public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values", sel)
{
Values = values,
Help = "The value that should be present to discard/keep the corresponding row."
}
});
}
return
(new Parameters(new SingleChoiceWithSubParams("Row")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the filtering should be based on.",
ParamNameWidth = 50,
TotalWidth = 731
}, new SingleChoiceParam("Mode")
{
Values = new[] { "Remove matching columns", "Keep matching columns" },
Help =
"If 'Remove matching columns' is selected, rows having the values specified above will be removed while " +
"all other rows will be kept. If 'Keep matching columns' is selected, the opposite will happen."
}, PerseusPluginUtils.CreateFilterModeParam(false)));
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("NGS samples", sel)
{
Values = values,
Help = "The groups of NGS data that should be used as reference for normalization.",
}, new MultiChoiceParam("MS samples", sel)
{
Values = values,
Help = "The groups of MS data that should be normalized."
}
});
}
return
(new Parameters(new SingleChoiceWithSubParams("Group")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the analysis should be based on.",
ParamNameWidth = 70,
TotalWidth = 731
}));
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values", sel)
{
Values = values,
Help = "The value that should be present to discard/keep the corresponding row."
}
});
}
return
(new Parameters(new Parameter[] {
new SingleChoiceWithSubParams("Row")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the filtering should be based on.",
ParamNameWidth = 50,
TotalWidth = 731
},
new StringParam("New term")
}));
}
public void ProcessData(IMatrixData mdata, Parameters param, ref IMatrixData[] supplTables,
ref IDocumentData[] documents, ProcessInfo processInfo)
{
ParameterWithSubParams <int> p = param.GetParamWithSubParams <int>("Row");
int colInd = p.Value;
if (colInd < 0)
{
processInfo.ErrString = "No categorical rows available.";
return;
}
Parameter <int[]> mcp = p.GetSubParameters().GetParam <int[]>("Values");
int[] inds = mcp.Value;
if (inds.Length == 0)
{
processInfo.ErrString = "Please select at least one term for filtering.";
return;
}
string[] values = new string[inds.Length];
string[] v = mdata.GetCategoryRowValuesAt(colInd);
for (int i = 0; i < values.Length; i++)
{
values[i] = v[inds[i]];
}
HashSet <string> value = new HashSet <string>(values);
bool remove = param.GetParam <int>("Mode").Value == 0;
string[][] cats = mdata.GetCategoryRowAt(colInd);
List <int> valids = new List <int>();
List <int> notvalids = new List <int>();
for (int i = 0; i < cats.Length; i++)
{
bool valid = true;
foreach (string w in cats[i])
{
if (value.Contains(w))
{
valid = false;
break;
}
}
if (valid && remove || !valid && !remove)
{
valids.Add(i);
}
else if (!valid)
{
notvalids.Add(i);
}
}
if (param.GetParam <int>("Filter mode").Value == 2)
{
supplTables = new[] { PerseusPluginUtils.CreateSupplTabSplitColumns(mdata, notvalids.ToArray()) };
}
PerseusPluginUtils.FilterColumnsNew(mdata, param, valids.ToArray());
}
public void ProcessData(IMatrixData mdata, Parameters param, ref IMatrixData[] supplTables,
ref IDocumentData[] documents, ProcessInfo processInfo)
{
ParameterWithSubParams <int> p = param.GetParamWithSubParams <int>("Row");
int colInd = p.Value;
if (colInd < 0)
{
processInfo.ErrString = "No categorical rows available.";
return;
}
Parameter <int[]> mcp = p.GetSubParameters().GetParam <int[]>("Values");
int[] inds = mcp.Value;
if (inds.Length < 1)
{
processInfo.ErrString = "Please select at least two terms for merging.";
return;
}
string newTerm = param.GetParam <string>("New term").Value;
if (newTerm.Length == 0)
{
processInfo.ErrString = "Please specify a new term.";
return;
}
string[] values = new string[inds.Length];
for (int i = 0; i < values.Length; i++)
{
values[i] = mdata.GetCategoryRowValuesAt(colInd)[inds[i]];
}
HashSet <string> value = new HashSet <string>(values);
string[][] cats = mdata.GetCategoryRowAt(colInd);
string[][] newCat = new string[cats.Length][];
for (int i = 0; i < cats.Length; i++)
{
string[] w = cats[i];
bool changed = false;
for (int j = 0; j < w.Length; j++)
{
if (value.Contains(w[j]))
{
w[j] = newTerm;
changed = true;
}
}
if (changed)
{
Array.Sort(w);
}
newCat[i] = w;
}
mdata.SetCategoryRowAt(newCat, colInd);
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values1", sel)
{
Values = values,
Help = "The groups that should be present to compute differential expression analysis.",
}, new MultiChoiceParam("Values2", sel)
{
Values = values,
Help = "The groups that should be present to compute differential expression analysis."
