Orange: t-SNE - Revision history

Onnowpurbo: /* Output */

2020-04-15T01:58:02Z

Output

← Older revision		Revision as of 01:58, 15 April 2020
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	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	Widget t-SNE memplot data dengan metode t-distributed stochastic neighbor embedding. t-SNE ~~adalah~~ teknik reduksi dimensi, mirip dengan MDS, di mana titik dipetakan ke ruang 2-D oleh distribusi probabilitas mereka.		Widget t-SNE memplot data dengan metode t-distributed stochastic neighbor embedding. Widget t-SNE menggunakan teknik reduksi dimensi, mirip dengan widget MDS, di mana titik dipetakan ke ruang 2-D oleh distribusi probabilitas mereka.

	[[File:TSNE-stamped.png\|center\|600px\|thumb]]		[[File:TSNE-stamped.png\|center\|600px\|thumb]]

Onnowpurbo: /* Output */

2020-04-15T01:57:24Z

Output

← Older revision		Revision as of 01:57, 15 April 2020
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	Widget t-SNE memplot data dengan metode t-distributed stochastic neighbor embedding. t-SNE adalah teknik reduksi dimensi, mirip dengan MDS, di mana titik dipetakan ke ruang 2-D oleh distribusi probabilitas mereka.		Widget t-SNE memplot data dengan metode t-distributed stochastic neighbor embedding. t-SNE adalah teknik reduksi dimensi, mirip dengan MDS, di mana titik dipetakan ke ruang 2-D oleh distribusi probabilitas mereka.

	[[File:TSNE-stamped.png\|center\|~~200px~~\|thumb]]		[[File:TSNE-stamped.png\|center\|600px\|thumb]]

	* Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.		* Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.

Onnowpurbo at 01:47, 15 April 2020

2020-04-15T01:47:21Z

← Older revision		Revision as of 01:47, 15 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html

	Widget t-SNE melakukan projeksi ~~Two~~-dimensi menggunakan t-SNE.		Widget t-SNE melakukan projeksi 2-dimensi menggunakan t-SNE.

	==Input==		==Input==

Onnowpurbo at 01:44, 15 April 2020

2020-04-15T01:44:02Z

← Older revision		Revision as of 01:44, 15 April 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html

	~~Projeksi~~ Two-dimensi menggunakan t-SNE.		Widget t-SNE melakukan projeksi Two-dimensi menggunakan t-SNE.

	==Input==		==Input==
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	Kita menggunakan Widget Single Cell Datasets untuk me-load Bone marrow mononuclear cells dengan AML (sample) data. Kemudian kita mengirimkannya melalui Widget k-Means dan memilih 2 cluster dari Silhouette Scores. OK tampaknya akan ada dua cluster yang berbeda disini.		Kita menggunakan Widget Single Cell Datasets untuk me-load Bone marrow mononuclear cells dengan AML (sample) data. Kemudian kita mengirimkannya melalui Widget k-Means dan memilih 2 cluster dari Silhouette Scores. OK tampaknya akan ada dua cluster yang berbeda disini.

	~~Dapat~~ kita menemukan sub-population di cell ini? Coba kita load Bone marrow mononuclear cells dengan AML (markers) dengan Widget Single Cell Datasets. Sekarang kirimkan marker genes ke Data Table dan pilih, sebagai natural killer cells dari daftar (NKG7).		Pertanyaannya - dapat kita menemukan sub-population di cell ini? Coba kita load Bone marrow mononuclear cells dengan AML (markers) dengan Widget Single Cell Datasets. Sekarang kirimkan marker genes ke widget Data Table dan pilih, sebagai natural killer cells dari daftar (NKG7).

	Kirimkan marker dan hasil k-Means ke widget Score Cells dan pilih geneName untuk mencocokan marker dengan gene. Akhirnya, tambahkan t-SNE untuk mem-visualisasi hasilnya.		Kirimkan marker dan hasil k-Means ke widget Score Cells dan pilih geneName untuk mencocokan marker dengan gene. Akhirnya, tambahkan widget t-SNE untuk mem-visualisasi hasilnya.

