx < clusterCount; ++clusterIx)
+	{
+	clusterSize[clusterIx] = hashIntVal(clusterHash, clusterNames[clusterIx]);
+	verbose(2, "clusterSize[%d] = %d\n", clusterIx, clusterSize[clusterIx]);
+	}
+
+    /* Make up array of doubles for each cluster to hold all samples in that clusters */
+    for (clusterIx = 0; clusterIx < clusterCount; ++clusterIx)
+	{
+	double *samples;
+	AllocArray(samples, clusterSize[clusterIx]);
+	clusterSamples[clusterIx] = samples;
+	}
+    }
+
+/* Make hash that goes from cluster name to cluster index */
+struct hash *clusterToClusterIdHash = hashNew(0);
+for (clusterIx = 0; clusterIx<clusterCount; ++clusterIx)
+    {
+    hashAddInt(clusterToClusterIdHash, clusterNames[clusterIx], clusterIx);
+    }
+
+
+/* Open output */
+FILE *f = mustOpen(output, "w");
+
+/* Open up matrix file and read first line into sample labeling */
+struct lineFile *lf = lineFileOpen(matrixTsv, TRUE);
+char *line;
+lineFileNeedNext(lf, &line, NULL);
+if (line[0] == '#')	// Opening sharp on labels is optional, skip it if there
+    line = skipLeadingSpaces(line+1);
+int colCount = chopByChar(line, '\t', NULL, 0);
+int colAlloc = colCount + 1;
+char **sampleNames;
+AllocArray(sampleNames, colAlloc);
+chopByChar(line, '\t', sampleNames, colCount);
+
+/* Make array that maps row index to clusterID */
+int colToCluster[colCount];
+int colIx;
+for (colIx=1; colIx <colCount; colIx = colIx+1)
+    {
+    char *colName = sampleNames[colIx];
+    char *clusterName = hashFindVal(sampleHash, colName);
+    colToCluster[colIx] = -1;
+    if (clusterName != NULL)
+        {
+	int clusterId = hashIntValDefault(clusterToClusterIdHash, clusterName, -1);
+	colToCluster[colIx] = clusterId;
+	if (clusterId == -1)
+	    warn("%s is in expression matrix but not in sample cluster file", clusterName);
+	}
+    }
+
+
+/* Set up row for reading one row of matrix at a time. */
+char **matrixRow;
+AllocArray(matrixRow, colAlloc);
+int hitCount = 0, missCount = 0;
+double sumTotal = 0;
+dotForUserInit(100);
+for (;;)
+    {
+    /* Fetch next line and remember how long it is.  Just skip over lines that are empty or
+     * start with # character. */
+    int lineLength = 0;
+    char *line;
+    if (!lineFileNext(lf, &line, &lineLength))
+        break;
+    char *s = skipLeadingSpaces(line);
+    char c = s[0];
+    if (c == 0 || c == '#')
+        continue;
+
+    /* Chop it into tabs */
+    int rowSize = chopByChar(line, '\t', matrixRow, colAlloc);
+    lineFileExpectWords(lf, colCount, rowSize);
+
+    char *geneName = matrixRow[0];
+    struct hashEl *onePos = hashLookup(geneHash, geneName);
+    if (onePos == NULL)
+	{
+	verbose(2, "Can't find gene %s in %s", geneName, geneBed);
+	++missCount;
+	continue;
+	}
+    else
+	{
+	++hitCount;
+	}
+
+    /* A gene may map multiple places.  This loop takes care of that */
+    for (; onePos != NULL; onePos = hashLookupNext(onePos))
+        {
+	char **geneBedVal = onePos->val;	// Get our bed as string array out of hash
+
+	/* Zero out cluster histogram */
+	int i;
+	for (i=0; i<clusterCount; ++i)
+	    {
+	    clusterTotal[i] = 0.0;
+	    clusterElements[i] = 0;
+	    }
+
+	/* Loop through rest of row filling in histogram */
+	for (i=1; i<colCount; ++i)
+	    {
+	    int clusterIx = colToCluster[i];
+	    char *textVal = matrixRow[i];
+	    // special case so common we parse out "0" inline
+	    double val = (textVal[0] == '0' && textVal[1] == 0) ? 0.0 : sqlDouble(textVal);
+	    sumTotal += val;
+	    int valCount = clusterElements[clusterIx];
+	    clusterElements[clusterIx] = valCount+1;
+	    if (doMedian)
+		{
+		if (valCount >= clusterSize[clusterIx])
+		    internalErr();
+		clusterSamples[clusterIx][valCount] = val;
+		}
+	    else
+		clusterTotal[clusterIx] += val;
+	    }
+
+	/* Output info - first six from the bed, then name2, then our barchart */
+	for (i=0; i<6; ++i)
+	    fprintf(f, "%s\t",  geneBedVal[i]);
+
+	char *name = geneBedVal[3];	// By bed definition it's fourth field
+	char *name2 = NULL;
+	if (nameToName2 != NULL)
+	    {
+	    char **namedRow = hashFindVal(nameToName2, name);
+	    if (namedRow != NULL)
+		name2 = namedRow[1];	    // [0] is name 
+	    else
+	        warn("Can't find %s in %s", name, clName2);
+	    }
+	else
+	    name2 = geneBedVal[name2Ix];
+	if (name2 == NULL)
+	    name2 = name;
+	fprintf(f, "%s\t", name2);
+
+	fprintf(f, "%d\t", clusterCount);
+	for (i=0; i<clusterCount; ++i)
+	    {
+	    if (i != 0)
+	       fprintf(f, ",");
+	    if (doMedian)
+		fprintf(f, "%g", doubleMedian(clusterElements[i], clusterSamples[i]));
+	    else
+		{
+		fprintf(f, "%g",  clusterTotal[i]/clusterElements[i]);
+		}
+	    }
+	
+	/* Data file offset info */
+	if (!clSimple)
+	    fprintf(f, "\t%lld\t%lld",  (long long)lineFileTell(lf), (long long)lineLength);
+
+	fprintf(f, "\n");
+	}
+    dotForUser();
+    }
+verbose(1, "\n%d genes found, %d (%0.2f%%) missed\n", hitCount, missCount, 100.0*missCount/(hitCount+missCount));
+if (!doMedian)
+    {
+    verbose(1, "matrix total %g, %d clusters, %g ave/cluster\n", 
+	sumTotal, clusterCount, sumTotal/clusterCount);
+    }
+carefulClose(&f);
+}
+
+int main(int argc, char *argv[])
+/* Process command line. */
+{
+optionInit(&argc, argv, options);
+if (argc != 5)
+    usage();
+clSimple = optionExists("simple");
+clMedian = optionExists("median");
+clName2 = optionVal("name2", clName2);
+clusterMatrixToBarChartBed(argv[1], argv[2], argv[3], argv[4]);
+return 0;
+}