}
});
}
return
(new Parameters(new SingleChoiceWithSubParams("Group")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the analysis should be based on.",
ParamNameWidth = 70,
TotalWidth = 731
}, new BoolWithSubParams("Log2 Fold Change")
{
Help = "The Log2 Fold Change threshold of the significant features.",
Value = true,
SubParamsTrue = new Parameters(new DoubleParam("Up-regluation", 1.5),
new DoubleParam("Down-regluation", -1.5)),
ParamNameWidth = 90,
TotalWidth = 731
}, new BoolWithSubParams("FDR")
{
Help = "The FDR threshold of the significant features.",
Value = true,
SubParamsTrue = new Parameters(new DoubleParam("Max. FDR", 0.05)),
ParamNameWidth = 90,
TotalWidth = 731
}, new BoolWithSubParams("P-value")
{
Help = "The p-value threshold of the significant features.",
Value = false,
SubParamsTrue = new Parameters(new DoubleParam("Max. p-value", 0.05)),
ParamNameWidth = 90,
TotalWidth = 731
}
));
}
public string[] ExtractGroup(IMatrixData mdata, ParameterWithSubParams <int> p, ProcessInfo processInfo,
string value, int colInd)
{
string[] errors = new string[0];
Parameter <int[]> mcp = p.GetSubParameters().GetParam <int[]>(value);
int[] inds = mcp.Value;
if (inds.Length == 0)
{
return(errors);
}
string[] values = new string[inds.Length];
string[] groupids = new string[inds.Length];
string[] v = mdata.GetCategoryRowValuesAt(colInd);
for (int i = 0; i < values.Length; i++)
{
groupids[i] = v[inds[i]];
}
return(groupids);
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values", sel)
{
Values = values,
Help = "The group that should be performed the filtering of low expression values."
}
});
}
return
(new Parameters(
new StringParam("Min. expression value")
{
Help = "The minimum expression value for filtering the rows of the table. " +
"At least one entry of a row needs to be higher than this given value.",
Value = "20",
}, new SingleChoiceParam("Mode")
{
Values = new[] { "At least one group", "All groups" },
Help =
"If 'At least one group' is selected, the row will be kept if at least one selected group can fit the 'Min. expression value.'" +
"If 'All groups' is selected, the row will be kept if all selected groups can fit the 'Min. expression value.'.",
Value = 0
}, new SingleChoiceWithSubParams("Group")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the analysis should be based on.",
ParamNameWidth = 50,
TotalWidth = 731
}
));
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values1", sel)
{
Values = values,
Help = "The groups that should be present to compute differential expression analysis.",
}, new MultiChoiceParam("Values2", sel)
{
Values = values,
Help = "The groups that should be present to compute differential expression analysis."
}
});
}
return
(new Parameters(new SingleChoiceWithSubParams("Program")
{
Values = new[] { "DESeq2", "EdgeR" },
SubParams = new[] {
new Parameters(new Parameter[] { new SingleChoiceParam("FitType")
{
Values = new[] { "parametric", "local", "mean" },
Help = "This fitType for running DESeq2. If your dataset without replicates, please use local or mean.",
} }),
new Parameters(new Parameter[] { new DoubleParam("Dispersion (without replicates)", 0.04)
{
Help = "This dispersion value of EdgeR for the dataset without replicates."
},
new SingleChoiceParam("Test method")
{
Values = new[] { "Generalized linear model", "Exact Test" },
Help = "This test method for running EdgeR."