	Di t-SNE, gunakan atribut Scores untuk memberikan warna (color) dari titik (point) dan set size (ukuran) titik tersebut. Kita akan melihat bahwa killer cell akan berkumpul menjadi sebuah cluster tersendiri dan ternyata t-SNE dapat menemukan subpopulasi-nya.		Di widget t-SNE, gunakan atribut Scores untuk memberikan warna (color) dari titik (point) dan set size (ukuran) titik tersebut. Kita akan melihat bahwa killer cell akan berkumpul menjadi sebuah cluster tersendiri dan ternyata widget t-SNE dapat menemukan subpopulasi-nya.

	[[File:TSNE-Example.png\|center\|600px\|thumb]]		[[File:TSNE-Example.png\|center\|600px\|thumb]]

Onnowpurbo: /* Contoh */

2020-04-13T06:27:44Z

Contoh

← Older revision		Revision as of 06:27, 13 April 2020
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	Di t-SNE, gunakan atribut Scores untuk memberikan warna (color) dari titik (point) dan set size (ukuran) titik tersebut. Kita akan melihat bahwa killer cell akan berkumpul menjadi sebuah cluster tersendiri dan ternyata t-SNE dapat menemukan subpopulasi-nya.		Di t-SNE, gunakan atribut Scores untuk memberikan warna (color) dari titik (point) dan set size (ukuran) titik tersebut. Kita akan melihat bahwa killer cell akan berkumpul menjadi sebuah cluster tersendiri dan ternyata t-SNE dapat menemukan subpopulasi-nya.

	[[File:TSNE-Example.png\|center\|~~200px~~\|thumb]]		[[File:TSNE-Example.png\|center\|600px\|thumb]]

	==Referensi==		==Referensi==

Onnowpurbo: /* Contoh */

2020-03-10T00:45:16Z

Contoh

← Older revision		Revision as of 00:45, 10 March 2020
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	Dapat kita menemukan sub-population di cell ini? Coba kita load Bone marrow mononuclear cells dengan AML (markers) dengan Widget Single Cell Datasets. Sekarang kirimkan marker genes ke Data Table dan pilih, sebagai natural killer cells dari daftar (NKG7).		Dapat kita menemukan sub-population di cell ini? Coba kita load Bone marrow mononuclear cells dengan AML (markers) dengan Widget Single Cell Datasets. Sekarang kirimkan marker genes ke Data Table dan pilih, sebagai natural killer cells dari daftar (NKG7).

	~~Pass the markers and~~ k-Means ~~results to~~ Score Cells ~~widget and select~~ geneName ~~to match markers with genes~~. ~~Finally~~, ~~add~~ t-SNE ~~to visualize the results~~.		Kirimkan marker dan hasil k-Means ke widget Score Cells dan pilih geneName untuk mencocokan marker dengan gene. Akhirnya, tambahkan t-SNE untuk mem-visualisasi hasilnya.

	In t-SNE, ~~use~~ Scores ~~attribute to~~ color ~~the points and~~ set ~~their~~ size. ~~We see that~~ killer ~~cells are nicely clustered together and that~~ t-SNE ~~indeed found subpopulations~~.		Di t-SNE, gunakan atribut Scores untuk memberikan warna (color) dari titik (point) dan set size (ukuran) titik tersebut. Kita akan melihat bahwa killer cell akan berkumpul menjadi sebuah cluster tersendiri dan ternyata t-SNE dapat menemukan subpopulasi-nya.

	[[File:TSNE-Example.png\|center\|200px\|thumb]]		[[File:TSNE-Example.png\|center\|200px\|thumb]]

Onnowpurbo: /* Contoh */

2020-03-10T00:34:59Z

Contoh

← Older revision		Revision as of 00:34, 10 March 2020
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	==Contoh==		==Contoh==

	~~We will use~~ Single Cell Datasets ~~widget to~~ load Bone marrow mononuclear cells ~~with~~ AML (sample) data. ~~Then we will pass it through~~ k-Means ~~and select~~ 2 ~~clusters from~~ Silhouette Scores. ~~Ok, it looks like there might be two distinct clusters here~~.		Kita menggunakan Widget Single Cell Datasets untuk me-load Bone marrow mononuclear cells dengan AML (sample) data. Kemudian kita mengirimkannya melalui Widget k-Means dan memilih 2 cluster dari Silhouette Scores. OK tampaknya akan ada dua cluster yang berbeda disini.