} })
},
Help = "The program for doing differential expression analysis.",
ParamNameWidth = 170,
TotalWidth = 731
}, new SingleChoiceWithSubParams("Group")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the analysis should be based on.",
ParamNameWidth = 70,
TotalWidth = 731
}, new SingleChoiceWithSubParams("Log2 Fold Change")
{
Help = "The Log2 Fold Change threshold of the significant features.",
Values = new[] { "Number", "Percentile", "None" },
SubParams = new[] {
new Parameters(new DoubleParam("Up-regluation", 2)
{
Help = "The minimum value of logFC for up-regulation. " +
"If the expressed values are higher than this value, " +
"it would be considered as up-regulated genes/proteins."
},
new DoubleParam("Down-regluation", -2)
{
Help = "The maximum value of logFC for down-regulation. " +
"If the expressed values are lower than this value, " +
"it would be considered as down-regulated genes/proteins."
}),
new Parameters(new DoubleParam("Up-regluation", 95)
{
Help = "The cutoff of logFC for up-regulation. " +
"If the expressed values are located at higher percentile of the assigned value, " +
"it would be considered as up-regulated genes/proteins."
},
new DoubleParam("Down-regluation", 5)
{
Help = "The cutoff of logFC for down-regulation. " +
"If the expressed values are located at lower percentile of the assigned value, " +
"it would be considered as down-regulated genes/proteins."
}),
new Parameters(),
},
ParamNameWidth = 90,
TotalWidth = 731
}, new BoolWithSubParams("Adjusted p-value (FDR)")
{
Help = "The Adjusted p-value (FDR) threshold of the significant features.",
Value = true,
SubParamsTrue = new Parameters(new DoubleParam("Max. Adjusted p-value (FDR)", 0.05)),
ParamNameWidth = 90,
TotalWidth = 731
}, new BoolWithSubParams("P-value")
{
Help = "The p-value threshold of the significant features.",
Value = false,
SubParamsTrue = new Parameters(new DoubleParam("Max. p-value", 0.05)),
ParamNameWidth = 90,
TotalWidth = 731
}
));
}
public void ProcessData(IMatrixData mdata, Parameters param, ref IMatrixData[] supplTables,
ref IDocumentData[] documents, ProcessInfo processInfo)
{
SingleChoiceWithSubParams p = param.GetSingleChoiceWithSubParams("Row");
int colInd = p.Value;
if (colInd < 0) {
processInfo.ErrString = "No categorical rows available.";
return;
}
MultiChoiceParam mcp = p.GetSubParameters().GetMultiChoiceParam("Values");
int[] inds = mcp.Value;
if (inds.Length < 1) {
processInfo.ErrString = "Please select at least two terms for merging.";
return;
}
string newTerm = param.GetStringParam("New term").Value;
if (newTerm.Length == 0){
processInfo.ErrString = "Please specify a new term.";
return;
}
string[] values = new string[inds.Length];
for (int i = 0; i < values.Length; i++) {
values[i] = mdata.GetCategoryRowValuesAt(colInd)[inds[i]];
}
HashSet<string> value = new HashSet<string>(values);
string[][] cats = mdata.GetCategoryRowAt(colInd);
string[][] newCat = new string[cats.Length][];
for (int i = 0; i < cats.Length; i++){
string[] w = cats[i];
bool changed = false;
for (int j = 0; j < w.Length; j++){
if (value.Contains(w[j])){
w[j] = newTerm;
changed = true;
}
}
if (changed){
Array.Sort(w);
}
newCat[i] = w;
}
mdata.SetCategoryRowAt(newCat, colInd);
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++) {
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[]{
new MultiChoiceParam("Values", sel)
{Values = values, Help = "The value that should be present to discard/keep the corresponding row."}
});
}
return
new Parameters(new Parameter[]{
new SingleChoiceWithSubParams("Row"){
Values = mdata.CategoryRowNames, SubParams = subParams,
Help = "The categorical row that the filtering should be based on.", ParamNameWidth = 50, TotalWidth = 731
},
new StringParam("New term")
});
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++)
{
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[] { 0 } : new int[0];
subParams[i] =
new Parameters(new Parameter[] {
new MultiChoiceParam("Values", sel)
{
Values = values,
Help = "The group that should be performed the filtering of low expression values."
}
});
}
return
(new Parameters(
new SingleChoiceWithSubParams("Min. expression value")
{
Help = "The minimum expression value for filtering the rows of the table. ",
Values = new[] { "Raw counts", "cpm" },
SubParams = new[] {
new Parameters(new IntParam("Min. raw counts", Math.Min(mdata.RowCount, 20))
{
Help =
"The minimum value of raw count for filtering the low expressed genes/proteins.",
}),
new Parameters(new IntParam("Min. cpm", 1)
{
Help =
"The minimum value of cpm for filtering the low expressed genes/proteins."
}),
},
ParamNameWidth = 50,
TotalWidth = 731
}, new SingleChoiceParam("Mode")
{
Values = new[] { "At least one group", "All groups", "Total" },
Help =
"If 'At least one group' is selected, the row will be kept if at least one selected group can fit the " +
"'Min. expression value' with the number of 'Min.valid samples in a group'." +
"If 'All groups' is selected, the row will be kept if all selected groups can fit the " +
"'Min. expression value' with the number of 'Min.valid samples in a group'." +
"If 'Total' is selected, the row will be kept if the samples (without considering group) can fit the " +
"'Min. expression value' with the number of 'Min.valid samples in a group'.",
Value = 0
},
new SingleChoiceWithSubParams("Min. valid samples in a group")
{
Help = "The minimum number of values in one row are higher than minimum expression value. ",
Values = new[] { "Number", "Percentage" },
SubParams = new[] {
new Parameters(new IntParam("Min. number of samples", Math.Min(mdata.RowCount, 1))
{
Help =
"If a row has less than the specified number of valid values it will be discarded in the output.",
}),
new Parameters(new IntParam("Min. percentage of samples", 25)
{
Help =
"If a row has less than the specified percentage of valid values it will be discarded in the output."
}),
},
ParamNameWidth = 50,
TotalWidth = 731
},
new SingleChoiceWithSubParams("Group")
{
Values = mdata.CategoryRowNames,
SubParams = subParams,
Help = "The categorical row that the analysis should be based on.",
ParamNameWidth = 50,
TotalWidth = 731
}
));
}
public void ProcessData(IMatrixData mdata, Parameters param, ref IMatrixData[] supplTables,
ref IDocumentData[] documents, ProcessInfo processInfo)
{
SingleChoiceWithSubParams p = param.GetSingleChoiceWithSubParams("Row");
int colInd = p.Value;
if (colInd < 0){
processInfo.ErrString = "No categorical rows available.";
return;
}
MultiChoiceParam mcp = p.GetSubParameters().GetMultiChoiceParam("Values");
int[] inds = mcp.Value;
if (inds.Length == 0){
processInfo.ErrString = "Please select at least one term for filtering.";
return;
}
string[] values = new string[inds.Length];
for (int i = 0; i < values.Length; i++){
values[i] = mdata.GetCategoryRowValuesAt(colInd)[inds[i]];
}
HashSet<string> value = new HashSet<string>(values);
bool remove = param.GetSingleChoiceParam("Mode").Value == 0;
string[][] cats = mdata.GetCategoryRowAt(colInd);
List<int> valids = new List<int>();
for (int i = 0; i < cats.Length; i++){
bool valid = true;
foreach (string w in cats[i]){
if (value.Contains(w)){
valid = false;
break;
}
}
if ((valid && remove) || (!valid && !remove)){
valids.Add(i);
}
}
PerseusPluginUtils.FilterColumns(mdata, param, valids.ToArray());
}
public Parameters GetParameters(IMatrixData mdata, ref string errorString)
{
Parameters[] subParams = new Parameters[mdata.CategoryRowCount];
for (int i = 0; i < mdata.CategoryRowCount; i++) {
string[] values = mdata.GetCategoryRowValuesAt(i);
int[] sel = values.Length == 1 ? new[]{0} : new int[0];
subParams[i] =
new Parameters(new Parameter[]{
new MultiChoiceParam("Values", sel)
{Values = values, Help = "The value that should be present to discard/keep the corresponding row."}
});
}
return
new Parameters(new Parameter[]{
new SingleChoiceWithSubParams("Row"){
Values = mdata.CategoryRowNames, SubParams = subParams,
Help = "The categorical row that the filtering should be based on.", ParamNameWidth = 50, TotalWidth = 731
},
new SingleChoiceParam("Mode"){
Values = new[]{"Remove matching columns", "Keep matching columns"},
Help =
"If 'Remove matching columns' is selected, rows having the values specified above will be removed while " +
"all other rows will be kept. If 'Keep matching columns' is selected, the opposite will happen."
},
PerseusPluginUtils.GetFilterModeParam(false)
});
}