	~~But can we find subpopulations in these cells~~? ~~Let us~~ load Bone marrow mononuclear cells ~~with~~ AML (markers) ~~with~~ Single Cell Datasets. ~~Now, pass the~~ marker genes to Data Table ~~and select, for example~~, natural killer cells ~~from the list~~ (NKG7).		Dapat kita menemukan sub-population di cell ini? Coba kita load Bone marrow mononuclear cells dengan AML (markers) dengan Widget Single Cell Datasets. Sekarang kirimkan marker genes ke Data Table dan pilih, sebagai natural killer cells dari daftar (NKG7).

	Pass the markers and k-Means results to Score Cells widget and select geneName to match markers with genes. Finally, add t-SNE to visualize the results.		Pass the markers and k-Means results to Score Cells widget and select geneName to match markers with genes. Finally, add t-SNE to visualize the results.
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	[[File:TSNE-Example.png\|center\|200px\|thumb]]		[[File:TSNE-Example.png\|center\|200px\|thumb]]




	==Referensi==		==Referensi==

Onnowpurbo at 00:29, 10 March 2020

2020-03-10T00:29:59Z

← Older revision		Revision as of 00:29, 10 March 2020
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	Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html		Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/tsne.html

	Two-~~dimensional data projection with~~ t-SNE.		Projeksi Two-dimensi menggunakan t-SNE.

	==Input==		==Input==
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	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	~~The~~ t-SNE ~~widget plots the~~ data ~~with a~~ t-distributed stochastic neighbor embedding ~~method~~. t-SNE ~~is a dimensionality reduction technique~~, ~~similar to~~ MDS, ~~where points are mapped to~~ 2-D ~~space by their probability distribution~~.		Widget t-SNE memplot data dengan metode t-distributed stochastic neighbor embedding. t-SNE adalah teknik reduksi dimensi, mirip dengan MDS, di mana titik dipetakan ke ruang 2-D oleh distribusi probabilitas mereka.

	[[File:TSNE-stamped.png\|center\|200px\|thumb]]		[[File:TSNE-stamped.png\|center\|200px\|thumb]]

Onnowpurbo at 01:45, 29 January 2020

2020-01-29T01:45:46Z

Onnowpurbo at 02:17, 24 January 2020

2020-01-24T02:17:00Z

← Older revision		Revision as of 01:45, 29 January 2020
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	Two-dimensional data projection with t-SNE.		Two-dimensional data projection with t-SNE.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset
	Data Subset: subset of instances		Data Subset: subset of instances

	~~Outputs~~		==Output==

	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot
	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	The t-SNE widget plots the data with a t-distributed stochastic neighbor embedding method. t-SNE is a dimensionality reduction technique, similar to MDS, where points are mapped to 2-D space by their probability distribution.		The t-SNE widget plots the data with a t-distributed stochastic neighbor embedding method. t-SNE is a dimensionality reduction technique, similar to MDS, where points are mapped to 2-D space by their probability distribution.
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	[[File:TSNE-stamped.png\|center\|200px\|thumb]]		[[File:TSNE-stamped.png\|center\|200px\|thumb]]

	Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.		* Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.
	Select the number of PCA components used for projection.		* Select the number of PCA components used for projection.
	Set the color of the displayed points (you will get colors for discrete values and grey-scale points for continuous). Set shape, size and label to differentiate between points. Set symbol size and opacity for all data points. Set jittering to randomly disperse data points.		* Set the color of the displayed points (you will get colors for discrete values and grey-scale points for continuous). Set shape, size and label to differentiate between points. Set symbol size and opacity for all data points. Set jittering to randomly disperse data points.
	Adjust plot properties:		* Adjust plot properties:
	Show legend displays a legend on the right. Click and drag the legend to move it.		** Show legend displays a legend on the right. Click and drag the legend to move it.
	Show all data on mouse hover enables information bubbles if the cursor is placed on a dot.		** Show all data on mouse hover enables information bubbles if the cursor is placed on a dot.
	Show class density colors the graph by class.		** Show class density colors the graph by class.
	Label only selected points allows you to select individual data instances and label them.		** Label only selected points allows you to select individual data instances and label them.
	If Send selected automatically is ticked, changes are communicated automatically. Alternatively, press Send Selected.		* If Send selected automatically is ticked, changes are communicated automatically. Alternatively, press Send Selected.
	Select, zoom, pan and zoom to fit are the options for exploring the graph. The manual selection of data instances works as an angular/square selection tool. Double click to move the projection. Scroll in or out for zoom.		* Select, zoom, pan and zoom to fit are the options for exploring the graph. The manual selection of data instances works as an angular/square selection tool. Double click to move the projection. Scroll in or out for zoom.
	Access help, save image or produce a report.		* Access help, save image or produce a report.

	==Contoh==		==Contoh==

← Older revision		Revision as of 02:17, 24 January 2020
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	Data: input dataset		Data: input dataset

	Data Subset: subset of instances		Data Subset: subset of instances

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	Selected Data: instances selected from the plot		Selected Data: instances selected from the plot

	Data: data with an additional column showing whether a point is selected		Data: data with an additional column showing whether a point is selected

	The t-SNE widget plots the data with a t-distributed stochastic neighbor embedding method. t-SNE is a dimensionality reduction technique, similar to MDS, where points are mapped to 2-D space by their probability distribution.		The t-SNE widget plots the data with a t-distributed stochastic neighbor embedding method. t-SNE is a dimensionality reduction technique, similar to MDS, where points are mapped to 2-D space by their probability distribution.

	~~../../_images/tSNE~~-stamped.png		[[File:TSNE-stamped.png\|center\|200px\|thumb]]

	Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.		Number of iterations for optimization and the measure of perplexity. Press Start to (re-)run the optimization.

	Select the number of PCA components used for projection.		Select the number of PCA components used for projection.

	Set the color of the displayed points (you will get colors for discrete values and grey-scale points for continuous). Set shape, size and label to differentiate between points. Set symbol size and opacity for all data points. Set jittering to randomly disperse data points.		Set the color of the displayed points (you will get colors for discrete values and grey-scale points for continuous). Set shape, size and label to differentiate between points. Set symbol size and opacity for all data points. Set jittering to randomly disperse data points.

	Adjust plot properties:		Adjust plot properties:

	Show legend displays a legend on the right. Click and drag the legend to move it.		Show legend displays a legend on the right. Click and drag the legend to move it.

	Show all data on mouse hover enables information bubbles if the cursor is placed on a dot.		Show all data on mouse hover enables information bubbles if the cursor is placed on a dot.

	Show class density colors the graph by class.		Show class density colors the graph by class.

	Label only selected points allows you to select individual data instances and label them.		Label only selected points allows you to select individual data instances and label them.

	If Send selected automatically is ticked, changes are communicated automatically. Alternatively, press Send Selected.		If Send selected automatically is ticked, changes are communicated automatically. Alternatively, press Send Selected.

	Select, zoom, pan and zoom to fit are the options for exploring the graph. The manual selection of data instances works as an angular/square selection tool. Double click to move the projection. Scroll in or out for zoom.		Select, zoom, pan and zoom to fit are the options for exploring the graph. The manual selection of data instances works as an angular/square selection tool. Double click to move the projection. Scroll in or out for zoom.

	Access help, save image or produce a report.		Access help, save image or produce a report.

	~~Example~~		==Contoh==

	We will use Single Cell Datasets widget to load Bone marrow mononuclear cells with AML (sample) data. Then we will pass it through k-Means and select 2 clusters from Silhouette Scores. Ok, it looks like there might be two distinct clusters here.		We will use Single Cell Datasets widget to load Bone marrow mononuclear cells with AML (sample) data. Then we will pass it through k-Means and select 2 clusters from Silhouette Scores. Ok, it looks like there might be two distinct clusters here.
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	In t-SNE, use Scores attribute to color the points and set their size. We see that killer cells are nicely clustered together and that t-SNE indeed found subpopulations.		In t-SNE, use Scores attribute to color the points and set their size. We see that killer cells are nicely clustered together and that t-SNE indeed found subpopulations.

	~~../../_images/tSNE~~-Example.png		[[File:TSNE-Example.png\|center\|200px\|thumb]]